essay about covid 19 by using expanded definition

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• Published: 28 January 2021

The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk

• Benjamin J. Singer 1 ,
• Robin N. Thompson 2 , 3 &
• Michael B. Bonsall 1  

Scientific Reports volume  11 , Article number:  2547 ( 2021 ) Cite this article

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• Applied mathematics
• Epidemiology

In the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters—such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions—on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

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Introduction.

In the early stages of an infectious disease outbreak, it is important to determine whether the pathogen responsible may go on to cause an epidemic or a pandemic 1 , 2 , 3 , 4 , 5 . There is extensive literature on determining the probability of a major epidemic given a small population of initial infected hosts 6 , 7 , 8 , 9 . This research has produced a natural mathematical definition of an epidemic, based on the bimodal distribution of outbreak sizes given by simple stochastic epidemiological models when \(R_0\) is larger than but not close to one 10 . The term ‘pandemic’ has no corresponding theoretical definition, and there is no consensus mathematical approach to determining the probability of a pandemic. In this study, we examine how alternative definitions of ‘pandemic’ affect quantitative estimates of pandemic risk assessed early in an infectious disease outbreak.

The term ‘pandemic’ is used extensively, appearing in phrases such as ‘pandemic preparedness’ 11 , 12 , 13 , ‘pandemic influenza’ 14 , 15 , 16 , and ‘pandemic potential’ 17 , 18 , 19 . A Google Scholar search returns 25,800 results using the term ‘pandemic’ for 2019 alone.

The International Epidemiology Association’s Dictionary of Epidemiology defines a pandemic as “an epidemic occurring worldwide, or over a very wide area, crossing international boundaries and usually affecting a large number of people” 20 . Notably this definition makes an explicit reference to national borders. Contrastingly, a World Health Organization (WHO) source makes reference to a pandemic as “the worldwide spread of a new disease” 21 .The use of the word ‘new’ here is ambiguous in the context of infectious diseases. HIV/AIDS is often referred to as a global pandemic, but is certainly not new on the timescale of, say, the emergence of influenza strains 22 , 23 . A study by Morens et al. in 2009 finds that there is little in common between all disease outbreaks that have been referred to as pandemics, except that they have a wide geographical extension 24 .

These kinds of differences between pandemic definitions can often go unnoticed, but in certain circumstances they can cause confusion between different stakeholders (e.g. between scientists and governments, or governments and the public), who may not have a shared background understanding of the term. In 2009, the WHO declared a pandemic of H1N1 influenza, using criteria related to the incidence and spread of the virus in different WHO regions 25 . The criteria did not include reference to morbidity or mortality 26 . This fact led to some controversy over whether the declaration of a pandemic was appropriate, as the declaration prompted some governments to mount an intensive response to an outbreak that resulted in fewer yearly deaths than a typical strain of seasonal flu 27 , 28 , 29 , 30 .

International health organisations such as the WHO have not provided any formal definitions of the term ‘pandemic’, and the WHO no longer uses it as an official status of any outbreak 25 , 31 . It would, however, be hasty to dismiss the importance of the term on these grounds. Although the WHO no longer uses the term ‘pandemic’ officially, the WHO Director-General drew attention to their use of the term as recently as March 2020, to describe the status of the COVID-19 outbreak 32 . The Director-General cited “alarming levels of inaction” as one of the reasons to use the term, along with the caveat that “describing the situation as a pandemic does not change WHO’s assessment of the threat posed by this virus”. The WHO’s use of the term was of interest to the public, receiving extensive press coverage 33 , 34 , 35 . The term ‘pandemic’ clearly continues to be important to indicate serious risk during disease outbreaks.

Regardless of the extent to which the pandemic definitions currently in use do or do not agree, they are all qualitative in nature, using descriptions such as “very wide area” and “large number of people”. Perhaps as a result of this, many quantitative studies on pandemics do not make use of a quantitative definition of a pandemic, but instead focus on causally related concepts, such as sustained transmission 19 , or emergence of novel viruses 36 . Others treat the spread of a pathogen at a pandemic level as a context in which to study transmission dynamics, without paying special attention to how those dynamics might lead to a pandemic as distinct from an epidemic or a more limited outbreak 37 , 38 , 39 . In this paper, we examine the effects of alternative pandemic definitions on the analysis of key epidemiological questions. The results provide a foundation for deciding the appropriate quantitative definition of ‘pandemic’ in a given context.

We use a metapopulation model to investigate the effects of pandemic definition on the results of a quantitative assessment of the probability of a pandemic. Metapopulation models are commonly applied to pathogens that spread between regions of the world, and so are appropriate for modelling pandemics 40 , 41 , 42 , 43 , 44 , 45 . We represent states of our metapopulation model as states of a Markov chain, allowing us to calculate the probability of a pandemic directly, as opposed to simulating many stochastic outbreaks and recording the proportion which result in pandemics. We explore two different kinds of pandemic definition, following Morens et al. 2009 24 , specifically:

the family of transregional definitions, where a pandemic is defined as an outbreak in which the number of regions experiencing epidemics meets or exceeds some threshold number n . We refer to specific transregional definitions as n -region transregional definitions, e.g. a 3-region transregional definition.

the interregional definition, where a pandemic is defined as an outbreak in which two or more non-adjacent regions experience epidemics.

Note that these definitions require a specific sense of ‘region’. These regions could be countries, or they could be larger or smaller than individual countries—from counties to health zones to WHO regions. Our metapopulation model (detailed in the Methods section below) can be used to model regions of any size. We have chosen not to include definitions with criteria relating to the number of people infected or killed, instead of, or in addition to, geographical extension. Extension is the only universal factor in pandemic definitions, and so is the focus of the current study 24 .

Three questions that help form public health policy at the beginning of an outbreak are:

Would interventions restricting travel reduce the risk of a pandemic?

Does a portion of the population have pre-existing immunity, and does this affect the risk of a pandemic?

How is the risk of a pandemic affected by regional differences in transmission?

Using our metapopulation model, we explore how changing the pandemic definition does or does not affect our answers to these questions. We show that the precise definition of a pandemic used in modelling studies can (but does not always) affect the inferred risk. The predicted effects of travel restrictions, the influence of pre-existing immunity, and the impact of regional differences in transmission can all vary when alternative definitions of ‘pandemic’ are used. This demonstrates clearly the need to consider carefully the pandemic definition used to assess the risk from an invading pathogen. This is necessary for clear communication of public health risk.

Travel rates

One important question about pandemic risk is what effect inter-regional travel rates have on the probability of a pandemic occurring 16 , 17 , 46 , 47 . Here we model epidemics occurring in regions connected on a network in which the connections and their weighting can be set at fixed values representing the rates of travel between regions. We consider simple networks that can illustrate the effects of our different pandemic definitions—namely, the star network, in which one central region is connected to all others with equal weighting and the non-central regions lack any other connections, and the fully connected network, in which each region is connected to every other with equal weighting. Figure  1 illustrates that the connectivity of the full network is much higher than that of the star network. Using the star network allows us to make the distinction between adjacent and non-adjacent regions, thus allowing us to distinguish between transregional and interregional pandemic definitions.

Illustrations of ( a ) a star network and ( b ) a full network, each with ten regions. Circles represent regions, and straight lines represent travel routes between regions.

Unless otherwise stated, all figures in the current study are generated with a transmission rate of \(\beta = 0.28\) per day, a recovery rate of \(\mu = 0.14\) per day, and an inter-regional travel rate of \(2\times 10^{-4}\) per day. This corresponds to a within-region basic reproduction number ( \(R_0\) ) of 2. These values are within the plausible range for both seasonal and pandemic influenza, and as such they can be used to simulate a plausible pathogen of pandemic potential 38 . We further assume an initial population of 1000 susceptible individuals in each region, and that the outbreak is seeded by a single infectious individual in one region. In the full network, all regions are equivalent, so we seed the outbreak in a single arbitrary region. In the star network, we take the average probability of a pandemic over outbreaks seeded in each region.

Using a model with ten regions allows us to test a range of different transregional definitions of a pandemic. The pandemic probability under a range of n -region transregional definitions for a 10-region network with a variety of travel rates is shown in Fig.  2 . An n -region transregional definition effectively provides a threshold number n —if more than n regions experience epidemics, the outbreak is counted as a pandemic, and otherwise it is not. Thus we indicate the different possible n -region definitions through their threshold numbers in Figs.  2 , 5 , and  6 .

Pandemic probability for a range of between-region travel rates and a range of transregional pandemic definitions. The “pandemic threshold number” refers to the minimum number of regions that must experience epidemics before a pandemic is declared. The pandemic probability is, in general, sensitive to the pandemic definition used, but the degree of sensitivity depends on network structure and travel rates. ( a ) Pandemic probability for a star network. The pandemic probability is, in general, highly sensitive to the pandemic definition used. ( b ) Pandemic probability for a fully connected network. The sensitivity of the pandemic probability to the pandemic definition used is significantly reduced at high travel rates.

The 1-region transregional definition merges the definitions of ‘pandemic’ and ‘epidemic’ in an implausible way, but it is included in these figures for comparison. The comparison between the pandemic probability according to the 2-region definition and according to the 10-region definition shows the difference between pandemic definitions that are satisfied by any transregional transmission and definitions that are satisfied only by truly global spread. For the star network, or for the fully connected network with low travel rates, there is a marked difference between the probability of either of these definitions being satisfied. However, for the fully connected network at medium or high rates of travel, if the pathogen invades the initial region successfully, then it will go on to spread globally. As such, the probability of a pandemic is nears the maximum of 0.5 (i.e. \(1-1/R_0\) ) at all thresholds. For any definition, the probability of a pandemic increases with the connectivity of the network, and with travel rates across the network.

We can also explore the difference in pandemic probability between the transregional and interregional definitions, which make use of a distinction between adjacent and non-adjacent regions. This is shown for a 10-region star network in Fig.  3 a, in which we consider the 2-region transregional and 2-region interregional definitions. We choose a star network as it is one of the simplest network types in which there are adjacent and non-adjacent regions. There is a difference between the 2-region interregional and transregional definitions, but the difference is much smaller than that between the 2-region interregional and 10-region (global) definition, and reduces as travel rates increase. In the case of a fully connected network, all regions are essentially adjacent to each other, so we compare only the 2-region transregional and global definitions. We find that the definitions are clearly distinct for low travel rates, but as the travel rate increases the difference between the likelihood of a pathogen causing an epidemic in one region and the likelihood of it causing epidemics in all regions disappears. This is due to the fact that the pathogen can be introduced into any population from any other.

Plots of pandemic probability against between-region travel rate for a range of pandemic definitions. The difference in probability for different pandemic definitions changes as travel rates increase. ( a ) Plot of pandemic probability for a star network. ( b ) Plot of pandemic probability for a full network. For a fully connected network all regions are adjacent, so no line is shown for the interregional definition, which requires non-adjacent regions to experience epidemics.

In this section we have shown that, when a pandemic is defined in terms of which regions experience epidemics of a disease, different definitions can produce very different estimates of the pandemic probability at low connectivity or travel rates, but have a much smaller effect at high connectivity and travel rates. In the supplementary information, we illustrate that effects due to network structure are mostly due to the difference in motility between the full network and the star network, although topology still plays an important role.

Cross-immunity

Some pathogens with pandemic potential have a prior history of infecting humans, such as pandemic influenza. Newly emerged pathogens with no history of infecting humans are less likely than these established pathogens to encounter regions where susceptible individuals have partial immunity to infection. Established pathogens may encounter individuals with partial immunity acquired from infections with previously circulating strains—i.e. cross-immunity 48 , 49 . It can be important in responding to an outbreak to consider whether any individuals might have existing immunity. We can therefore investigate the interaction between immunity generated by prior exposure and pandemic definition by examining how cross-immunity affects our calculation of the pandemic probability on a network.

We modelled the spread of a pathogen over a ten-region network with no cross-immunity initially, where the initial infected individual could originate in any region. We only included cases where at least one region experienced an epidemic of this initial pathogen. To simulate the emergence of a strain with higher pandemic potential, we then introduced a second pathogen with a higher transmission rate of \(\beta = 0.42\) (corresponding to a basic reproduction number of 3), to which infection with the initial pathogen conferred some degree of partial immunity to infection. The strength of this immunity is written as \(\alpha\) . See the Methods section for details of how cross-immunity is incorporated into our modelling framework. We defined a pandemic as occurring when all ten regions experienced epidemics of the second pathogen, and repeated the model for two values of the level of cross-immunity at a variety of between-region travel rates. The results are presented in Fig.  4 .

Plots of pandemic probability against travel rate for high and low levels of cross-immunity ( \(\alpha\) ) on ten-region networks. A pandemic is defined here as all ten regions experiencing epidemics, i.e. the 10-region transregional definition. The plots show a large relative difference both in the probability of pandemics and in how that probability scales with travel rates for different levels of cross-immunity. The initial infected individual for each outbreak originates in a randomly chosen region. ( a ) Plot of pandemic probability for a star network. ( b ) Plot of pandemic probability for a full network.

First, increasing cross-immunity decreases the probability of a pandemic. Second, the presence of cross-immunity changes how pandemic probability scales with travel rates. In general, the pandemic probability increases faster with travel when the level of cross-immunity is low, except when it reaches a point of saturation as in Fig.  4 b.

Figure  5 shows the simultaneous effects of different n -region transregional pandemic definitions and the degree of cross-immunity in determining the pandemic probability. Here we fix the travel rate at \(2.0 \times 10^{-4}\) per day. In the full network there is a distinct transition from higher risk to lower risk, as cross-immunity approaches one. However, in the star network there is, on average, less circulation of the initial pathogen, so the effect of cross-immunity is less dramatic. Increased cross-immunity can also increase the difference in risk for different pandemic definitions—for the fully connected network, when cross-immunity exceeds \(\alpha = 0.5\) , differences in probability between different thresholds become visible that are much smaller at lower values. This suggests that the probability that an outbreak will develop into a pandemic may be more sensitive to the exact pandemic definition for outbreaks of pathogens that encounter pre-existing immunity than for pathogens which encounter only fully susceptible populations. However, this effect is not seen for the star network, in which the low connectivity of the network results in larger differences in probability between different thresholds even at low levels of cross-immunity.

Pandemic probability for various levels of cross-immunity ( \(\alpha\) ) and a range of transregional pandemic definitions, on a ten-region network. ( a ) Pandemic probability for a star network. ( b ) Pandemic probability for a fully connected network. Here the sensitivity of the pandemic probability to the pandemic definition used increases with cross-immunity, until the probability of any epidemic becomes very low.

Heterogeneous transmission

A topic of great concern during a pandemic is heterogeneity in risk between different countries or regions 50 , 51 . Cross-immunity can create one kind of heterogeneity, since it is common for previous exposure to a pathogen to differ between regions 52 . Another kind of heterogeneity is that due to different public health interventions. Here we ignore cross-immunity and instead examine a heterogeneous fully connected network of ten regions, five of which have a higher rate of transmission of the pathogen than the other five. This can be thought of as an approximation to the difference between poor regions with a relative lack of public health interventions, and wealthy regions with well-funded public health organisations and increased access to healthcare.

The level of heterogeneity was defined as the ratio of the transmission rate in the higher-transmission regions to the transmission rate in the lower-transmission regions. The average transmission rate across all regions was kept fixed at \({\bar{\beta }} = 0.28\) per day, corresponding to a basic reproduction number of 2. The simultaneous effects of heterogeneity and the pandemic definition in determining the pandemic probability are illustrated in Fig.  6 .

Pandemic probability for various degrees of heterogeneity of transmission rates and a range of transregional pandemic definitions, on a fully connected ten-region network where five regions are classed as higher-transmission and the other five regions are classed as lower-transmission. Note that the colour scales differ between the two plots, in order to make the variation in plot ( a ) clearer. ( a ) Pandemic probability for a pathogen emerging in a higher-transmission region. For low thresholds heterogeneity increases the pandemic probability, but at the 10-region threshold the pandemic probability grows and then decreases with increasing heterogeneity. ( b ) Pandemic probability for a pathogen emerging in a lower-transmission region. At all thresholds increasing heterogeneity decreases the pandemic probability.

The row for the 1-region definition shows how the risk of any outbreak varies with the changing basic reproduction number of the pathogen in the region in which it emerges. More complex effects can be seen for higher n -region definitions, especially the 10-region definition, where, at high levels of heterogeneity, even pathogens emerging in higher-transmission regions are prevented from spreading globally due to the low chance of epidemics in lower-transmission regions. Thus the probability of a pandemic under a 10-region definition increases and then decreases with increasing heterogeneity. In the supplementary information, we show that this increasing-decreasing effect exists in networks of different sizes and structures. It appears at different thresholds in different networks. No corresponding effect exists for a pathogen emerging in a lower-transmission region, where increasing heterogeneity always decreases the chance of a pandemic, however it is defined.

In this study, we have developed a theoretical framework to estimate the probability of a pandemic, as detailed in the Methods section below. We use a Markov chain technique based on SIR dynamics to model the spread of a pathogen. The results of this modelling framework reveal in which situations the definition of ‘pandemic’ has a strong effect on the calculated pandemic risk and in which situations it does not. The models also illustrate the effects of differing epidemiological parameters on the pandemic risk under different definitions, and how these effects interact with each other.

Returning to the three epidemiological questions introduced in the introduction, we can see that our results show how the answers can depend on our definition of a pandemic, and on key population and pathogen parameters. The first question was “Would interventions restricting travel reduce the risk of a pandemic?” In Fig.  2 , we see that reductions in travel rates always reduce risk in a network with low connectivity, where travel occurs mainly through a central hub. However, in a highly connected network with high travel rates, travel would have to be extremely highly suppressed to change the probability of a pandemic substantially, under most definitions. This accords with previous findings regarding the effectiveness of restricting travel 53 . Additionally, in the highly connected network, changing the definition of a pandemic makes little difference to the pandemic probability, for high enough values of the travel rate.

Figure  3 further illustrates the effects of different definitions. Changing the pandemic definition can sometimes greatly alter the estimated probability of a pandemic, as seen in Fig.  3 a between the yellow line, representing the 2-region transregional definition, and the purple line, representing the 10-region transregional definition. The effect on the pandemic risk of reducing travel rates also differs substantially between these two definitions. However, there are situations where changing the definition does not significantly change the pandemic probability, as seen in the same figure between the yellow line and the dashed green line, representing the 2-region interregional definition. Both the estimated risk and the effect of reducing travel are very similar in these two cases. So, while some changes in definition do not cause a large change in quantitative analyses of the risk of a pandemic, others may significantly alter both our point estimates and the predicted effects of key parameters. Figure  3 b shows that this may depend on the values of those key parameters themselves. For low travel rates, the pandemic probability is very different for the two illustrated definitions, but at high travel rates the pandemic probabilities for the two definitions converge.

The second question was “Does a portion of the population have pre-existing immunity, and does this affect the risk of a pandemic?” The presence of immunity can significantly alter the results discussed in the paragraphs above. In Fig.  5 b, the leftmost column is equivalent to the column from Fig.  2 b in which \(\lambda = 2.0 \times 10^{-4}\) per day, but with a higher transmission rate of \(\beta =0.42\) . However, as cross-immunity increases, a marked difference in the pandemic probability between different definitions becomes visible. This shows that the conclusion that precise pandemic definitions are of reduced importance in a highly connected network with high travel rates is context sensitive—if the population has high immunity, differences between definitions re-emerge.

The third question was “How is the risk of a pandemic affected by differences between regions?” In Fig.  6 , we examined how heterogeneous transmission rates in different regions affect the pandemic probability. Many pathogens have higher transmission rates in lower income countries, and novel pathogens are more likely to emerge in low income countries 50 , 51 , 54 . Putting these two facts together, we see that pathogens are most likely to emerge in countries in which they have higher transmission rates. Motivated by this, we compared the scenarios of emergence in a higher-transmission and lower-transmission region, finding that pandemic definition makes a larger difference for diseases emerging in a higher-transmission region. In particular, when the pandemic definition requires many countries to experience epidemics to qualify an outbreak as a pandemic, including countries with lower transmission rates, we see striking non-linearity in the relationship between heterogeneity and the pandemic probability. For these definitions, as the difference in transmission rates between higher- and lower-transmission regions increases, the pandemic probability increases initially, before decreasing. This initial rise is due to the enhanced spread between high-transmission regions increasing the importation rate to low-transmission regions. This result implies that, when the mean value of the transmission rate is fixed, a small gap in the effectiveness of public health infrastructure between wealthy and poor regions puts all regions at greater risk, while a larger gap protects wealthier regions while the risk for poor regions continues to increase.

To illustrate this concept, consider the contrasting examples of Ebola and COVID-19. The 2014 outbreak of Ebola virus followed the pattern of high incidence in low income countries but low incidence in high income countries. The virus spread through several low-income African countries but was effectively contained when introduced to high-income countries 55 , 56 , 57 . In this case, high-income countries had the capacity to prevent a pandemic from taking hold, being able to quickly isolate and treat symptomatic individuals. This generated high heterogeneity in transmission, corresponding to the right side of Fig.  6 a, with low-income countries at high risk and high-income countries at low risk. In contrast, high-income countries have not been able to escape the pandemic of COVID-19, in part due to asymptomatic and presymptomatic transmission of SARS-CoV-2 allowing it to evade surveillance and public health measures 58 , 59 . This has led to more similar transmission rates between different countries, corresponding to the left side of Fig.  6 a, where risk is more uniform between regions and therefore between pandemic definitions.

In our analyses, we use a metapopulation modelling framework. Metapopulation models are widely used in pandemic modelling 40 , 41 , 42 , 43 , 44 , 45 . Our novel Markov chain approach allows us to calculate pandemic probabilities directly, without requiring large numbers of simulations to generate an approximation. We expect our overall conclusion, that the effects of key parameters on pandemic risk depend on the pandemic definition, to hold irrespective of the underlying modelling framework. Future studies could replicate our analyses using different models and modelling approaches, such as metapopulation models with additional epidemiological complexity 43 , 45 , 60 , 61 or the widely used global epidemic and mobility (GLEaM) model 62 , 63 , 64 . Exploring how our quantitative results vary for different modelling frameworks in the field of mathematical epidemiology 14 , 16 , 65 , 66 , 67 is a target for further investigation.

Other future work using our modelling framework could address the role of pandemic definitions in quantifying the effects of additional epidemiological parameters on pandemic risk, such as use of different types of travel (e.g. within-country transport or international flights) 45 , 68 , 69 , the rate of nosocomial infections 70 , or age structure 71 . Our metapopulation modelling framework is generally applicable, and this framework could be extended to represent outbreaks of many different specific pathogens emerging in various locations. An important factor for response planning is the timescale over which outbreaks develop into pandemics. The duration of the initial phase of outbreaks has been a subject of previous study 72 , as has the overall duration of outbreaks 10 , 73 , 74 , 75 , 76 . In theory, Markov chain models could be used to assess the time for a local epidemic to develop into a pandemic, and we leave this as an avenue for further work.

In summary, we have developed a novel modelling framework for estimating the pandemic risk. We have applied this framework to assess the pandemic risk in a range of different scenarios, and have interpreted the results under a variety of pandemic definitions. We have found that certain relationships, such as the effect of heterogeneity in transmission between regions on the risk of a pandemic, are highly dependent on the definition of ‘pandemic’ used, while others, such as the effect of high travel rates on pandemic risk in a highly connected network, are not. This work provides a foundation for improved communication about pandemic risk, by highlighting the contexts in which pandemic definitions need to be provided in quantitative detail. In general, we contend that, when assessing the risk that an outbreak will develop into a pandemic, the precise pandemic definition used for a given analysis should be considered and stated clearly. Future work could investigate the effects of alternative definitions in more detailed epidemiological models, and extend this framework to investigate different dynamical features of pandemics.

We have combined standard epidemiological modelling techniques with a novel Markov chain treatment of metapopulation dynamics to produce a method for calculating the probabilities of epidemics and pandemics in a network of population regions. At each step of this chain, we resolve information about which regions may experience epidemics. The order in which the status of any given region is resolved does not necessarily match the order in which the given epidemics occur in calendar time. A benefit of our model is that we can calculate the probabilities of different final outcomes directly, without requiring large numbers of stochastic simulations to estimate these values. This comes at the cost that temporal information is not represented explicitly in our model: we focus on the pandemic probability, accounting for all possible ways that a pandemic could occur, rather than estimating the possible times at which epidemics could occur in different regions or the timescale over which an outbreak will develop into a pandemic (see Discussion).

We model the transmission of a pathogen through n regions labelled \(P_1, P_2, P_3, \ldots , P_n\) . Each region \(P_j\) has associated with it some intra-region pathogen transmissibility \(\beta _j\) , disease recovery rate \(\mu _j\) , and population size \(N_j\) . From these quantities it is possible to calculate a region-specific basic reproduction number \(R_{0,j}\) . This can be fixed across all regions for a particular pathogen, or allowed to vary from region to region to reflect local epidemiological differences.

First let us consider the spread of the pathogen in a single region, using well-established results of stochastic Susceptible-Infected-Recovered (SIR) models. If a region \(P_j\) contains an initial number of infected individuals \(I_j(0)\) , then in the stochastic SIR model, the probability that these individuals do not cause an epidemic in \(P_j\) is \((1/R_{0,j})^{I_j(0)}\) when \(R_{0,j}\ge 1\) , and 1 otherwise 17 . We also define the final size of an epidemic \(R_{j}(\infty )\) (not to be confused with \(R_{0,j}\) ) as the number of recovered individuals in \(P_j\) at the end of the epidemic. This equals the total number of individuals in \(P_j\) who become infected at any time, and is given by the solution of the following equation 77 .

Infected individuals are assumed to travel from region \(P_j\) to region \(P_m\) at a rate \(\lambda _{jm}\) . We seek the probability that infected individuals travelling from \(P_j\) will not cause an epidemic in \(P_m\) , in the case where initially infected individuals in \(P_m\) do not cause an epidemic in \(P_m\) (including the case where there are no initially infected individuals in \(P_m\) ). This is equal to the probability that i infected individuals migrate from \(P_j\) to \(P_m\) , multiplied by the probability that this number of individuals fails to cause a major epidemic, summed over possible values of i . The minimum value of i is the case where no infected individuals migrate, and the maximum value is the case where all individuals in \(P_j\) that become infected at any point migrate. This gives us an expression for \(q_{jm}\) , the conditional probability that, if \(P_j\) experiences an epidemic and \(P_m\) does not experience an epidemic due to a source of infected individuals other than \(P_j\) , \(P_m\) does not experience an epidemic.

This approximation is valid when the number of infected individuals that travel between regions is much smaller than the size of the regions.

We assume that infected individuals travelling from a region \(P_j\) cannot cause an epidemic in a neighbouring region \(P_m\) if \(P_j\) does not itself experience an epidemic. Then computing the value of \(q_{jm}\) for every pair of populations \(P_j\) and \(P_m\) gives us sufficient information to determine the probability of any particular set of regions connected on a network experiencing epidemics so long as there are no interactions between different groups of migrants arriving in a region, and the total numbers of migrants in any region remains very small relative to the region’s size. If these assumptions hold, we can imagine the regions on a network with weighted directed edges, where the weight of the edge directed from region \(P_j\) to region \(P_m\) is \(q_{jm}\) .

To determine how the final probabilities of epidemics depend on the pairwise probabilities \(q_{jm}\) , we use a Markov chain. The states of this Markov chain assign one of three states to each region— N (for neutral), where it is not yet determined whether the region will experience an epidemic, E (for epidemic), where it is determined that the region will experience an epidemic but it is not yet determined in which further regions it will cause epidemics, and T (for terminal), where it is determined that the region will experience an epidemic and in which further regions it will cause epidemics due to onward transmission. As our model does not explicitly represent dynamical processes occurring over time, these states should not be interpreted as actual states of infection and recovery within regions, but rather as bookkeeping devices for the role of various regions in determining the spread of the pathogen through the network.

Suppose we have a network connecting n regions. In the initial state, each region where the initially infected individuals have caused an epidemic is in state E , and all the other regions are in state N . The global state of the network is simply the product of the states of each system. We can then define a transition matrix \(\mathbf{T }\) that acts on the global state. The elements of this matrix are denoted \(t_{x_1x_2\ldots x_n \rightarrow y_1y_2\ldots y_n}\) .

\(x_j\) is the state ( N , E , or T ) of region \(P_j\) before the transition, and \(y_j\) is its state afterwards. The expression inside the first set of square brackets ensures that the only acceptable transitions for any given region are \(N \rightarrow E\) and \(E \rightarrow T\) , and requires that all epidemic regions in the initial state must be terminated in the transition (this prevents double-counting of possible transmission paths). The expression inside the second set of square brackets gives the probability of each \(N \rightarrow E\) transition, and the expression inside the final set of square brackets gives the probability of each \(N \rightarrow N\) transition, given the regions that are in state E before the transition.

Note that these transitions do not represent a dynamical process—the order of transitions in this model does not necessarily correspond to the order in which regions experience epidemics. Instead, the transitions are simply stages along the exploration of different routes and outcomes from the disease spreading process.

The initial probability of each global state \(z_1z_2\ldots z_n\) (where \(z_i \in \{N, E, T\}\) ) is given by:

where \(Q_j = \min ((1/R_{0,j})^{I_j(0)},1)\) is the probability that the initial population of infective individuals does not cause an epidemic in region \(P_j\) . Essentially, no region can begin in state T , and the probability of each initial global state is given by the product of the probabilities of each region being in the corresponding initial regional state.

In this system, all states in which no region is epidemic are absorbing, and in each transition at least one epidemic state must become terminal. This means that the system must reach an absorbing state in at most n transitions, since at least one region becomes terminal in each transition, and a fully terminal state is absorbing. So the final probability vector \(p_{\mathrm {final}}\) is given by

with \({\mathbf{T}}\) as the transition matrix and \(p_{\mathrm {initial}}\) as the vector whose elements defined by Eq. ( 5 ). This final vector gives the probabilities of each configuration of the metapopulation, with populations in state N never experiencing an epidemic, and regions in state T experiencing an epidemic at some point.

Cross-Immunity

The model described above can incorporate certain epidemiological details, such as heterogeneity of population parameters, but is restricted to treating quite simple disease dynamics. In this section we expand the model to treat pathogens that give those who overcome infection cross-protection against future strains of that pathogen. This is necessary to be able to investigate how pre-existing immunity changes how pandemic definitions affect the results of our model.

We first describe the spread of a pathogen strain X using the methods above, introducing a superscript X to the relevant parameters to mark the strain, e.g. \(R_0^X\) , \(R^X(\infty )\) , and \(p^X_{\mathrm {final}}\) . We assume that infection with pathogen X confers cross-immunity \(\alpha\) to a second strain of the pathogen, which we call Y . In each population \(P_j\) we can define an effective basic reproductive number for Y in the case that \(P_j\) has experienced an epidemic of X , which we call \(R^Y_{e,j}\) .

This expression simply multiplies the basic reproductive number by the effective number of susceptible individuals given the prevalence of cross-immunity in the population. It is through this expression that cross-immunity enters the model—the parameter \(\alpha\) does not otherwise appear in what follows.

We can write down an equation for the expected total number of individuals in \(P_j\) infected in an epidemic of Y in analogy to Eq. ( 1 ). In the case where there has been no previous epidemic of X in \(P_j\) , the expected epidemic size is the solution \(R_{j,\mathrm{no}X}^Y(\infty )\) of

In the case where there has been a previous epidemic of X in \(P_j\) , the expected epidemic size is the solution \(R_{j,X}^Y(\infty )\) of

We assume that individuals infected with Y travel at the same rate as individuals infected with X . We then define the pairwise probabilities of transmission of Y between populations in analogy to Eq. ( 2 ). That is,

where \(R^Y_{c,m} = R^Y_{0,m}\) when \(P_m\) has not experienced a previous epidemic of X , \(R^Y_{c,m} = R^Y_{e,m}\) when \(P_m\) has experienced a previous epidemic of X , \(R^Y_{j,b}(\infty ) = R^Y_{j,\mathrm{no}X}(\infty )\) when \(P_j\) has not experienced a previous epidemic of X , and \(R^Y_{j,b}(\infty ) = R^Y_{j,X}(\infty )\) when \(P_j\) has experienced a previous epidemic of X .

These expressions for \(q^Y_{jm}\) can be substituted for \(q_{jm}\) in Eq. ( 3 ) to yield a transition matrix for modelling the spread of Y , which we will call \({\mathbf{T}}^Y(s_1s_2\ldots s_n)\) , where \(s_j\) is the final state (either N or T ) of the X outbreak in \(P_j\) . We find the initial probabilities of each state with regards to Y , \(p^Y_{\mathrm {initial}}\) , in analogy to Eq. ( 5 ), given an initial number of individuals infected with Y in each population \(I^Y_j(0)\) .

where \(Q^Y_j = \min [(1/R^Y_{0,j})^{I^Y_j(0)},1]\) when \(P_j\) has not experienced a previous epidemic of X (i.e. \(s_j=N\) ), and \(Q^Y_j = \min [(1/R^Y_{e,j})^{I^Y_j(0)},1]\) when \(P_j\) has experienced a previous epidemic of X (i.e \(s_j = T\) ). We can then write the final probabilities of each combination of possible epidemics of Y , for a given set of previous epidemics of X , as

To find the overall probability of each combination of epidemics of Y in various populations given a prior probability of each combination of epidemics of X (given by \(p^X_{\mathrm {final}}(s_1s_2\ldots s_n)\) defined in Eq. ( 6 )), we sum over the possible values of \((s_1s_2\ldots s_n)\) , weighted by their probability.

Code availability

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This work was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. This research was funded by Christ Church, Oxford, via a Junior Research Fellowship (RNT).

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Singer, B.J., Thompson, R.N. & Bonsall, M.B. The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk. Sci Rep 11 , 2547 (2021). https://doi.org/10.1038/s41598-021-81814-3

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Could expanding the covid-19 case definition improve the UK’s pandemic response?

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• Alex Crozier , biomedical researcher 1 ,
• Jake Dunning , senior research fellow 2 3 ,
• Selina Rajan , public health specialist registrar 4 ,
• Malcolm G Semple , professor of outbreak medicine and child health 5 6 ,
• Iain E Buchan , professor of public health and clinical informatics 5 7
• 1 Division of Biosciences, University College London, London, UK
• 2 Royal Free London NHS Foundation Trust, London, UK
• 3 Epidemic Diseases Research Group Oxford, University of Oxford, Oxford, UK
• 4 Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
• 5 NIHR Health Protection Research Unit in Emerging and Zoonotic Infections and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
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• Correspondence to: A Crozier alexander.crozier.20{at}ucl.ac.uk

Alex Crozier and colleagues evaluate the potential opportunities and challenges of expanding the symptom list linked to self-isolation and testing as vaccines are rolled out

During the covid-19 pandemic the British public has been instructed: “If you have a high fever, a new continuous cough, or you’ve lost your sense of smell or taste or its changed, self-isolate and get a test.” 1 Yet these symptoms are just a few of many described by those infected with SARS-CoV-2. 2 3 4 5 Many people with mild-to-moderate disease don’t have these symptoms (initially), and other symptoms often manifest earlier. 3 6 Most spread is from symptomatic cases around the time of symptom onset, 7 8 9 10 11 and interrupting transmission depends on early identification and isolation of contagious individuals. 12 13 The narrow UK case definition therefore limits this detection, restricting the effectiveness of the test, trace, and isolate programme. 8 14 15

As vaccination progresses and social mixing increases, infections are now highest among young, unvaccinated, or partially vaccinated people, who are also more likely to experience ‘unofficial’ symptoms. 16 17 Variants are adding further to transmission, as predicted, with potential for another wave of hospital admissions and deaths. 18 Improvements in transmission control are urgently needed. Here, we build on calls to broaden the UK’s covid-19 case definition, 5 19 analysing the potential to improve self-isolation and symptomatic testing guided by a case definition fit for the vaccination era.

Updating the UK’s clinical case definition

The European Centre for Disease Prevention and Control described a breadth of symptoms associated with mild-to-moderate covid-19, the most commonly reported being headache (70%), nasal obstruction (68%), weakness or fatigue (63%), myalgia (63%), rhinorrhoea (60%), gustatory dysfunction (54%), and sore throat (53%). 20 Many infected people do not present with the symptoms used in the UK case definition: loss of taste or smell, a cough, or fever which, before vaccination rollout were reported by 70%, 63%, and 45% of symptomatic cases, respectively. 3 21 While restricting access to symptomatic testing to those with “official” symptoms may control the volume of testing, this narrow definition is now likely to impede control of transmission.

Critically, unofficial symptoms often manifest earlier. 9 In a recent population based study in Arizona, US, the most commonly reported first symptoms were sore throat (19%), headache (16%), cough (13%), runny nose or cold-like symptoms (12%), and fatigue (12%). 22 These symptoms are more common in school age children 16 and younger people, 17 who now account for an even greater proportion of transmission because older people are vaccinated.

The World Health Organization 2 and Centers for Disease Control and Prevention 4 already include nine and 11 more case defining symptoms, respectively, than the UK. Greater testing capacity is now available to accommodate a wider case definition in the UK, particularly with rapid antigen tests. However, rapid tests are officially being used only for self-testing (at home or at testing centres) by people without symptoms, 23 24 although some people with wider symptoms may also be using them. 25 Symptomatic testing using reverse transcription polymerase chain reaction (RT-PCR) tests meanwhile is open only to those declaring a high temperature, a new continuous cough, or a loss or change in sense of smell, and to confirmed contacts of RT-PCR positive cases.

The UK’s narrow clinical case definition impedes not only the identification of cases but also the understanding of SARS-CoV-2 transmission. Although infected individuals without symptoms can clearly pass on the virus, 26 the characterisation of asymptomatic infection and transmission has been poor. 3 It is important to distinguish between those not experiencing symptoms throughout infection (persistently asymptomatic), becoming infectious before symptoms manifest (presymptomatic), or having only unofficial or subtle symptoms (pauci-symptomatic). Persistently asymptomatic cases probably account for less than 20% of infections, and these people may be 3-25 times less likely than those with symptoms to pass on the virus. 7 8 9 10 11

Real world evidence suggests presymptomatic and (official and unofficial) symptomatic cases drive transmission more than asymptomatic cases. 7 8 9 10 11 It seems counterintuitive, therefore, to have no official UK guidance on wider covid-19 symptoms, or to offer different testing routes for those with official symptoms and those with no symptoms, with nothing in- between. People with unofficial symptoms can bypass the rules to get a test—legitimising this choice could be helpful.

Concerns have been raised over testing capacity, false negative rapid test results, and non-compliance with self-isolation. 23 24 However, the benefits of identifying more cases sooner are likely to be substantial. The Scientific Advisory Group for Emergencies (SAGE) recommended “prioritising rapid testing of symptomatic people is likely to have a greater impact on identifying positive cases and reducing transmission than frequent testing of asymptomatic people in an outbreak area.” 27

Testing people with a single, non-specific symptom could, of course, overwhelm or waste capacity. Indeed, September 2020 government advisory groups 28 29 considered data from the First Few Hundred Study 30 and Covid Symptom Study App to reason against expanding eligibility for symptomatic testing. The data suggested expanding the definition would decrease symptom specificity from 97% to 94% while only marginally increasing symptom sensitivity from 85% to 95%. However, more recent evidence on symptom combinations warrants reconsidering the case definition, especially since vaccination means the population most likely to be infected and transmit will now be younger or partially immunised, and so less likely to experience severe disease or official symptoms.

Combinations of symptoms could be used to help identify more cases sooner without overwhelming testing capacity. An age stratified approach derived from the React study selected chills (all ages), headache (5–17 years), appetite loss (≥18 years), and muscle aches (18–54 years) as jointly predictive of positive RT-PCR results, together with the official symptoms. 5 The authors concluded that triage based on these symptoms would identify more cases than the current approach, at any level of testing.

The Virus Watch cohort suggested that using a wider symptom definition captured cases a day earlier than the current definition, on average, 31 a critical time difference for preventing transmission. The Covid Symptom Study App was used to identify optimal symptom combinations for capturing most cases with fewest tests, and found that within three days of symptom onset, dyspnoea plus the official symptom combination (cough, fever, loss of smell or taste) identified only 69% of symptomatic cases and required 47 tests for each case identified. 32 The combination with the highest coverage (fatigue, loss of smell or taste, cough, diarrhoea, headache, sore throat) identified 96% of symptomatic cases (requiring 96 tests per case identified). 32 This combination of symptoms would increase the number of cases captured by symptomatic testing by over a third, and would likely result in earlier identification of many cases, 22 potentially containing transmission more as we reopen society.

Implementing an updated clinical case definition

Expanding the case definition is likely to increase demand for testing and numbers self-isolating. The system-wide effects would be complex, requiring careful implementation. 33 Any change must neither overwhelm NHS Test and Trace nor impede existing symptomatic testing. Instructions such as “isolate if you have case defining symptoms, regardless of test status” must not lose clarity despite more complex lists of symptoms. Potential harms from false negative or positive results need mitigation. While it is essential to consider the pre-test probability of infection (based on background prevalence, epidemiological history, and clinical presentation) and the performance of the test used, 34 35 a substantial net reduction in transmission is likely if more symptomatic people are identified and isolate sooner.

The UK’s decision to adopt a narrow case definition was based on ease of communication, avoiding confusion with other infections, and preserving testing capacity. This situation is now different—testing capacity is high. The emergence of the delta variant and the potential evolution of more transmissible or vaccine resistant variants means that, even with vaccination, further waves of cases, hospital admissions, and deaths may ensue. 18 Mitigating these waves, and the potential for enduring transmission, 36 requires agile intervention to minimise the risks of vaccine escape variants, long covid, further NHS disruption, and harms from restrictions. To realise the benefits of a wider case definition it will be necessary to revise policies for testing and self-isolation.

Since RT-PCR capacity is limited, and quick turnaround is vital, we suggest dynamic targeting of RT-PCR testing, guided by continuous review of symptoms, transmission patterns, variants, vaccination uptake, and circulation of other respiratory viruses. Routinely collected data could be used to adapt testing eligibility, access, and communications systematically and quickly. 37 38 39 Communication is particularly important considering that only half the public can correctly identify the existing official covid-19 symptoms. 40 Data intensive, intelligence-led adaptation of the test, trace, and isolate system could make an important contribution to the UK’s pandemic responses while we wait for the vaccination programme to progress as far as possible and for covid-19 to abate.

Refining test, trace, and isolate

Given the heterogeneity in SARS-CoV-2 transmission, 8 14 15 whereby fewer than 20% of cases may account for more than 80% of transmission, reopening society ahead of maximum vaccination coverage requires better identification and self-isolation of infectious cases to contain emerging clusters. To achieve this, the NHS Test and Trace system must increase the proportion of cases tested (and isolated) early in their infection and trace more contacts before onwards transmission. Early, active case finding combined with enhanced contact tracing (including backwards to identify source of infection), 14 effective symptom monitoring, 41 and prompt contact testing 42 can also reduce transmission. 13 Repeat testing of contacts may usefully replace isolation for those without symptoms. 43 44 Viral sequencing can also help trace clusters back to their source, 45 as well as targeting resources to identify and contain more transmissible or vaccine resistant variants. Hyper-local approaches—involving communities at neighbourhood or street level, in faith groups, and other local contexts—are also vital.

Testing uptake among people with symptoms has been low, and engagement with testing and isolation has been lowest in communities with the highest prevalence of SARS-CoV-2 and the gravest consequences from covid-19. 23 24 40 Effective support, including prompt financial help, during self-isolation is the key to controlling transmission. 46 47 To make the most of an expanded case definition, public health and NHS systems must integrate more at both local and national levels, 48 49 50 enabling nimbler, more equitable targeting of test-trace-isolate resources 51 52 and surge vaccination. 53 In addition, combinations of RT-PCR and rapid antigen tests may be helpful in reducing delays between symptom onset, testing, self-isolation, and initiation of contact tracing. 39

Vaccinations alone are unlikely to end the pandemic. New, more transmissible and (partially) vaccine resistant variants may spread through susceptible populations causing high hospital admission rates. Inequities in vaccination are also shifting the burden of disease and disruption to the most disadvantaged communities, who are also harmed most by covid-19 restrictions. To reopen society with greater speed and fairness, control of transmission must improve. This starts with an expanded and more context appropriate case definition and rests on adaptive, locally grounded, and information-led public health responses.

Key messages

Covid-19 is associated with a wide range of symptoms

Many patients do not experience the UK’s official case defining symptoms, initially, or ever, and other symptoms often manifest earlier

Limiting the symptomatic testing to those with these official symptoms will miss or delay identification of many covid-19 cases, hampering efforts to interrupt transmission

Expanding the clinical case definition of covid−19, the criteria for self-isolation, and eligibility for symptomatic testing could improve the UK’s pandemic response

Dynamic targeting based on data could avoid overloading resources

Contributors and sources: The authors have broad experience and direct involvement in covid-19 responses. AC has expertise in developing and troubleshooting diagnostic assays and improved covid-19 testing programmes for sports organisations. JD was a national incident director for covid-19 at Public Health England. SR has supported the Public Health England regional response, including managing outbreaks in care homes and educational institutions. MGS is a member of the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and regularly attends SAGE covid-19. IB is a NIHR senior investigator and led the evaluation of the Liverpool community testing pilot. AC conceived the article and wrote the first draft in discussion with all authors, and with input from Martin McKee. All authors edited drafts. IB acts as guarantor.

Competing interests: We have read and understood BMJ policy on declaration of interests and have the following interests to declare: IEB and MGS received grant funding from the UK Department of Health and Social Care to evaluate lateral flow tests in the Liverpool pilot. IEB reports fees from AstraZeneca as chief data scientist adviser via Liverpool University outside the submitted work. MGS is chair of the infectious disease scientific advisory board and a minority shareholder in Integrum Scientific which has interests in covid-19 testing, and reports grants from the NIHR, the Medical Research Council, and the Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool.

Provenance and peer review: Not commissioned; externally peer reviewed.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

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Could expanding the covid-19 case definition improve the UK's pandemic response?

Affiliations.

• 1 Division of Biosciences, University College London, London, UK [email protected].
• 2 Royal Free London NHS Foundation Trust, London, UK.
• 3 Epidemic Diseases Research Group Oxford, University of Oxford, Oxford, UK.
• 4 Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
• 5 NIHR Health Protection Research Unit in Emerging and Zoonotic Infections and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
• 6 Respiratory Medicine, Alder Hey Children's Hospital, Liverpool, UK.
• 7 Institute of Population Health, University of Liverpool, Liverpool, UK.
• PMID: 34193527
• DOI: 10.1136/bmj.n1625

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Conflict of interest statement

Competing interests: We have read and understood BMJ policy on declaration of interests and have the following interests to declare: IEB and MGS received grant funding from the UK Department of Health and Social Care to evaluate lateral flow tests in the Liverpool pilot. IEB reports fees from AstraZeneca as chief data scientist adviser via Liverpool University outside the submitted work. MGS is chair of the infectious disease scientific advisory board and a minority shareholder in Integrum Scientific which has interests in covid-19 testing, and reports grants from the NIHR, the Medical Research Council, and the Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool. Provenance and peer review: Not commissioned; externally peer reviewed.

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MINI REVIEW article

Covid-19: emergence, spread, possible treatments, and global burden.

• 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
• 2 Department of Health Sciences, School of Education and Health, Cape Breton University, Sydney, NS, Canada

The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death. Until the outbreak of SARS, this group of viruses was greatly overlooked. However, since the SARS and MERS outbreaks, these viruses have been studied in greater detail, propelling the vaccine research. On December 31, 2019, mysterious cases of pneumonia were detected in the city of Wuhan in China's Hubei Province. On January 7, 2020, the causative agent was identified as a new coronavirus (2019-nCoV), and the disease was later named as COVID-19 by the WHO. The virus spread extensively in the Wuhan region of China and has gained entry to over 210 countries and territories. Though experts suspected that the virus is transmitted from animals to humans, there are mixed reports on the origin of the virus. There are no treatment options available for the virus as such, limited to the use of anti-HIV drugs and/or other antivirals such as Remdesivir and Galidesivir. For the containment of the virus, it is recommended to quarantine the infected and to follow good hygiene practices. The virus has had a significant socio-economic impact globally. Economically, China is likely to experience a greater setback than other countries from the pandemic due to added trade war pressure, which have been discussed in this paper.

Introduction

Coronaviridae is a family of viruses with a positive-sense RNA that possess an outer viral coat. When looked at with the help of an electron microscope, there appears to be a unique corona around it. This family of viruses mainly cause respiratory diseases in humans, in the forms of common cold or pneumonia as well as respiratory infections. These viruses can infect animals as well ( 1 , 2 ). Up until the year 2003, coronavirus (CoV) had attracted limited interest from researchers. However, after the SARS (severe acute respiratory syndrome) outbreak caused by the SARS-CoV, the coronavirus was looked at with renewed interest ( 3 , 4 ). This also happened to be the first epidemic of the 21st century originating in the Guangdong province of China. Almost 10 years later, there was a MERS (Middle East respiratory syndrome) outbreak in 2012, which was caused by the MERS-CoV ( 5 , 6 ). Both SARS and MERS have a zoonotic origin and originated from bats. A unique feature of these viruses is the ability to mutate rapidly and adapt to a new host. The zoonotic origin of these viruses allows them to jump from host to host. Coronaviruses are known to use the angiotensin-converting enzyme-2 (ACE-2) receptor or the dipeptidyl peptidase IV (DPP-4) protein to gain entry into cells for replication ( 7 – 10 ).

In December 2019, almost seven years after the MERS 2012 outbreak, a novel Coronavirus (2019-nCoV) surfaced in Wuhan in the Hubei region of China. The outbreak rapidly grew and spread to neighboring countries. However, rapid communication of information and the increasing scale of events led to quick quarantine and screening of travelers, thus containing the spread of the infection. The major part of the infection was restricted to China, and a second cluster was found on a cruise ship called the Diamond Princess docked in Japan ( 11 , 12 ).

The new virus was identified to be a novel Coronavirus and was thus initially named 2019-nCoV; later, it was renamed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ( 13 ), and the disease it causes is now referred to as Coronavirus Disease-2019 (COVID-19) by the WHO. The virus was suspected to have begun its spread in the Huanan seafood wholesale market in the Wuhan region. It is possible that an animal that was carrying the virus was brought into or sold in the market, causing the spread of the virus in the crowded marketplace. One of the first claims made was in an article published in the Journal of Medical Virology ( 14 ), which identified snakes as the possible host. A second possibility was that pangolins could be the wild host of SARS-CoV-2 ( 15 ), though the most likely possibility is that the virus originated from bats ( 13 , 16 – 19 ). Increasing evidence and experts are now collectively concluding the virus had a natural origin in bats, as with previous such respiratory viruses ( 2 , 20 – 24 ).

Similarly, SARS and MERS were also suspected to originate from bats. In the case of MERS, the dromedary camel is an intermediate host ( 5 , 10 ). Bats have been known to harbor coronaviruses for quite some time now. Just as in the case of avian flu, SARS, MERS, and possibly even HIV, with increasing selection and ecological pressure due to human activities, the virus made the jump from animal to man. Humans have been encroaching increasingly into forests, and this is true over much of China, as in Africa. Combined with additional ecological pressure due to climate change, such zoonotic spillovers are now more common than ever. It is likely that the next disease X will also have such an origin ( 25 ). We have learned the importance of identification of the source organism due to the Ebola virus pandemic. Viruses are unstable organisms genetically, constantly mutating by genetic shift or drift. It is not possible to predict when a cross-species jump may occur and when a seemingly harmless variant form of the virus may turn into a deadly strain. Such an incident occurred in Reston, USA, with the Reston virus ( 26 ), an alarming reminder of this possibility. The identification of the original host helps us to contain future spreads as well as to learn about the mechanism of transmission of viruses. Until the virus is isolated from a wild animal host, in this case, mostly bats, the zoonotic origin will remain hypothetical, though likely. It should further be noted that the virus has acquired several mutations, as noted by a group in China, indicating that there are more than two strains of the virus, which may have had an impact on its pathogenicity. However, this claim remains unproven, and many experts have argued otherwise; data proving this are not yet available ( 27 ). A similar finding was reported from Italy and India independently, where they found two strains ( 28 , 29 ). These findings need to be further cross-verified by similar analyses globally. If true, this finding could effectively explain why some nations are more affected than others.

Transmission

When the spread of COVID-19 began ( Figure 1 ), the virus appeared to be contained within China and the cruise ship “Diamond Princess,” which formed the major clusters of the virus. However, as of April 2020, over 210 countries and territories are affected by the virus, with Europe, the USA, and Iran forming the new cluster of the virus. The USA ( Figure 2 ) has the highest number of confirmed COVID-19 cases, whereas India and China, despite being among the most population-dense countries in the world, have managed to constrain the infection rate by the implementation of a complete lockdown with arrangements in place to manage the confirmed cases. Similarly, the UK has also managed to maintain a low curve of the graph by implementing similar measures, though it was not strictly enforced. Reports have indicated that the presence of different strains or strands of the virus may have had an effect on the management of the infection rate of the virus ( 27 – 29 ). The disease is spread by droplet transmission. As of April 2020, the total number of infected individuals stands at around 3 million, with ~200,000 deaths and more than 1 million recoveries globally ( 30 , 34 ). The virus thus has a fatality rate of around 2% and an R 0 of 3 based on current data. However, a more recent report from the CDC, Atlanta, USA, claims that the R 0 could be as high as 5.7 ( 35 ). It has also been observed from data available from China and India that individuals likely to be infected by the virus from both these countries belong to the age groups of 20–50 years ( 36 , 37 ). In both of these countries, the working class mostly belongs to this age group, making exposure more likely. Germany and Singapore are great examples of countries with a high number of cases but low fatalities as compared to their immediate neighbors. Singapore is one of the few countries that had developed a detailed plan of action after the previous SARS outbreak to deal with a similar situation in the future, and this worked in their favor during this outbreak. Both countries took swift action after the outbreak began, with Singapore banning Chinese travelers and implementing screening and quarantine measures at a time when the WHO recommended none. They ordered the elderly and the vulnerable to strictly stay at home, and they ensured that lifesaving equipment and large-scale testing facilities were available immediately ( 38 , 39 ). Germany took similar measures by ramping up testing capacity quite early and by ensuring that all individuals had equal opportunity to get tested. This meant that young, old, and at-risk people all got tested, thus ensuring positive results early during disease progression and that most cases were mild like in Singapore, thus maintaining a lower death percentage ( 40 ). It allowed infected individuals to be identified and quarantined before they even had symptoms. Testing was carried out at multiple labs, reducing the load and providing massive scale, something which countries such as the USA did quite late and India restricted to select government and private labs. The German government also banned large gatherings and advocated social distancing to further reduce the spread, though unlike India and the USA, this was done quite late. South Korea is another example of how a nation has managed to contain the spread and transmission of the infection. South Korea and the USA both reported their first COVID-19 cases on the same day; however, the US administration downplayed the risks of the disease, unlike South Korean officials, who constantly informed their citizens about the developments of the disease using the media and a centralized messaging system. They also employed the Trace, Test, and Treat protocol to identify and isolate patients fast, whereas the USA restricted this to patients with severe infection and only later broadened this criterion, like many European countries as well as India. Unlike the USA, South Korea also has universal healthcare, ensuring free diagnostic testing.

Figure 1 . Timeline of COVID-19 progression ( 30 – 32 ).

Figure 2 . Total confirmed COVID 19 cases as of May 2020 ( 33 ).

The main mode of transmission of 2019-nCoV is human to human. As of now, animal-to-human transfer has not yet been confirmed. Asymptomatic carriers of the virus are at major risk of being superinfectors with this disease, as all those infected may not develop the disease ( 41 ). This is a concern that has been raised by nations globally, with the Indian government raising concerns on how to identify and contain asymptomatic carriers, who could account for 80% of those infected ( 42 ). Since current resources are directed towards understanding the hospitalized individuals showing symptoms, there is still a vast amount of information about asymptomatic individuals that has yet to be studied. For example, some questions that need to be answered include: Do asymptomatic individuals develop the disease at any point in time at all? Do they eventually develop antibodies? How long do they shed the virus for? Can any tissue of these individuals store the virus in a dormant state? Asymptomatic transmission is a gray area that encompasses major unknowns in COVID-19.

The main route of human-to-human transmission is by droplets, which are generated during coughing, talking, or sneezing and are then inhaled by a healthy individual. They can also be indirectly transmitted to a person when they land on surfaces that are touched by a healthy individual who may then touch their nose, mouth, or eyes, allowing the virus entry into the body. Fomites are also a common issue in such diseases ( 43 ).

Aerosol-based transmission of the virus has not yet been confirmed ( 43 ). Stool-based transmission via the fecal-oral route may also be possible since the SARS-CoV-2 has been found in patient feces ( 44 , 45 ). Some patients with COVID-19 tend to develop diarrhea, which can become a major route of transmission if proper sanitation and personal hygiene needs are not met. There is no evidence currently available to suggest intrauterine vertical transmission of the disease in pregnant women ( 46 ).

More investigation is necessary of whether climate has played any role in the containment of the infection in countries such as India, Singapore, China, and Israel, as these are significantly warmer countries as compared with the UK, the USA, and Canada ( Figure 2 ). Ideally, a warm climate should prevent the virus from surviving for longer periods of time on surfaces, reducing transmissibility.

Pathophysiology

On gaining entry via any of the mucus membranes, the single-stranded RNA-based virus enters the host cell using type 2 transmembrane serine protease (TMPRSS2) and ACE2 receptor protein, leading to fusion and endocytosis with the host cell ( 47 – 49 ). The uncoated RNA is then translated, and viral proteins are synthesized. With the help of RNA-dependant RNA polymerase, new RNA is produced for the new virions. The cell then undergoes lysis, releasing a load of new virions into the patients' body. The resultant infection causes a massive release of pro-inflammatory cytokines that causes a cytokine storm.

Clinical Presentation

The clinical presentation of the disease resembles beta coronavirus infections. The virus has an incubation time of 2–14 days, which is the reason why most patients suspected to have the illness or contact with an individual having the illness remain in quarantine for the said amount of time. Infection with SARS-CoV-2 causes severe pneumonia, intermittent fever, and cough ( 50 , 51 ). Symptoms of rhinorrhoea, pharyngitis, and sneezing have been less commonly seen. Patients often develop acute respiratory distress syndrome within 2 days of hospital admission, requiring ventilatory support. It has been observed that during this phase, the mortality tends to be high. Chest CT will show indicators of pneumonia and ground-glass opacity, a feature that has helped to improve the preliminary diagnosis ( 51 ). The primary method of diagnosis for SARS-CoV-2 is with the help of PCR. For the PCR testing, the US CDC recommends testing for the N gene, whereas the Chinese CDC recommends the use of ORF lab and N gene of the viral genome for testing. Some also rely on the radiological findings for preliminary screening ( 52 ). Additionally, immunodiagnostic tests based on the presence of antibodies can also play a role in testing. While the WHO recommends the use of these tests for research use, many countries have pre-emptively deployed the use of these tests in the hope of ramping up the rate and speed of testing ( 52 – 54 ). Later, they noticed variations among the results, causing them to stop the use of such kits; there was also debate among the experts about the sensitivity and specificity of the tests. For immunological tests, it is beneficial to test for antibodies against the virus produced by the body rather than to test for the presence of the viral proteins, since the antibodies can be present in larger titers for a longer span of time. However, the cross-reactivity of these tests with other coronavirus antibodies is something that needs verification. Biochemical parameters such as D-dimer, C-reactive protein, and variations in neutrophil and lymphocyte counts are some other parameters that can be used to make a preliminary diagnosis; however, these parameters vary in a number of diseases and thus cannot be relied upon conclusively ( 51 ). Patients with pre-existing diseases such as asthma or similar lung disorder are at higher risk, requiring life support, as are those with other diseases such as diabetes, hypertension, or obesity. Those above the age of 60 have displayed the highest mortality rate in China, a finding that is mirrored in other nations as well ( Figure 3 ) ( 55 ). If we cross-verify these findings with the population share that is above the age of 70, we find that Italy, the United Kingdom, Canada, and the USA have one of the highest elderly populations as compared to countries such as India and China ( Figure 4 ), and this also reflects the case fatality rates accordingly ( Figure 5 ) ( 33 ). This is a clear indicator that aside from comorbidities, age is also an independent risk factor for death in those infected by COVID-19. Also, in the US, it was seen that the rates of African American deaths were higher. This is probably due to the fact that the prevalence of hypertension and obesity in this community is higher than in Caucasians ( 56 , 57 ). In late April 2020, there are also claims in the US media that young patients in the US with COVID-19 may be at increased risk of stroke; however, this is yet to be proven. We know that coagulopathy is a feature of COVID-19, and thus stroke is likely in this condition ( 58 , 59 ). The main cause of death in COVID-19 patients was acute respiratory distress due to the inflammation in the linings of the lungs caused by the cytokine storm, which is seen in all non-survival cases and in respiratory failure. The resultant inflammation in the lungs, served as an entry point of further infection, associated with coagulopathy end-organ failure, septic shock, and secondary infections leading to death ( 60 – 63 ).

Figure 3 . Case fatality rate by age in selected countries as of April 2020 ( 33 ).

Figure 4 . Case fatality rate in selected countries ( 33 ).

Figure 5 . Population share above 70 years of age ( 33 ).

For COVID-19, there is no specific treatment available. The WHO announced the organization of a trial dubbed the “Solidarity” clinical trial for COVID-19 treatments ( 64 ). This is an international collaborative study that investigates the use of a few prime candidate drugs for use against COVID-19, which are discussed below. The study is designed to reduce the time taken for an RCT by over 80%. There are over 1087 studies ( Supplementary Data 1 ) for COVID-19 registered at clinicaltrials.gov , of which 657 are interventional studies ( Supplementary Data 2 ) ( 65 ). The primary focus of the interventional studies for COVID-19 has been on antimalarial drugs and antiviral agents ( Table 1 ), while over 200 studies deal with the use of different forms of oxygen therapy. Most trials focus on improvement of clinical status, reduction of viral load, time to improvement, and reduction of mortality rates. These studies cover both severe and mild cases.

Table 1 . List of therapeutic drugs under study for COVID-19 as per clinical trials registered under clinicaltrials.gov .

Use of Antimalarial Drugs Against SARS-CoV-2

The use of chloroquine for the treatment of corona virus-based infection has shown some benefit in the prevention of viral replication in the cases of SARS and MERS. However, it was not validated on a large scale in the form of a randomized control trial ( 50 , 66 – 68 ). The drugs of choice among antimalarials are Chloroquine (CQ) and Hydroxychloroquine (HCQ). The use of CQ for COVID-19 was brought to light by the Chinese, especially by the publication of a letter to the editor of Bioscience Trends by Gao et al. ( 69 ). The letter claimed that several studies found CQ to be effective against COVID-19; however, the letter did not provide many details. Immediately, over a short span of time, interest in these two agents grew globally. Early in vitro data have revealed that chloroquine can inhibit the viral replication ( 70 , 71 ).

HCQ and CQ work by raising the pH of the lysosome, the cellular organelle that is responsible for phagocytic degradation. Its function is to combine with cell contents that have been phagocytosed and break them down eventually, in some immune cells, as a downstream process to display some of the broken proteins as antigens, thus further enhancing the immune recruitment against an antigen/pathogen. The drug was to be administered alone or with azithromycin. The use of azithromycin may be advocated by the fact that it has been seen previously to have some immunomodulatory role in airway-related disease. It appears to reduce the release of pro-inflammatory cytokines in respiratory illnesses ( 72 ). However, HCQ and azithromycin are known to have a major drug interaction when co-administered, which increases the risk of QT interval prolongation ( 73 ). Quinine-based drugs are known to have adverse effects such as QT prolongation, retinal damage, hypoglycemia, and hemolysis of blood in patients with G-6-PD deficiency ( 66 ). Several preprints, including, a metanalysis now indicate that HCQ may have no benefit for severe or critically ill patients who have COVID-19 where the outcome is need for ventilation or death ( 74 , 75 ). As of April 21, 2020, after having pre-emptively recommended their use for SARS-CoV-2 infection, the US now advocates against the use of these two drugs based on the new data that has become available.

Use of Antiviral Drugs Against SARS-CoV-2

The antiviral agents are mainly those used in the case of HIV/AIDS, these being Lopinavir and Ritonavir. Other agents such as nucleoside analogs like Favipiravir, Ribavirin, Remdesivir, and Galidesivir have been tested for possible activity in the prevention of viral RNA synthesis ( 76 ). Among these drugs, Lopinavir, Ritonavir, and Remdesivir are listed in the Solidarity trial by the WHO.

Remdesivir is a nucleotide analog for adenosine that gets incorporated into the viral RNA, hindering its replication and causing chain termination. This agent was originally developed for Ebola Virus Disease ( 77 ). A study was conducted with rhesus macaques infected with SARS-CoV-2 ( 78 ). In that study, after 12 h of infection, the monkeys were treated with either Remdesivir or vehicle. The drug showed good distribution in the lungs, and the animals treated with the drug showed a better clinical score than the vehicle group. The radiological findings of the study also indicated that the animals treated with Remdesivir have less lung damage. There was a reduction in viral replication but not in virus shedding. Furthermore, there were no mutations found in the RNA polymerase sequences. A randomized clinical control study that became available in late April 2020 ( 79 ), having 158 on the Remdesivir arm and 79 on the placebo arm, found that Remdesivir reduced the time to recovery in the Remdesivir-treated arm to 11 days, while the placebo-arm recovery time was 15 days. Though this was not found to be statistically significant, the agent provided a basis for further studies. The 28-days mortality was found to be similar for both groups. This has now provided us with a basis on which to develop future molecules. The study has been supported by the National Institute of Health, USA. The authors of the study advocated for more clinical trials with Remdesivir with a larger population. Such larger studies are already in progress, and their results are awaited. Remdesivir is currently one of the drugs that hold most promise against COVID-19.

An early trial in China with Lopinavir and Ritonavir showed no benefit compared with standard clinical care ( 80 ). More studies with this drug are currently underway, including one in India ( 81 , 82 ).

Use of Convalescent Patient Plasma

Another possible option would be the use of serum from convalescent individuals, as this is known to contain antibodies that can neutralize the virus and aid in its elimination. This has been tried previously for other coronavirus infections ( 83 ). Early emerging case reports in this aspect look promising compared to other therapies that have been tried ( 84 – 87 ). A report from China indicates that five patients treated with plasma recovered and were eventually weaned off ventilators ( 84 ). They exhibited reductions in fever and viral load and improved oxygenation. The virus was not detected in the patients after 12 days of plasma transfusion. The US FDA has provided detailed recommendations for investigational COVID-19 Convalescent Plasma use ( 88 ). One of the benefits of this approach is that it can also be used for post-exposure prophylaxis. This approach is now beginning to be increasingly adopted in other countries, with over 95 trials registered on clinicaltrials.gov alone, of which at least 75 are interventional ( 89 ). The use of convalescent patient plasma, though mostly for research purposes, appears to be the best and, so far, the only successful option for treatment available.

From a future perspective, the use of monoclonal antibodies for the inhibition of the attachment of the virus to the ACE-2 receptor may be the best bet. Aside from this, ACE-2-like molecules could also be utilized to attach and inactivate the viral proteins, since inhibition of the ACE-2 receptor would not be advisable due to its negative repercussions physiologically. In the absence of drug regimens and a vaccine, the treatment is symptomatic and involves the use of non-invasive ventilation or intubation where necessary for respiratory failure patients. Patients that may go into septic shock should be managed as per existing guidelines with hemodynamic support as well as antibiotics where necessary.

The WHO has recommended that simple personal hygiene practices can be sufficient for the prevention of spread and containment of the disease ( 90 ). Practices such as frequent washing of soiled hands or the use of sanitizer for unsoiled hands help reduce transmission. Covering of mouth while sneezing and coughing, and disinfection of surfaces that are frequently touched, such as tabletops, doorknobs, and switches with 70% isopropyl alcohol or other disinfectants are broadly recommended. It is recommended that all individuals afflicted by the disease, as well as those caring for the infected, wear a mask to avoid transmission. Healthcare works are advised to wear a complete set of personal protective equipment as per WHO-provided guidelines. Fumigation of dormitories, quarantine rooms, and washing of clothes and other fomites with detergent and warm water can help get rid of the virus. Parcels and goods are not known to transmit the virus, as per information provided by the WHO, since the virus is not able to survive sufficiently in an open, exposed environment. Quarantine of infected individuals and those who have come into contact with an infected individual is necessary to further prevent transmission of the virus ( 91 ). Quarantine is an age-old archaic practice that continues to hold relevance even today for disease containment. With the quarantine being implemented on such a large scale in some countries, taking the form of a national lockdown, the question arises of its impact on the mental health of all individuals. This topic needs to be addressed, especially in countries such as India and China, where it is still a matter of partial taboo to talk about it openly within the society.

In India, the Ministry of Ayurveda, Yoga, and Naturopathy, Unani, Siddha and Homeopathy (AYUSH), which deals with the alternative forms of medicine, issued a press release that the homeopathic, drug Arsenicum album 30, can be taken on an empty stomach for 3 days to provide protection against the infection ( 92 ). It also provided a list of herbal drugs in the same press release as per Ayurvedic and Unani systems of medicine that can boost the immune system to deal with the virus. However, there is currently no evidence to support the use of these systems of medicine against COVID-19, and they need to be tested.

The prevention of the disease with the use of a vaccine would provide a more viable solution. There are no vaccines available for any of the coronaviruses, which includes SARS and MERS. The development of a vaccine, however, is in progress at a rapid pace, though it could take about a year or two. As of April 2020, no vaccine has completed the development and testing process. A popular approach has been with the use of mRNA-based vaccine ( 93 – 96 ). mRNA vaccines have the advantage over conventional vaccines in terms of production, since they can be manufactured easily and do not have to be cultured, as a virus would need to be. Alternative conventional approaches to making a vaccine against SARS-CoV-2 would include the use of live attenuated virus as well as using the isolated spike proteins of the virus. Both of these approaches are in progress for vaccine development ( 97 ). Governments across the world have poured in resources and made changes in their legislation to ensure rapid development, testing, and deployment of a vaccine.

Barriers to Treatment

Lack of transparency and poor media relations.

The lack of government transparency and poor reporting by the media have hampered the measures that could have been taken by healthcare systems globally to deal with the COVID-19 threat. The CDC, as well as the US administration, downplayed the threat and thus failed to stock up on essential supplies, ventilators, and test kits. An early warning system, if implemented, would have caused borders to be shut and early lockdowns. The WHO also delayed its response in sounding the alarm regarding the severity of the outbreak to allow nations globally to prepare for a pandemic. Singapore is a prime example where, despite the WHO not raising concerns and banning travel to and from China, a country banned travelers and took early measures, thus managing the outbreak quite well. South Korea is another example of how things may have played out had those measures by agencies been taken with transparency. Increased transparency would have allowed the healthcare sector to better prepare and reduced the load of patients they had to deal with, helping flatten the curve. The increased patient load and confusion among citizens arising from not following these practices has proved to be a barrier to providing effective treatments to patients with the disease elsewhere in the world.

Lack of Preparedness and Protocols

Despite the previous SARS outbreak teaching us important lessons and providing us with data on a potential outbreak, many nations did not take the important measures needed for a future outbreak. There was no allocation of sufficient funds for such an event. Many countries experienced severe lack of PPE, and the lockdown precautions hampered the logistics of supply and manufacturing of such essential equipment. Singapore and South Korea had protocols in place and were able to implement them at a moment's notice. The spurt of cases that Korea experienced was managed well, providing evidence to this effect. The lack of preparedness and lack of protocol in other nations has resulted in confusion as to how the treatment may be administered safely to the large volume of patients while dealing with diagnostics. Both of these factors have limited the accessibility to healthcare services due to sheer volume.

Socio-Economic Impact

During the SARS epidemic, China faced an economic setback, and experts were unsure if any recovery would be made. However, the global and domestic situation was then in China's favor, as it had a lower debt, allowing it to make a speedy recovery. This is not the case now. Global experts have a pessimistic outlook on the outcome of this outbreak ( 98 ). The fear of COVID-19 disease, lack of proper understanding of the dangers of the virus, and the misinformation spread on the social media ( 99 ) have caused a breakdown of the economic flow globally ( 100 ). An example of this is Indonesia, where a great amount of fear was expressed in responses to a survey when the nation was still free of COVID-19 ( 101 ). The pandemic has resulted in over 2.6 billion people being put under lockdown. This lockdown and the cancellation of the lunar year celebration has affected business at the local level. Hundreds of flights have been canceled, and tourism globally has been affected. Japan and Indonesia are estimated to lose over 2.44 billion dollars due to this ( 102 , 103 ). Workers are not able to work in factories, transportation in all forms is restricted, and goods are not produced or moved. The transport of finished products and raw materials out of China is low. The Economist has published US stock market details indicating that companies in the US that have Chinese roots fell, on average, 5 points on the stock market as compared to the S&P 500 index ( 104 ). Companies such as Starbucks have had to close over 4,000 outlets due to the outbreak as a precaution. Tech and pharma companies are at higher risk since they rely on China for the supply of raw materials and active pharmaceutical ingredients. Paracetamol, for one, has reported a price increase of over 40% in India ( 104 – 106 ). Mass hysteria in the market has caused selling of shares of these companies, causing a tumble in the Indian stock market. Though long-term investors will not be significantly affected, short-term traders will find themselves in soup. Politically, however, this has further bolstered support for world leaders in countries such as India, Germany, and the UK, who are achieving good approval ratings, with citizens being satisfied with the government's approach. In contrast, the ratings of US President Donald Trump have dropped due to the manner in which the COVID-19 pandemic was handled. These minor impacts may be of temporary significance, and the worst and direct impact will be on China itself ( 107 – 109 ), as the looming trade war with the USA had a negative impact on the Chinese and Asian markets. The longer production of goods continues to remain suspended, the more adversely it will affect the Chinese economy and the global markets dependent on it ( 110 ). If this disease is not contained, more and more lockdowns by multiple nations will severely affect the economy and lead to many social complications.

The appearance of the 2019 Novel Coronavirus has added and will continue to add to our understanding of viruses. The pandemic has once again tested the world's preparedness for dealing with such outbreaks. It has provided an outlook on how a massive-scale biological event can cause a socio-economic disturbance through misinformation and social media. In the coming months and years, we can expect to gain further insights into SARS-CoV-2 and COVID-19.

Author Contributions

KN: conceptualization. RK, AA, JM, and KN: investigation. RK and AA: writing—original draft preparation. KN, PN, and JM: writing—review and editing. KN: supervision.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge the contributions made by Dr. Piya Paul Mudgal, Assistant Professor, Manipal Institute of Virology, Manipal Academy of Higher Education towards inputs provided by her during the drafting of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00216/full#supplementary-material

Supplementary Data 1, 2. List of all studies registered for COVID-19 on clinicaltrials.gov .

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Keywords: 2019-nCoV, COVID-19, SARS-CoV-2, coronavirus, pandemic, SARS

Citation: Keni R, Alexander A, Nayak PG, Mudgal J and Nandakumar K (2020) COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. Front. Public Health 8:216. doi: 10.3389/fpubh.2020.00216

Received: 21 February 2020; Accepted: 11 May 2020; Published: 28 May 2020.

Reviewed by:

Copyright © 2020 Keni, Alexander, Nayak, Mudgal and Nandakumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Krishnadas Nandakumar, mailnandakumar77@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Research article
• Open access
• Published: 01 August 2020

The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence

• Elham Monaghesh 1 &
• Alireza Hajizadeh 2  

BMC Public Health volume  20 , Article number:  1193 ( 2020 ) Cite this article

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The outbreak of coronavirus disease-19 (COVID-19) is a public health emergency of international concern. Telehealth is an effective option to fight the outbreak of COVID-19. The aim of this systematic review was to identify the role of telehealth services in preventing, diagnosing, treating, and controlling diseases during COVID-19 outbreak.

This systematic review was conducted through searching five databases including PubMed, Scopus, Embase, Web of Science, and Science Direct. Inclusion criteria included studies clearly defining any use of telehealth services in all aspects of health care during COVID-19 outbreak, published from December 31, 2019, written in English language and published in peer reviewed journals. Two reviewers independently assessed search results, extracted data, and assessed the quality of the included studies. Quality assessment was based on the Critical Appraisal Skills Program (CASP) checklist. Narrative synthesis was undertaken to summarize and report the findings.

Eight studies met the inclusion out of the 142 search results. Currently, healthcare providers and patients who are self-isolating, telehealth is certainly appropriate in minimizing the risk of COVID-19 transmission. This solution has the potential to prevent any sort of direct physical contact, provide continuous care to the community, and finally reduce morbidity and mortality in COVID-19 outbreak.

Conclusions

The use of telehealth improves the provision of health services. Therefore, telehealth should be an important tool in caring services while keeping patients and health providers safe during COVID-19 outbreak.

Peer Review reports

Coronaviruses, a genus of the coronaviridae family, may cause illness in animals or humans [ 1 , 2 ]. In humans, several coronaviruses are known to cause infections of respiratory ranging from the common cold to more serious diseases. The most recently discovered coronavirus causes coronavirus disease-19 (COVID-19) [ 1 ]. The disease originated in Wuhan, China and has kept spreading widely to other regions of the world [ 3 ]. Primitive symptoms of COVID-19 contain fever, dry cough, breathing difficulty, and boredom [ 4 , 5 ]. Elderly people and those with underlying medical problems such as hypertension, heart problems, and diabetes are more susceptible to develop the disease in its form of most intensive [ 1 ]. This universal event has been announced a pandemic by the World Health Organization (WHO) [ 6 ]. A significant factor in slowing down the transmission of the virus is the “social gap” or social distancing that is made possible by the reduction of person-to-person contact [ 7 , 8 ].

To reduce transmission, travel restrictions have been appointed and enforced around the world, and most cities have been quarantined [ 9 ]. However, people who are not infected with the COVID-19, especially those who are at greater risk of developing the disease (e.g. Elderly people and those with underlying diseases), should receive daily care without the risk of exposure to other patients in the hospital [ 7 ]. Moreover, under strict infection control, unnecessary personnel such as clinical psychiatrists strongly refuse to enter COVID-19 patient’s ward [ 10 , 11 ]. Natural disasters and epidemics pose many challenges in providing health care [ 12 ]. As a result, unique and innovative solutions are needed to address both the critical needs of patients with COVID-19 and other people who need healthcare service. In this respect, technological advances provide new options [ 13 ]. Although the ultimate solution for COVID-19 will be multifaceted, it is one of the effective ways to use existing technologies to facilitate optimal service delivery while minimizing the hazard of direct person-to-person exposure [ 7 , 14 ]. The use of telemedicine at the time of epidemic conditions (COVID-19 pandemic) has the potential to improve research of epidemiological, control of disease and management of clinical case [ 7 , 14 , 15 ].

The use of telehealth technology is a twenty-first century approach that is both patient-centered and protects patients, physicians, as well as others [ 16 , 17 ]. Telehealth is the delivery of health care services by health care professionals, where distance is a critical factor, through using information and communication technologies (ICT) for the exchange of valid and correct information [ 18 ]. Telehealth services are renderdusing real-time or store-and-forward techniques [ 19 ]. With the rapid evolution and downsizing of portable electronics, most families have at least one device of digital, such as smartphones [ 20 ] and webcams that provide communication between patient and healthcare provider [ 21 ]. Video conferencing and similar television systems are also used to provide health care programs for people who are hospitalized or in quarantine to reduce the risk of exposure to others and employees [ 7 ]. Physicians who are in quarantine can employ these services to take care of their patients remotely [ 8 , 22 ]. In addition, covering multiple sites with a tele-physician can address some of the challenges of the workforce [ 8 , 23 ].

There are various benefits in using technology of telehealth, especially in non-emergency / routine care and in cases where services do not require direct patient-provider interaction, such as providing psychological services [ 24 ]. Remote care reduces the use of resources in health centers, improves access to care, while minimizing the risk of direct transmission of the infectious agent from person to person [ 25 ]. In addition to being beneficial in keeping people safe, including the general public, patients and health workers, another important advantage is providing widely access to care givers [ 12 ].. Therefore, this technology is an attractive, effectual and affordable option [ 14 , 26 , 27 ]. Patients are eager to use telehealth, but hindrances still exist [ 28 , 29 ]. The barriers of implementing these programs also largely depend on accreditation, payments systems, and insurance [ 8 ]. Furthermore, some physicians are concerned about technical and clinical quality, safety, privacy, and accountability [ 23 , 30 ].

Telehealth can become a basic need for the general population, health care providers, and patients with COVID-19, especially when people are in quarantine, enabling patients in real time through contact with health care provider for advice on their health problems. Thus, the aim of this review was to identify and systematically review the role of telehealth services in preventing, diagnosing, treating, and controlling diseases during COVID-19 outbreak.

Study design

This systematic review was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A method of systematic review was selected to permit a robust and reproducible approach to structure a critical synthesis of the existing and current evidence. Considering the necessity of the matter and limited available evidence on the topic, we did not register the protocol of this systematic review.

Search strategy and data sources

Five online databases, included PubMed, Scopus, Embase, Web of Science and Science Direct, were searched to identify relevant and published studies. The search was conducted on Titles and Abstracts. An elementary search in March 26, 2020 identified a range of available evidence on the role of telehealth services during 2019 novel coronavirus (COVID-19) outbreak. A further search was conducted on April 3, 2020 to update the results. The combination of keywords and Medical Subject Headings (MeSH) were used: COVID19, COVID-19, Coronavirus, Novel coronavirus, 2019-nCoV, Wuhan coronavirus, SARS-CoV-2, SARS2, Tele*, Telemedicine, Tele-medicine, Telehealth, Tele-health, Telecare, Mobile Health, mHealth, Electronic health, and ehealth. To combine terms the Boolean operators (AND, OR and NOT) were used. During this phase, a librarian was consulted to certify that the strategy of search was satisfactory. The search in each database was adapted accordingly. For example, the search strategy in the PubMed database was enforced as follows:

(COVID-19[title/abstract] OR COVID19[title/abstract] OR Coronavirus [title/abstract] OR Novel coronavirus [title/abstract] OR 2019-nCoV [title/abstract] OR Wuhan coronavirus [title/abstract] OR SARS-CoV-2[title/abstract] OR SARS2[title/abstract]) AND (Telemedicine [title/abstract] OR Tele-medicine [title/abstract] OR Telehealth [title/abstract] OR Tele-health [title/abstract] OR Telecare [title/abstract] OR Mobile health [title/abstract] OR mHealth [title/abstract] OR Electronic health [title/abstract] OR eHealth [title/abstract]).

Manual search in web-based resources was accomplished on Google, Google Scholar, journals which published key articles and through searching specific website (WHO, https://www.who.int , Centers for Disease Control and Prevention, https://www.cdc.gov , National Institute for Health and Clinical Excellence, https://www.nice.org.uk , National Health Commission of the People’s Republic of China http://www.nhc.gov and National Administration of Traditional Chinese Medicine http://www.satcm.gov.cn ). In addition, we reviewed the selected articles references in order to identify additional studies or reports not retrieved by the preliminary searches (reference by reference).

Eligibility criteria

All studies with evidence reporting the role of telehealth services in COVID-19 were included in our analyses. In fact, studies were included if they obviously defined function type of telehealth in prevention, diagnosis, management and treatment of COVID-19, published from December 31, 2019 to April 3, 2020, were written in language of English and published in peer reviewed journals. The reason for electing December 31 was due to the fact that this date coincides with the appearance of COVID-19 in Wuhan, Hubei Province, China. Actually, all studies representing any sorts of using tools of telehealth in all aspects of health care (primary, secondary or tertiary level health care) to provide clinical services, diagnosis, assessment of symptoms, triage of patients, consultation services, and training or supervision of clinicians were included. Studies about other technologies (e.g. Internet of Medical Things or IoMT), duplicate publications, review articles, opinion articles, and letters not rendering principal data were excluded, as well as studies reporting incomplete information.

Study selection and data extraction

Two authors (A.H. and E.M.) who performed the literature search also independently followed the application of the inclusion and exclusion criteria and screened the studies based on the titles and abstracts. After initial screening, full-text of studies were obtained and examined to ensure eligibility for the development of the data extraction table.

Data were extracted from all papers which met the eligibility and inclusion criteria for the review. The following data were extracted and analyzed: first author, date of publication, country, design of study, type of used telehealth, key outputs of studies and effects of telehealth.

Quality assessment

To assess the quality of the included studies, the Critical Appraisal Skills Program (CASP) checklists were accorded. To teach people how to critically appraise different types of evidence, the CASP tools were developed [ 31 ]. Included studies were divided into three categories of poor, medium, and good for scoring the quality of them.

Evidence synthesis

For expressing and synthesizing the results of the included studies, narrative synthesis of overall evidence was undertaken by comparing and contrasting the data. Three stages of the narrative synthesis included the development of a preliminary synthesis, exploration of the relationships within and between studies and the determination of the robustness of the synthesis [ 32 ]. Data of the included studies was qualitatively described and presented. The authors to reach consensus on the findings, met frequently to discuss.

Search results

The details on the literature search and processes of screening are illustrate in Fig.  1 . Following the removal of duplicate search records and screening titles and abstracts of studies, we appraised 46 relevant studies in full text. From the studies of remaining, 39 articles did not meet our inclusion criteria and were removed. Finally, one study was added after reference screening (reference by reference) and eight full studies included for stage of evidence synthesis.

PRISMA flow diagram illustrating study selection

Characteristics of the included studies

Some general characteristics of the included studies were demonstrated in Table  1 . The included studies published in various international journals between February 17, 2020 and Apr 9, 2020 were mostly conducted in the USA. Eight included studies were carried out in six countries: USA ( n  = 5), China ( n  = 2), UK ( n  = 2), Canada ( n  = 2), Iran ( n  = 1) and Italy ( n  = 1). Based on the design of study, five studies were cross-sectional, two were case studies and one was case-control. In the included studies, most of telehealth and social media channels were applied during COVID-19 pandemic such as telephone, live video conferencing, and e-mail.

Our systematic review included eight studies that were appraised using the tools of CASP. The qualities of the assessed studies were generally in high level. Six (75%) studies enjoyed good quality, and two (25%) had medium quality. Also, no studies were excluded on the basis of the level of quality appraisal.

Telehealth services during the COVID-19 outbreak

We recognized eight studies that presented precious data on telehealth regarding the status of people infected with COVID-19. Telehealth has the capability to incorporate several organizations and situations of health care into one virtual network, led by the central clinic. This network can contain physical locations in different region: central and remote clinics, prevention centers, private clinics, and, private offices of physicians, centers of rehab state and all registered patients within their locations. By using virtual care for very regular, essential medical care, and deferring elective procedures or yearly checkups, we can free up medical staff and equipment required for those who become seriously ill from COVID-19. Additionally, by not congregating in small spaces like waiting rooms, the ability of the coronavirus to transmission from one person to another were thwart. Keeping people discrete is called “social distancing”. Keeping healthcare staffs discrete from patients and other providers is “medical distancing”. In present time the Telehealth is one strategy to help us carry out this.

Telehealth can mobilize all aspects of healthcare potentials to decrease transmission of disease, conduct people to the right level of health care, ensure safety for provide health services online, protect patients, clinicians, and the community from exposure to infection, and finally diminish the burden on the healthcare providers and health system. Some of the telehealth usage cases for patients were control and triage during the outbreak of COVID-19 pandemic, self and distance monitoring, treatment, patients after discharge in health centers (follow-ups) and implementation of online health services. These methods have the potential to reduce morbidity and mortality during pandemic. For all healthcare workers and clinicians with mild symptoms can still work remotely with patients, facilitate quick access to medical decision making, seek second opinion for severe cases of patients, exchange cross-border experiences, and offer teleradiology and online trainings for health workers. To provide continued access to necessary health services, telehealth should be a key weapon in the fight against the COVID-19outbreak.

The aim of this systematic review was to identify the role of telehealth services in preventing, diagnosing, treating, and controlling diseases during COVID-19 pandemic. In this review, we explained the benefits and implications of several tools of telehealth with the purpose of improving the management of COVID-19 infection. Nowadays, the best preventive strategy is to avoid being exposed to the coronavirus, because there is no vaccine to overcome COVID-19 in the all countries [ 41 ]. A series of strategies have been proposed for infection prevention and control (IPC) that may diminish the exposure risk, such as wearing of face masks in mass population, Covering mouth and nose with tissue when coughing and sneezing, continuous hand washing with soap and water or hand sanitizer containing at least 60% alcohol, avoidance of close contact with others and keeping true social distance, and refraining from touch of unwashed hands with eyes, nose, and mouth [ 42 ].

Meanwhile, to reduce the number of those who receive face-to-face services of health care, healthcare workers can contact with patients through telecommunication tools for triaging, assessing and caring for all patients [ 43 ]. Telehealth with use of live video conferencing or a simple mobile call allow health care professionals to ask special questions and collect required information, triage of patient and supply consultation, or if a person can continue to self-monitor symptoms at home while recovering. It can also be applied for regular check-ins such as respiratory, blood pressure and oxygen level rate needed in home [ 34 ].

During the COVID-19 outbreak in China, online mental health surveys with communication programs, such as Weibo, WeChat and TikTok have enabled mental health professionals and health authorities to render safety mental health services online during the COVID-19 pandemic [ 44 ]. Chinese government officials launched a remote consultation network that can be carried out internet or telephone consultations in a safe setting to ensure the continuous provision of mental health services and reduce the hazard of cross infections [ 45 ]. Also, the National Health Commission of China have published several online guideline and free electronic books about COVID-19 with the aim of helping the progress of Chinese people emergency interventions, safety, improving the quality and effectiveness of emergency interventions [ 10 ]. In addition, telehealth can provide mental online health services in the setting of patient isolation by reducing the mental health burden from COVID-19 and sharing information about symptoms of burnout, depression, and anxiety [ 14 ].

Greenhawt et al. suggested that telehealth has several benefits in providing allergy and immunology services such as limiting exposure of health professionals to potentially infected patients and access to the rapid evaluation for COVID-19 infection [ 38 ]. in addition to the conventional methods used in diagnosing COVID-19, the study identified a novel screening and triage strategy during deadly COVID-19 pandemic in Iran. Services for teleradiology and teleconsultation for triage of COVID-19 infection through a social media massager delivered by Iranian Society of Radiology (ISR) to response to the shortage of on-site thoracic radiologists during COVID-19 pandemic [ 33 ]. In addition to taking actions to protect the health and safety of patients, also staff should take mobile health technology to develop staffing plans and carry out billing for healthcare services [ 39 ].

Our results demonstrate that to manage COVID-19, there are many easy-to- set-up potentials in live video consulting. Live video conferencing can lead to the avoiding of direct physical contact, thereby diminishing the risk of exposure to respiratory secretions and preventing the potential transmission of infection to physicians and other healthcare providers [ 34 ]. Also, live video could be very useful for patients seeking consultation on covid-19, for people with heightened anxiety and instead of in-person visits in cases of chronic disease reviews (such as diabetes and cancer), some medication checks, and triage when telephone is insufficient [ 23 ]. In order to control the spread of the COVID-19 outbreak, video consultations and telephone follow-up is possible in multiple cancer settings including lung, endometrial, colorectal, and prostate [ 37 ].

Based on the study conducted in the USA, phone calls and electronic health records (EHR) can facilitates screening or treating a patient without the need for in-person visits and improve decision making process among healthcare team in an ambulatory and urgent care [ 35 ]. Generally, the impact of telehealth during the COVID-19 pandemic in preventing morbidity and avoiding of presence the public from high-risk areas such as hospital premises was significant. Also, the elderly people can access health services by using electronic devices [ 36 ]. These days, suitable adaptation of local systems with changes regarding to payment and coordination of services are major barriers for the large-scale use of telehealth to deal with COVID-19 infection [ 8 ]. Finally, we hope can make substantial progress in preventing and controlling COVID-19 pandemic through further training of health providers and patients on how to make the most of telehealth tools, revisiting traditional definitions of clinical practice and using closed online platforms.

Future research

The biggest challenge for future research in the use of telehealth is probably defining the obstacles and facilitators in health providers and patients. Future research is suggested to specify the effects of telehealth solutions in the efficiency indicators and hospital performance. Also, further global research is warranted to determine how to set up telehealth in primary care. Researchers can also examine the effectiveness of using telehealth approaches in different health areas, especially in the field of home nursing the elderly who are high-risk people in the community. It is also highly recommended to use this technology in the field of psychiatry as it does not require in-person visits. Other future research can tap into evaluating the satisfaction of patients and providers with telehealth services.

Limitations

Our systematic review holds three limitations. Firstly, it is possible that some relevant studies were not taken into account because they have been published in languages other than English (e.g. Chinese). Secondly, we did not have access to some other databases such as CINAHL and PsycINFO. Thirdly, there could be some other studies on this theme in the literature that skipped our attention and analyses though we did our best to adopt a comprehensive search strategy and cover a broad range of evidence across the world.

This study provides a comprehensive systematic review solely exploring the potentials of telehealth during the COVID-19 pandemic. In response to WHO’s call for studies on the COVID-19 infection and presentation of the most recent evidence published in this early period of the outbreak for health care providers, this study was conducted to identify the role of telehealth during COVID-19 outbreak. As the COVID-19 epidemic scales exponentially across the worlds, calls for expended use of telehealth as innovative solutions, clearly highlight unmet needs in the world healthcare system. Telehealth has the potential to address many of the key challenges in providing health services during the outbreak of COVID-19. Also, telehealth can help us avoid direct physical contact and minimize the risk of COVID transmission and finally provide continuous care to the community. Based on the findings of this review study, clinicians and patients are strongly recommended to apply telehealth tools as an appropriate option to prevent and contain COVID-19 infection.

Availability of data and materials

Datasets are available through the corresponding author upon request.

Abbreviations

Corona virus disease 2019

Critical appraisal skills program

World health organization

Medical subject headings

Internet of medical things

Infection preventive and control

Electronic health record

Persons under investigation

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Monaghesh, E., Hajizadeh, A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health 20 , 1193 (2020). https://doi.org/10.1186/s12889-020-09301-4

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I Thought We’d Learned Nothing From the Pandemic. I Wasn’t Seeing the Full Picture

M y first home had a back door that opened to a concrete patio with a giant crack down the middle. When my sister and I played, I made sure to stay on the same side of the divide as her, just in case. The 1988 film The Land Before Time was one of the first movies I ever saw, and the image of the earth splintering into pieces planted its roots in my brain. I believed that, even in my own backyard, I could easily become the tiny Triceratops separated from her family, on the other side of the chasm, as everything crumbled into chaos.

Some 30 years later, I marvel at the eerie, unexpected ways that cartoonish nightmare came to life – not just for me and my family, but for all of us. The landscape was already covered in fissures well before COVID-19 made its way across the planet, but the pandemic applied pressure, and the cracks broke wide open, separating us from each other physically and ideologically. Under the weight of the crisis, we scattered and landed on such different patches of earth we could barely see each other’s faces, even when we squinted. We disagreed viciously with each other, about how to respond, but also about what was true.

Recently, someone asked me if we’ve learned anything from the pandemic, and my first thought was a flat no. Nothing. There was a time when I thought it would be the very thing to draw us together and catapult us – as a capital “S” Society – into a kinder future. It’s surreal to remember those early days when people rallied together, sewing masks for health care workers during critical shortages and gathering on balconies in cities from Dallas to New York City to clap and sing songs like “Yellow Submarine.” It felt like a giant lightning bolt shot across the sky, and for one breath, we all saw something that had been hidden in the dark – the inherent vulnerability in being human or maybe our inescapable connectedness .

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But it turns out, it was just a flash. The goodwill vanished as quickly as it appeared. A couple of years later, people feel lied to, abandoned, and all on their own. I’ve felt my own curiosity shrinking, my willingness to reach out waning , my ability to keep my hands open dwindling. I look out across the landscape and see selfishness and rage, burnt earth and so many dead bodies. Game over. We lost. And if we’ve already lost, why try?

Still, the question kept nagging me. I wondered, am I seeing the full picture? What happens when we focus not on the collective society but at one face, one story at a time? I’m not asking for a bow to minimize the suffering – a pretty flourish to put on top and make the whole thing “worth it.” Yuck. That’s not what we need. But I wondered about deep, quiet growth. The kind we feel in our bodies, relationships, homes, places of work, neighborhoods.

Like a walkie-talkie message sent to my allies on the ground, I posted a call on my Instagram. What do you see? What do you hear? What feels possible? Is there life out here? Sprouting up among the rubble? I heard human voices calling back – reports of life, personal and specific. I heard one story at a time – stories of grief and distrust, fury and disappointment. Also gratitude. Discovery. Determination.

Among the most prevalent were the stories of self-revelation. Almost as if machines were given the chance to live as humans, people described blossoming into fuller selves. They listened to their bodies’ cues, recognized their desires and comforts, tuned into their gut instincts, and honored the intuition they hadn’t realized belonged to them. Alex, a writer and fellow disabled parent, found the freedom to explore a fuller version of herself in the privacy the pandemic provided. “The way I dress, the way I love, and the way I carry myself have both shrunk and expanded,” she shared. “I don’t love myself very well with an audience.” Without the daily ritual of trying to pass as “normal” in public, Tamar, a queer mom in the Netherlands, realized she’s autistic. “I think the pandemic helped me to recognize the mask,” she wrote. “Not that unmasking is easy now. But at least I know it’s there.” In a time of widespread suffering that none of us could solve on our own, many tended to our internal wounds and misalignments, large and small, and found clarity.

Read More: A Tool for Staying Grounded in This Era of Constant Uncertainty

I wonder if this flourishing of self-awareness is at least partially responsible for the life alterations people pursued. The pandemic broke open our personal notions of work and pushed us to reevaluate things like time and money. Lucy, a disabled writer in the U.K., made the hard decision to leave her job as a journalist covering Westminster to write freelance about her beloved disability community. “This work feels important in a way nothing else has ever felt,” she wrote. “I don’t think I’d have realized this was what I should be doing without the pandemic.” And she wasn’t alone – many people changed jobs , moved, learned new skills and hobbies, became politically engaged.

Perhaps more than any other shifts, people described a significant reassessment of their relationships. They set boundaries, said no, had challenging conversations. They also reconnected, fell in love, and learned to trust. Jeanne, a quilter in Indiana, got to know relatives she wouldn’t have connected with if lockdowns hadn’t prompted weekly family Zooms. “We are all over the map as regards to our belief systems,” she emphasized, “but it is possible to love people you don’t see eye to eye with on every issue.” Anna, an anti-violence advocate in Maine, learned she could trust her new marriage: “Life was not a honeymoon. But we still chose to turn to each other with kindness and curiosity.” So many bonds forged and broken, strengthened and strained.

Instead of relying on default relationships or institutional structures, widespread recalibrations allowed for going off script and fortifying smaller communities. Mara from Idyllwild, Calif., described the tangible plan for care enacted in her town. “We started a mutual-aid group at the beginning of the pandemic,” she wrote, “and it grew so quickly before we knew it we were feeding 400 of the 4000 residents.” She didn’t pretend the conditions were ideal. In fact, she expressed immense frustration with our collective response to the pandemic. Even so, the local group rallied and continues to offer assistance to their community with help from donations and volunteers (many of whom were originally on the receiving end of support). “I’ve learned that people thrive when they feel their connection to others,” she wrote. Clare, a teacher from the U.K., voiced similar conviction as she described a giant scarf she’s woven out of ribbons, each representing a single person. The scarf is “a collection of stories, moments and wisdom we are sharing with each other,” she wrote. It now stretches well over 1,000 feet.

A few hours into reading the comments, I lay back on my bed, phone held against my chest. The room was quiet, but my internal world was lighting up with firefly flickers. What felt different? Surely part of it was receiving personal accounts of deep-rooted growth. And also, there was something to the mere act of asking and listening. Maybe it connected me to humans before battle cries. Maybe it was the chance to be in conversation with others who were also trying to understand – what is happening to us? Underneath it all, an undeniable thread remained; I saw people peering into the mess and narrating their findings onto the shared frequency. Every comment was like a flare into the sky. I’m here! And if the sky is full of flares, we aren’t alone.

I recognized my own pandemic discoveries – some minor, others massive. Like washing off thick eyeliner and mascara every night is more effort than it’s worth; I can transform the mundane into the magical with a bedsheet, a movie projector, and twinkle lights; my paralyzed body can mother an infant in ways I’d never seen modeled for me. I remembered disappointing, bewildering conversations within my own family of origin and our imperfect attempts to remain close while also seeing things so differently. I realized that every time I get the weekly invite to my virtual “Find the Mumsies” call, with a tiny group of moms living hundreds of miles apart, I’m being welcomed into a pocket of unexpected community. Even though we’ve never been in one room all together, I’ve felt an uncommon kind of solace in their now-familiar faces.

Hope is a slippery thing. I desperately want to hold onto it, but everywhere I look there are real, weighty reasons to despair. The pandemic marks a stretch on the timeline that tangles with a teetering democracy, a deteriorating planet , the loss of human rights that once felt unshakable . When the world is falling apart Land Before Time style, it can feel trite, sniffing out the beauty – useless, firing off flares to anyone looking for signs of life. But, while I’m under no delusions that if we just keep trudging forward we’ll find our own oasis of waterfalls and grassy meadows glistening in the sunshine beneath a heavenly chorus, I wonder if trivializing small acts of beauty, connection, and hope actually cuts us off from resources essential to our survival. The group of abandoned dinosaurs were keeping each other alive and making each other laugh well before they made it to their fantasy ending.

Read More: How Ice Cream Became My Own Personal Act of Resistance

After the monarch butterfly went on the endangered-species list, my friend and fellow writer Hannah Soyer sent me wildflower seeds to plant in my yard. A simple act of big hope – that I will actually plant them, that they will grow, that a monarch butterfly will receive nourishment from whatever blossoms are able to push their way through the dirt. There are so many ways that could fail. But maybe the outcome wasn’t exactly the point. Maybe hope is the dogged insistence – the stubborn defiance – to continue cultivating moments of beauty regardless. There is value in the planting apart from the harvest.

I can’t point out a single collective lesson from the pandemic. It’s hard to see any great “we.” Still, I see the faces in my moms’ group, making pancakes for their kids and popping on between strings of meetings while we try to figure out how to raise these small people in this chaotic world. I think of my friends on Instagram tending to the selves they discovered when no one was watching and the scarf of ribbons stretching the length of more than three football fields. I remember my family of three, holding hands on the way up the ramp to the library. These bits of growth and rings of support might not be loud or right on the surface, but that’s not the same thing as nothing. If we only cared about the bottom-line defeats or sweeping successes of the big picture, we’d never plant flowers at all.

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• http://orcid.org/0000-0003-1512-4471 Emily Long 1 ,
• Susan Patterson 1 ,
• Karen Maxwell 1 ,
• Carolyn Blake 1 ,
• http://orcid.org/0000-0001-7342-4566 Raquel Bosó Pérez 1 ,
• Ruth Lewis 1 ,
• Mark McCann 1 ,
• Julie Riddell 1 ,
• Kathryn Skivington 1 ,
• Rachel Wilson-Lowe 1 ,
• http://orcid.org/0000-0002-4409-6601 Kirstin R Mitchell 2
• 1 MRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow , UK
• 2 MRC/CSO Social and Public Health Sciences Unit, Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
• Correspondence to Dr Emily Long, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G3 7HR, UK; emily.long{at}glasgow.ac.uk

This essay examines key aspects of social relationships that were disrupted by the COVID-19 pandemic. It focuses explicitly on relational mechanisms of health and brings together theory and emerging evidence on the effects of the COVID-19 pandemic to make recommendations for future public health policy and recovery. We first provide an overview of the pandemic in the UK context, outlining the nature of the public health response. We then introduce four distinct domains of social relationships: social networks, social support, social interaction and intimacy, highlighting the mechanisms through which the pandemic and associated public health response drastically altered social interactions in each domain. Throughout the essay, the lens of health inequalities, and perspective of relationships as interconnecting elements in a broader system, is used to explore the varying impact of these disruptions. The essay concludes by providing recommendations for longer term recovery ensuring that the social relational cost of COVID-19 is adequately considered in efforts to rebuild.

• inequalities

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Data sharing not applicable as no data sets generated or analysed for this essay.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/jech-2021-216690

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Introduction

Infectious disease pandemics, including SARS and COVID-19, demand intrapersonal behaviour change and present highly complex challenges for public health. 1 A pandemic of an airborne infection, spread easily through social contact, assails human relationships by drastically altering the ways through which humans interact. In this essay, we draw on theories of social relationships to examine specific ways in which relational mechanisms key to health and well-being were disrupted by the COVID-19 pandemic. Relational mechanisms refer to the processes between people that lead to change in health outcomes.

At the time of writing, the future surrounding COVID-19 was uncertain. Vaccine programmes were being rolled out in countries that could afford them, but new and more contagious variants of the virus were also being discovered. The recovery journey looked long, with continued disruption to social relationships. The social cost of COVID-19 was only just beginning to emerge, but the mental health impact was already considerable, 2 3 and the inequality of the health burden stark. 4 Knowledge of the epidemiology of COVID-19 accrued rapidly, but evidence of the most effective policy responses remained uncertain.

The initial response to COVID-19 in the UK was reactive and aimed at reducing mortality, with little time to consider the social implications, including for interpersonal and community relationships. The terminology of ‘social distancing’ quickly became entrenched both in public and policy discourse. This equation of physical distance with social distance was regrettable, since only physical proximity causes viral transmission, whereas many forms of social proximity (eg, conversations while walking outdoors) are minimal risk, and are crucial to maintaining relationships supportive of health and well-being.

The aim of this essay is to explore four key relational mechanisms that were impacted by the pandemic and associated restrictions: social networks, social support, social interaction and intimacy. We use relational theories and emerging research on the effects of the COVID-19 pandemic response to make three key recommendations: one regarding public health responses; and two regarding social recovery. Our understanding of these mechanisms stems from a ‘systems’ perspective which casts social relationships as interdependent elements within a connected whole. 5

Social networks

Social networks characterise the individuals and social connections that compose a system (such as a workplace, community or society). Social relationships range from spouses and partners, to coworkers, friends and acquaintances. They vary across many dimensions, including, for example, frequency of contact and emotional closeness. Social networks can be understood both in terms of the individuals and relationships that compose the network, as well as the overall network structure (eg, how many of your friends know each other).

Social networks show a tendency towards homophily, or a phenomenon of associating with individuals who are similar to self. 6 This is particularly true for ‘core’ network ties (eg, close friends), while more distant, sometimes called ‘weak’ ties tend to show more diversity. During the height of COVID-19 restrictions, face-to-face interactions were often reduced to core network members, such as partners, family members or, potentially, live-in roommates; some ‘weak’ ties were lost, and interactions became more limited to those closest. Given that peripheral, weaker social ties provide a diversity of resources, opinions and support, 7 COVID-19 likely resulted in networks that were smaller and more homogenous.

Such changes were not inevitable nor necessarily enduring, since social networks are also adaptive and responsive to change, in that a disruption to usual ways of interacting can be replaced by new ways of engaging (eg, Zoom). Yet, important inequalities exist, wherein networks and individual relationships within networks are not equally able to adapt to such changes. For example, individuals with a large number of newly established relationships (eg, university students) may have struggled to transfer these relationships online, resulting in lost contacts and a heightened risk of social isolation. This is consistent with research suggesting that young adults were the most likely to report a worsening of relationships during COVID-19, whereas older adults were the least likely to report a change. 8

Lastly, social connections give rise to emergent properties of social systems, 9 where a community-level phenomenon develops that cannot be attributed to any one member or portion of the network. For example, local area-based networks emerged due to geographic restrictions (eg, stay-at-home orders), resulting in increases in neighbourly support and local volunteering. 10 In fact, research suggests that relationships with neighbours displayed the largest net gain in ratings of relationship quality compared with a range of relationship types (eg, partner, colleague, friend). 8 Much of this was built from spontaneous individual interactions within local communities, which together contributed to the ‘community spirit’ that many experienced. 11 COVID-19 restrictions thus impacted the personal social networks and the structure of the larger networks within the society.

Social support

Social support, referring to the psychological and material resources provided through social interaction, is a critical mechanism through which social relationships benefit health. In fact, social support has been shown to be one of the most important resilience factors in the aftermath of stressful events. 12 In the context of COVID-19, the usual ways in which individuals interact and obtain social support have been severely disrupted.

One such disruption has been to opportunities for spontaneous social interactions. For example, conversations with colleagues in a break room offer an opportunity for socialising beyond one’s core social network, and these peripheral conversations can provide a form of social support. 13 14 A chance conversation may lead to advice helpful to coping with situations or seeking formal help. Thus, the absence of these spontaneous interactions may mean the reduction of indirect support-seeking opportunities. While direct support-seeking behaviour is more effective at eliciting support, it also requires significantly more effort and may be perceived as forceful and burdensome. 15 The shift to homeworking and closure of community venues reduced the number of opportunities for these spontaneous interactions to occur, and has, second, focused them locally. Consequently, individuals whose core networks are located elsewhere, or who live in communities where spontaneous interaction is less likely, have less opportunity to benefit from spontaneous in-person supportive interactions.

However, alongside this disruption, new opportunities to interact and obtain social support have arisen. The surge in community social support during the initial lockdown mirrored that often seen in response to adverse events (eg, natural disasters 16 ). COVID-19 restrictions that confined individuals to their local area also compelled them to focus their in-person efforts locally. Commentators on the initial lockdown in the UK remarked on extraordinary acts of generosity between individuals who belonged to the same community but were unknown to each other. However, research on adverse events also tells us that such community support is not necessarily maintained in the longer term. 16

Meanwhile, online forms of social support are not bound by geography, thus enabling interactions and social support to be received from a wider network of people. Formal online social support spaces (eg, support groups) existed well before COVID-19, but have vastly increased since. While online interactions can increase perceived social support, it is unclear whether remote communication technologies provide an effective substitute from in-person interaction during periods of social distancing. 17 18 It makes intuitive sense that the usefulness of online social support will vary by the type of support offered, degree of social interaction and ‘online communication skills’ of those taking part. Youth workers, for instance, have struggled to keep vulnerable youth engaged in online youth clubs, 19 despite others finding a positive association between amount of digital technology used by individuals during lockdown and perceived social support. 20 Other research has found that more frequent face-to-face contact and phone/video contact both related to lower levels of depression during the time period of March to August 2020, but the negative effect of a lack of contact was greater for those with higher levels of usual sociability. 21 Relatedly, important inequalities in social support exist, such that individuals who occupy more socially disadvantaged positions in society (eg, low socioeconomic status, older people) tend to have less access to social support, 22 potentially exacerbated by COVID-19.

Social and interactional norms

Interactional norms are key relational mechanisms which build trust, belonging and identity within and across groups in a system. Individuals in groups and societies apply meaning by ‘approving, arranging and redefining’ symbols of interaction. 23 A handshake, for instance, is a powerful symbol of trust and equality. Depending on context, not shaking hands may symbolise a failure to extend friendship, or a failure to reach agreement. The norms governing these symbols represent shared values and identity; and mutual understanding of these symbols enables individuals to achieve orderly interactions, establish supportive relationship accountability and connect socially. 24 25

Physical distancing measures to contain the spread of COVID-19 radically altered these norms of interaction, particularly those used to convey trust, affinity, empathy and respect (eg, hugging, physical comforting). 26 As epidemic waves rose and fell, the work to negotiate these norms required intense cognitive effort; previously taken-for-granted interactions were re-examined, factoring in current restriction levels, own and (assumed) others’ vulnerability and tolerance of risk. This created awkwardness, and uncertainty, for example, around how to bring closure to an in-person interaction or convey warmth. The instability in scripted ways of interacting created particular strain for individuals who already struggled to encode and decode interactions with others (eg, those who are deaf or have autism spectrum disorder); difficulties often intensified by mask wearing. 27

Large social gatherings—for example, weddings, school assemblies, sporting events—also present key opportunities for affirming and assimilating interactional norms, building cohesion and shared identity and facilitating cooperation across social groups. 28 Online ‘equivalents’ do not easily support ‘social-bonding’ activities such as singing and dancing, and rarely enable chance/spontaneous one-on-one conversations with peripheral/weaker network ties (see the Social networks section) which can help strengthen bonds across a larger network. The loss of large gatherings to celebrate rites of passage (eg, bar mitzvah, weddings) has additional relational costs since these events are performed by and for communities to reinforce belonging, and to assist in transitioning to new phases of life. 29 The loss of interaction with diverse others via community and large group gatherings also reduces intergroup contact, which may then tend towards more prejudiced outgroup attitudes. While online interaction can go some way to mimicking these interaction norms, there are key differences. A sense of anonymity, and lack of in-person emotional cues, tends to support norms of polarisation and aggression in expressing differences of opinion online. And while online platforms have potential to provide intergroup contact, the tendency of much social media to form homogeneous ‘echo chambers’ can serve to further reduce intergroup contact. 30 31

Intimacy relates to the feeling of emotional connection and closeness with other human beings. Emotional connection, through romantic, friendship or familial relationships, fulfils a basic human need 32 and strongly benefits health, including reduced stress levels, improved mental health, lowered blood pressure and reduced risk of heart disease. 32 33 Intimacy can be fostered through familiarity, feeling understood and feeling accepted by close others. 34

Intimacy via companionship and closeness is fundamental to mental well-being. Positively, the COVID-19 pandemic has offered opportunities for individuals to (re)connect and (re)strengthen close relationships within their household via quality time together, following closure of many usual external social activities. Research suggests that the first full UK lockdown period led to a net gain in the quality of steady relationships at a population level, 35 but amplified existing inequalities in relationship quality. 35 36 For some in single-person households, the absence of a companion became more conspicuous, leading to feelings of loneliness and lower mental well-being. 37 38 Additional pandemic-related relational strain 39 40 resulted, for some, in the initiation or intensification of domestic abuse. 41 42

Physical touch is another key aspect of intimacy, a fundamental human need crucial in maintaining and developing intimacy within close relationships. 34 Restrictions on social interactions severely restricted the number and range of people with whom physical affection was possible. The reduction in opportunity to give and receive affectionate physical touch was not experienced equally. Many of those living alone found themselves completely without physical contact for extended periods. The deprivation of physical touch is evidenced to take a heavy emotional toll. 43 Even in future, once physical expressions of affection can resume, new levels of anxiety over germs may introduce hesitancy into previously fluent blending of physical and verbal intimate social connections. 44

The pandemic also led to shifts in practices and norms around sexual relationship building and maintenance, as individuals adapted and sought alternative ways of enacting sexual intimacy. This too is important, given that intimate sexual activity has known benefits for health. 45 46 Given that social restrictions hinged on reducing household mixing, possibilities for partnered sexual activity were primarily guided by living arrangements. While those in cohabiting relationships could potentially continue as before, those who were single or in non-cohabiting relationships generally had restricted opportunities to maintain their sexual relationships. Pornography consumption and digital partners were reported to increase since lockdown. 47 However, online interactions are qualitatively different from in-person interactions and do not provide the same opportunities for physical intimacy.

Recommendations and conclusions

In the sections above we have outlined the ways in which COVID-19 has impacted social relationships, showing how relational mechanisms key to health have been undermined. While some of the damage might well self-repair after the pandemic, there are opportunities inherent in deliberative efforts to build back in ways that facilitate greater resilience in social and community relationships. We conclude by making three recommendations: one regarding public health responses to the pandemic; and two regarding social recovery.

Recommendation 1: explicitly count the relational cost of public health policies to control the pandemic

Effective handling of a pandemic recognises that social, economic and health concerns are intricately interwoven. It is clear that future research and policy attention must focus on the social consequences. As described above, policies which restrict physical mixing across households carry heavy and unequal relational costs. These include for individuals (eg, loss of intimate touch), dyads (eg, loss of warmth, comfort), networks (eg, restricted access to support) and communities (eg, loss of cohesion and identity). Such costs—and their unequal impact—should not be ignored in short-term efforts to control an epidemic. Some public health responses—restrictions on international holiday travel and highly efficient test and trace systems—have relatively small relational costs and should be prioritised. At a national level, an earlier move to proportionate restrictions, and investment in effective test and trace systems, may help prevent escalation of spread to the point where a national lockdown or tight restrictions became an inevitability. Where policies with relational costs are unavoidable, close attention should be paid to the unequal relational impact for those whose personal circumstances differ from normative assumptions of two adult families. This includes consideration of whether expectations are fair (eg, for those who live alone), whether restrictions on social events are equitable across age group, religious/ethnic groupings and social class, and also to ensure that the language promoted by such policies (eg, households; families) is not exclusionary. 48 49 Forethought to unequal impacts on social relationships should thus be integral to the work of epidemic preparedness teams.

Recommendation 2: intelligently balance online and offline ways of relating

A key ingredient for well-being is ‘getting together’ in a physical sense. This is fundamental to a human need for intimate touch, physical comfort, reinforcing interactional norms and providing practical support. Emerging evidence suggests that online ways of relating cannot simply replace physical interactions. But online interaction has many benefits and for some it offers connections that did not exist previously. In particular, online platforms provide new forms of support for those unable to access offline services because of mobility issues (eg, older people) or because they are geographically isolated from their support community (eg, lesbian, gay, bisexual, transgender and queer (LGBTQ) youth). Ultimately, multiple forms of online and offline social interactions are required to meet the needs of varying groups of people (eg, LGBTQ, older people). Future research and practice should aim to establish ways of using offline and online support in complementary and even synergistic ways, rather than veering between them as social restrictions expand and contract. Intelligent balancing of online and offline ways of relating also pertains to future policies on home and flexible working. A decision to switch to wholesale or obligatory homeworking should consider the risk to relational ‘group properties’ of the workplace community and their impact on employees’ well-being, focusing in particular on unequal impacts (eg, new vs established employees). Intelligent blending of online and in-person working is required to achieve flexibility while also nurturing supportive networks at work. Intelligent balance also implies strategies to build digital literacy and minimise digital exclusion, as well as coproducing solutions with intended beneficiaries.

Recommendation 3: build stronger and sustainable localised communities

In balancing offline and online ways of interacting, there is opportunity to capitalise on the potential for more localised, coherent communities due to scaled-down travel, homeworking and local focus that will ideally continue after restrictions end. There are potential economic benefits after the pandemic, such as increased trade as home workers use local resources (eg, coffee shops), but also relational benefits from stronger relationships around the orbit of the home and neighbourhood. Experience from previous crises shows that community volunteer efforts generated early on will wane over time in the absence of deliberate work to maintain them. Adequately funded partnerships between local government, third sector and community groups are required to sustain community assets that began as a direct response to the pandemic. Such partnerships could work to secure green spaces and indoor (non-commercial) meeting spaces that promote community interaction. Green spaces in particular provide a triple benefit in encouraging physical activity and mental health, as well as facilitating social bonding. 50 In building local communities, small community networks—that allow for diversity and break down ingroup/outgroup views—may be more helpful than the concept of ‘support bubbles’, which are exclusionary and less sustainable in the longer term. Rigorously designed intervention and evaluation—taking a systems approach—will be crucial in ensuring scale-up and sustainability.

The dramatic change to social interaction necessitated by efforts to control the spread of COVID-19 created stark challenges but also opportunities. Our essay highlights opportunities for learning, both to ensure the equity and humanity of physical restrictions, and to sustain the salutogenic effects of social relationships going forward. The starting point for capitalising on this learning is recognition of the disruption to relational mechanisms as a key part of the socioeconomic and health impact of the pandemic. In recovery planning, a general rule is that what is good for decreasing health inequalities (such as expanding social protection and public services and pursuing green inclusive growth strategies) 4 will also benefit relationships and safeguard relational mechanisms for future generations. Putting this into action will require political will.

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Not required.

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Twitter @karenmaxSPHSU, @Mark_McCann, @Rwilsonlowe, @KMitchinGlasgow

Contributors EL and KM led on the manuscript conceptualisation, review and editing. SP, KM, CB, RBP, RL, MM, JR, KS and RW-L contributed to drafting and revising the article. All authors assisted in revising the final draft.

Funding The research reported in this publication was supported by the Medical Research Council (MC_UU_00022/1, MC_UU_00022/3) and the Chief Scientist Office (SPHSU11, SPHSU14). EL is also supported by MRC Skills Development Fellowship Award (MR/S015078/1). KS and MM are also supported by a Medical Research Council Strategic Award (MC_PC_13027).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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The COVID-19 pandemic: a global health crisis

Casey a. pollard.

1 Department of Surgery, The University of Toledo, College of Medicine and Life Sciences, Toledo, Ohio

Michael P. Morran

2 The University of Toledo Advanced Microscopy and Imaging Center, The University of Toledo, College of Medicine and Life Sciences, Toledo, Ohio

Andrea L. Nestor-Kalinoski

The novel coronavirus SARS-CoV-2 was identified as the causative agent for a series of atypical respiratory diseases in the Hubei Province of Wuhan, China in December of 2019. The disease SARS-CoV-2, termed COVID-19, was officially declared a pandemic by the World Health Organization on March 11, 2020. SARS-CoV-2 contains a single-stranded, positive-sense RNA genome surrounded by an extracellular membrane containing a series of spike glycoproteins resembling a crown. COVID-19 infection results in diverse symptoms and morbidity depending on individual genetics, ethnicity, age, and geographic location. In severe cases, COVID-19 pathophysiology includes destruction of lung epithelial cells, thrombosis, hypercoagulation, and vascular leak leading to sepsis. These events lead to acute respiratory distress syndrome (ARDS) and subsequent pulmonary fibrosis in patients. COVID-19 risk factors include cardiovascular disease, hypertension, and diabetes, which are highly prevalent in the United States. This population has upregulation of the angiotensin converting enzyme-2 (ACE2) receptor, which is exploited by COVID-19 as the route of entry and infection. Viral envelope proteins bind to and degrade ACE2 receptors, thus preventing normal ACE2 function. COVID-19 infection causes imbalances in ACE2 and induces an inflammatory immune response, known as a cytokine storm, both of which amplify comorbidities within the host. Herein, we discuss the genetics, pathogenesis, and possible therapeutics of COVID-19 infection along with secondary complications associated with disease progression, including ARDS and pulmonary fibrosis. Understanding the mechanisms of COVID-19 infection will allow the development of vaccines or other novel therapeutic approaches to prevent transmission or reduce the severity of infection.

CORONAVIRUSES AND SARS-C o V-2 GENETICS

Coronaviruses are a well-studied group of viruses in the Coronaviridae family that are known for their ability to infect a variety of hosts due to their capacity to evolve in epidemiological situations, including crossing species barriers, mutagenesis, tissue tropism, and pathogenicity ( 10b , 14 , 83 ). Coronaviruses are round, enveloped virions roughly 80–220 nm in diameter that contain a single-stranded, positive-sense RNA genome of ∼26–32 kb surrounded by an extracellular membrane containing a casing of spike glycoproteins ( 32 , 80 ). The term corona in Latin translates to crown and was given to these viruses due to the presence of the spike casing that resembled a “crown-like structure” using electron microscopy ( 37 ).

Coronaviruses have been implicated in human disease as early as the late 1960s, where they were identified as the causative agents in respiratory illnesses that presented with mild symptoms associated with the common cold ( 32 ). Seven strains of human coronaviruses have been characterized, four of which are known to infect the upper respiratory tract and cause mild symptoms, while the three others are known for their severe disease-causing characteristics of the lower respiratory tract including the following: SARS-CoV (severe acute respiratory syndrome), MERS-CoV (Middle East respiratory syndrome), and SARS-CoV-2 (COVID-19) ( 42 ). Since the emergence of the COVID-19 pandemic, data-sharing initiatives have led to the much needed generation of SARS-CoV-2 data, including complete reference genomes in the National Center for Biotechnology Information database ( {"type":"entrez-nucleotide","attrs":{"text":"NC_045512","term_id":"1798174254","term_text":"NC_045512"}} NC_045512 .2), which contains the 29,903 bp genomic sequence ( 83 ).

While it is known that the RNA polymerase of viruses lack proofreading capacity, the ensuing result is a high mutation rate with low replicative fidelity. In contrast, the coronaviruses possess an exonuclease proofreading capability that has resulted in the expansion and maintenance of one of the largest known viral genomes at ∼30 kb ( 17 , 60 ). The large viral genome of SARS-CoV-2 codes for four structural proteins including the envelope, membrane, nucleocapsid, and spike glycoprotein, which play a role in both molecular characterization and host cell entry ( 23 , 35 ). The SARS-CoV-2 genome also includes 16 nonstructural proteins and 9 accessory proteins required for replication and pathogenesis ( 23 , 35 , 60 ). While SARS-CoV-2 and SARS-CoV are 75–80% identical ( 3 , 89 ), SARS-CoV-2 displays the highest sequence similarities with BatCoV at 96.2% ( 11 ). Global sequence comparison of SARS-CoV-2 isolates have expanded the literature and information known for this virus in a short period of time. Initial analysis of roughly 100 genomes of SARS-CoV-2 identified two major subtypes, designated L and S, which vary due to the presence of two linked single nucleotide polymorphisms ( 71 ). Interestingly, the L subtype is a derivative of the S type and was identified in ∼70% of the genomes compared with the S type in the remaining 30% ( 71 ). Phylogenic tree analysis of the L type suggests that the differences are related to a significantly higher mutation rate, which, consequently, results in higher transmission and/or replication rates ( 71 ). Furthermore, the SARS-CoV-2 virus has geographically diverse strains that seemingly vary in severity, mortality rate, and treatment options that were characterized using phylogenetic network analysis of 160 SARS-CoV-2 genomes ( 18 ). Three distinct viral clusters (A, B, and C) were identified with derivative subgroups, with cluster A sharing the closest similarity to the BatCoV genome. Clusters A and C are found predominantly in the Americas and Europe, while cluster C is found across East Asia ( 18 ).

INDIVIDUAL GENETIC PREDISPOSITION/SUSCEPTIBILITY

Throughout the progression of the COVID-19 pandemic, it is clear that not all infected patients are created equal. The diversity in symptoms, morbidity, genetics, age, and geographic location all play distinct roles in viral transmission. Understanding the genetic implications underlying severe COVID-19 infection requires complex biochemical and immunological studies. Previously identified immune-related genetic variants known to be associated with susceptibility to SARS-CoV ( 61 , 85 ), including mannose-binding lectin, basigin (CD147), C-C motif chemokine ligand 2 (CCL2), interleukin-12 and human leukocyte antigen (HLA) genes, might show promise due to the shared homology of the two viral genomes ( 41 , 69 , 73 , 78 ). Utilizing our current understanding of viral entry and pathophysiology in relation to viral infection has prompted research focused on host genetic factors that may help to mitigate differences in viral replication and the innate and adaptive immune responses triggered during viral infection ( 75 ). While angiotensin-converting enzyme-2 (ACE2) receptor expression seems promising as a genetic element that could relate to immunity, no polymorphisms or mutations in ACE2 related to spike protein binding resistance have been reported in populations ( 8 ). Although rare, ACE2 variants have been identified that alter the interaction between host cells and SARS-CoV-2 causing reduced affinity of SARS-CoV-2 binding ( 66 ). Along this same line of reasoning, the gene encoding the transmembrane serine protease 2 (TMPRSS2) protease responsible for spike protein priming for viral entry has received much attention. Cell lines expressing high amounts of TMPRSS2 are highly susceptible to SARS-CoV-2 infection ( 43 ). In addition, it is known that TMPRSS2 has 2 isoforms 1 with and 1 without a 37 amino acid long cytoplasmic tail, which is thought to interact with viral spike proteins and promote viral spreading within the host ( 90 ).

Monoclonal antibodies against the spike protein of COVID-19 could play a pivotal role in blocking the virus attachment, fusion, and entry into host cells ( 67 , 72 ). Antibodies against the receptor-binding domain (RBD) of the spike protein or antibodies that bind to the ACE2 receptor have been discussed as potential therapeutics ( 67 , 72 ). Furthermore, recombinant RBD proteins have been shown to strongly bind to the ACE2 receptor in human and bat cells ( 67 ). There are also studies targeting glycocalyx loss as a therapeutic target of the spike protein. Importantly, blocking these initial steps in viral entry and replication could block the downstream cascade of COVID-19 pathophysiology. This would effectively decrease the morality rate of the current pandemic as it reduces the viral load in patients. Additionally, these antibodies could be potential candidates for COVID-19 antiviral and vaccine development ( 67 ). However, this therapeutic method would have very little impact on the case rate or the infectious propensity of the virus.

In addition, genetic alterations in immune response elements will be important in identifying possible gene candidates that could control host inflammatory responses that elicit the cytokine storm to help reduce secondary complications of infection by altering expression and activity of cytokines like IL-1, IL-6, interferons, and others ( 10 ). HLA is known to be one of the most polymorphic antigen systems in the body. In silico studies point out that all known HLA genotypes A, B, and C have affinity to bind SARS-CoV-2 peptides ( 50 ). Furthermore, predictive alleles have been found to have a binding capability that can infer susceptibility or possibly impart some T-cell-based immune response ( 50 ). Further studies have reviewed the genetic association of COVID-19 infection based on blood type ( 47 ) and sex, with the number of X chromosomes having an effect on susceptibility and progression of infection ( 20 ).

COVID-19 PATHOPHYSIOLOGY

The novel coronavirus SARS-CoV-2 was originally identified as the causative agent for a series of atypical respiratory diseases in the Hubei Province of Wuhan, China in December of 2019. The disease SARS-CoV-2, which will be termed COVID-19 from herein, was officially declared a pandemic by the World Health Organization (WHO) on March 11, 2020 ( 82b ). According to the WHO, there are 28,637,952 positive COVID-19 cases and 917,417 deaths worldwide as of September 14th, 2020 ( 82a ). As shown in Table 1 , the United States had 6,571,867 total cases resulting in 195,053 deaths, as of September 16th, 2020 according to the Centers of Disease Control and Prevention ( 10b ). Highly populated states like California, Texas, Florida, and New York have the highest total number of cases exceeding 400,000, while less populated rural states such as Vermont, Wyoming, and Maine have total case numbers below 5,000 ( 10b ). This reflects the predilection of the virus for more densely populated areas, allowing for higher rates of transmission in crowded areas compared with rural communities that are less densely populated. This can be seen in New York wherein the number of total deaths was 32,765 out numbering both California’s and Texas’s total deaths at 28,794 ( Table 1 ).

United States SARS-CoV-2 Statistics

The epidemiology of COVID-19 to date has been found to have disproportionate impacts on populations depending on sex and ethnicity. Table 1 highlights the differences in total cases and mortality by ethnicity, sex, and age. For example, in the United States ∼51.7% of total COVID-19 cases are female and 48.3% are male ( 10a ). In contrast, 54% of the total deaths in the United States are male compared with 46% female ( 10a ). The most significant predictor of poor outcome and mortality associated with COVID-19 is age. The mortality data in Table 1 include available data in nine different age brackets spanning 0–85 yr and above. Most notably, patients 50 yr and above in the United States have the highest mortality rates accounting for >94% of the total deaths due to COVID-19 ( Table 1 ; 10b , 10c ). In contrast, individuals 18–29 yr old have the highest percentage of total cases at 23.3% but only have a mortality rate of ∼0.5% ( 10b , 10c ). Older adults have higher rates of chronic health conditions that have been associated with poorer COVID-19 outcomes including hypertension, diabetes, coronary artery disease, and chronic kidney disease ( 62 ). These conditions place adults over 60 yr old at the highest risk of developing a complicated COVID-19 infection and mortality compared with younger cohorts without these conditions ( 62 ). Many patients with these conditions also take daily medications that interfere with the renin-angiotensin-aldosterone system (RAAS) such as angiotensin-converting enzyme (ACE) inhibitors for hypertension. This system has been implicated in COVID-19 infection and the virus’s ability to attach to host cells, causing dysregulated host cell responses, which subsequently results in worse outcomes ( 20 , 25 , 66 ).

Patients with COVID-19 often present with an array of symptoms that are similar to influenza that can make it difficult to diagnose. An epidemiological study of the first 41 patients infected with COVID-19 in Wuhan, China found that fatigue, cough, and fever were the most commonly reported symptoms ( 28 , 31 ). As a result, the general symptoms of COVID-19 are challenging to diagnose without reliable testing. Positive COVID-19 classifications include the following: asymptomatic, mild, moderate, severe, and critical. Asymptomatic patients test positive and exhibit no clinical symptoms while mild cases present with acute symptoms of respiratory tract infection and digestive complications. Moderate patients experience pneumonia, without noticeable hypoxemia, with lesions on chest computerized tomography (CT) scan. Severe patients experience pneumonia with detectable hypoxemia and CT lesions while critical patients experience acute respiratory distress syndrome (ARDS) along with possible shock, encephalopathy, myocardial injury, coagulation dysfunction, heart failure, and acute kidney injury ( 86 ). In a study of 80 patients hospitalized for COVID-19, over 90% had detectable ground glass opacities present on CT scan ( 31 , 84 ). A correlation was also found with the degree of inflammation seen on chest CT and lymphopenia (low white blood cell count), days of symptoms, and fever ( 84 ). Although these symptoms are often informative in diagnosis, COVID-19 has an unpredictable clinical course. As a result, 13.8% of positive patients had severe cases that required an in-patient hospital stay, with 4.7% requiring intensive care unit hospitalization and 2.3% of cases resulting in death ( 31 ). Taken together, these factors make COVID-19 difficult to manage and hard for clinicians to diagnose and predict clinical outcomes. Furthermore, real-time generation of data using artificial intelligence is an absolute priority to combat the spread, diagnosis, treatment, and categorized susceptibility to COVID-19 ( 1 ).

Understanding the pathophysiology of COVID-19 is critical to improving patient outcomes and determining how we can overcome the current pandemic. A key component to the virus being able to enter host cells and replicate is the ACE2 receptor, which is highly expressed in alveolar epithelial cells of the lung as confirmed by RNA-seq ( 91 ). The viral glycoprotein spike casing found on the exterior of a virus particle is responsible for eliciting viral entry into susceptible host cells ( 27 ). The process of viral entry requires priming of the spike protein by host expressed TMPRSS2, which interacts with the spike protein and cleaves it into two functional subunits known as S1 and S2 ( 27 , 43 , 66 ). The S1 subunit directly interacts with the ACE2 receptor, leaving the S2 subunit to facilitate viral fusion with the host cell membrane ( Fig. 1 ; 25 , 27 , 41a ). Internalization and replication of virus subsequently cause degradation of membrane-bound ACE2 receptors ( 27 ), which in turn causes an increase in angiotensin II (ANG II) and the angiotensin type 1 receptor (AT 1 R) ( Fig. 1 ). Angiotensinogen is cleaved by renin to angiotensin I (ANG I). ANG I is cleaved via ACE to ANG II, wherein it can freely interact with AT 1 R and angiotensin type 2 receptor (AT 2 R). Excess ANG I and II are hydrolyzed by ACE2 to become the heptapeptides ANG-(1-9)/ANG-(1-7) ( Fig. 1 ). Reduced or bound ACE2 is unable to hydrolyze ANG I/II, which results in an inability of the counterbalancing effects of the Mas receptor (Mas-R) to protect against detrimental disease/immune complications. As a result of COVID-19 infection, decreases in ACE2 cause elevated activity in the ANG II/AT 1 R axis, resulting in an inflammatory immune response ( 76 ). This deficiency leads to many adverse outcomes for patients including interstitial fibrosis, myocardial hypertrophy, endothelial fibrosis, and increased inflammation ( 76 ). Additionally, thrombosis and hypercoagulation secondary to platelet activation after lung epithelial damage are seen in patients with severe infections ( 39 , 86 ). Further consequences of hypercoagulation include disseminated intravascular coagulation, pulmonary embolisms, cardiac complications, and an increased risk of death ( 39 , 70 ). Coagulation is induced as a protective physiological control in response to vascular leak but in turn elicits dangerous consequences in COVID-19 patients. Often the physiologic response mechanisms to vascular leak and permeability fail, which allows for enhanced viral invasion, thus amplifying the problem in host cells on two separate fronts ( 86 ).

Biological effects of COVID-19 infection on angiotensin-converting enzyme 2 (ACE2) receptor and GTPase signaling pathways. The COVID-19 virus can bind and interact with both shed ACE2 and the cell membrane-bound ACE2 receptor. COVID-19 particles utilize and degrade membrane bound ACE2 receptors to gain entry into host cells. Virus particles also bind shed ACE2 causing a reduction in free ACE2 thus preventing the hydrolysis of ANG I/II into ANG-(1-9)/ANG-(1-7), which results in an imbalanced renin-angiotensin system that becomes skewed toward the ANG II/angiotensin type 1 receptor (AT 1 R) axis. COVID-19 produces an inflammatory response, i.e., the cytokine storm, which triggers cellular activation through cytokine receptors (CRs). Upon infection, these interactions favor detrimental complications such as acute respiratory distress syndrome (ARDS)/pulmonary fibrosis, vasoconstriction and alters cytoskeletal dynamics including cell proliferation, migration, and cytoskeletal composition. Intracellular elements such as Abelson murine leukemia viral oncogene homolog 1 kinase and Rho GTPase-associated proteins play a significant role in controlling polymerization of F-actin, maintaining the density of the extracellular matrix (ECM), and modulating myofibroblast proliferation, and the development of pulmonary fibrosis.

CARDIOVASCULAR DISEASE AND COVID-19

The highest risk factors for severe COVID-19 infection, including ARDS, is diabetes, hypertension, and a history of heart disease ( 76 ). Although the primary target of COVID-19 is the lungs, it can also have detrimental effects on the cardiovascular system. These comorbidities result in an upregulation of ACE2 on the cell surface of perivascular pericytes and cardiomyocytes, which is exploited by COVID-19 as the route of entry and infection ( 25 ). The leading cause of death in the United States is cardiovascular disease (CVD) causing more than 800,000 deaths in 2016 ( 20a ). A meta-analysis study in China found that COVID-19 causes acute cardiac injury in roughly 8.0% of patients, which poses concern for those that have a preexisting cardiac or metabolic condition ( 40 ). Cardiac injury may present as common arrhythmias, myocarditis, cardiogenic shock, and/or heart failure ( 24 , 49 ). Patients with prior cardiac history, including acute coronary syndrome and angina or myocardial infraction, have a higher risk for developing pneumonia and a decreased cardiac reserve that poses significant risks if they contract COVID-19 ( 40 , 88 ). The middle east respiratory syndrome coronavirus (MERS-CoV) is in the same corona virus family as COVID-19, has similar clinical outcomes, and has been extensively studied in patients with these comorbid conditions ( 5 ). In an analysis of 637 MERS-CoV patient cases, 30% had cardiac diseases and 50% had hypertension or diabetes ( 5 , 88 ). These cardiovascular disorders are highly prevalent in the United States, placing this vulnerable population in a higher risk category for acquiring severe infection with COVID-19. Patients with CVD may not have the ability to maintain cardiovascular function upon COVID-19 infection, leading to an increase in metabolic demand, exacerbating cardiovascular conditions thus increasing their risk for severe outcomes ( 68 ).

COVID-19 AND ACUTE RESPIRATORY DISTRESS SYNDROME

The host immune response to COVID-19 is similar to ARDS and therefore treatment modalities may be beneficial in treating COVID-19 patients. ARDS is defined clinically as bilateral neutrophilic infiltrates seen on imaging, acute hypoxia, and pulmonary edema ( 19 , 30 ). ARDS is caused by a dysregulated immune response with a fibroproliferative component due to excessive levels of cytokines, chemokines, and reactive oxygen species ( 30 ). ARDS-positive patients exhibit elevated levels of proinflammatory cytokines including IFN-y, IL-6, IL-12, and IL-1 compared with patients with uncomplicated COVID-19 infections ( 12 ). A study in ARDS positive mice confirmed these findings, wherein bronchoalveolar lavage fluid from ARDS positive mice strains had higher levels of TNF-α, IL-6, and vascular endothelial growth factor (VEGF) with reduced levels of IL-10 in comparison with controls ( 57 ). Similarly, patients hospitalized with severe COVID-19 infections have elevated cytokine profiles that are reflective of what defines a “cytokine storm.” The cytokine storm is a result of an uncontrolled immune response due to systemic inflammation and hemodynamic instability due to the abundance of proinflammatory cytokines that include IL-1, IL-6, IL-18, IFN-γ, and TNF-α ( Fig. 1 ) ( 65 ). As a result, new therapies are needed to thwart the immune response including nonconventional immunomodulation ( 22 ) to control the increase in proinflammatory cytokines that results in an accumulation of macrophages, neutrophils, and T cells from the circulation to the lung destroying the cell-cell interactions resulting in severe cases of ARDS. These findings suggest that patients suffering from ARDS and severe COVID-19 have a failed anti-inflammatory response that contributes to the excessive inflammatory damage caused by a host of proinflammatory cytokines wreaking havoc on lung tissue ( 58 ). Extensive damage to epithelial and endothelial cells of the lung triggers apoptotic destruction ( 12 ) leading to changes in the cellular junctions in alveolar tissue, thus increasing vascular permeability and ultimately alveolar fluid leak ( 30 ). Consequently, these cellular changes result in the pulmonary edema classically seen in ARDS patients ( 30 ), which is further complicated by an increase in dysregulated epithelial cell remodeling contributing to pulmonary fibrosis ( 12 ), a common cause of mortality in ARDS patients ( 30 ).

ABL1 AND VASCULAR PERMEABILITY

Abelson murine leukemia viral oncogene homolog 1 (Abl1) is a widely expressed nonreceptor tyrosine kinase that has been implicated in controlling cell morphology, growth, and survival ( 79 , 82 ). Abl1 is activated through a variety of receptor interactions and factors including cytokines, DNA damage, and oxidative stress ( 77 ). Abl1 plays a major role in modulating cytoskeletal dynamics influencing cell proliferation, cell survival, endocytosis, membrane trafficking, and cell-cell junctions and is also implicated in solid tumor proliferation and survival ( 34 ). Abl1 signals proteins that are critical to extracellular matrix (ECM) function and composition including the formation of actin stress fibers. These fibers interact with F-actin, inducing filopodia, which can alter cell-cell junctions ( 59 , 79 , 82 ).

Inhibition of Abl1 leads to increased Rho-Rock signaling, actomyosin contractility, and destabilization of cell-cell adhesions leading to an increase in barrier disruption ( 16 , 59 , 87 ). There is a direct implication of Abl1 as a therapeutic target to regulate GTPases in an effort to control ARDS and fibrosis as a result of disrupted endothelial barrier function and vascular leak in the lungs of ARDS patients ( 45 , 82 , 87 ). This critical association can be detrimental in ARDS, pulmonary fibrosis, and in severe cases of COVID-19 infection when vascular leak becomes uncontrolled and leads to sepsis ( 30 ). Multiple studies have investigated therapies to preserve endothelial barrier function. This includes the therapeutic use of low molecular weight heparin to combat the degradation of heparin sulfate by heparinase, thus protecting the endothelial barrier ( 7 ). Furthermore, the drug imatinib, an Abl1 inhibitor, has been investigated for possible repurposing and use for lung injury patients ( 36 , 82 ). One study found that pretreatment with imatinib protected against acute lung injury in mice ( 36 ) and may have potential to be repurposed in patients suffering from ARDS and/or COVID-19. Case studies report that imatinib resolved pneumonitis and pulmonary fibrosis secondary to antibiotics ( 9 , 59 ). Selective targeting of Abl1-based therapeutics needs further investigation to avoid potential negative side effects. For example, studies have shown that inhibiting Abl1 leads to increased endothelial permeability because of F-actin alternations and is amplified in cells undergoing cyclic stretch secondary to mechanical ventilation ( 38 , 59 ). As a result, increased vascular permeability will lead to an acceleration in vascular leak, exacerbating outcomes in ARDS patients.

PULMONARY FIBROSIS AND GTPase SIGNALING

While much is known about the progression of COVID-19 and ARDS, the mechanism of pathophysiology and associated treatment strategies are still under investigation. One such area includes GTPase signaling and its role in the development of ARDS and subsequent pulmonary fibrosis. Pulmonary fibrosis is caused by excessive fibroblasts and ECM protein deposits in the lungs, referred to as scarring of the lungs ( 4 ). Myofibroblasts are derived from resident fibroblasts and mesenchymal cells in the lung that express high amounts of smooth muscle actin ( 29 ) and are major players in the production of excess collagen leading to progressive fibrosis in patients ( 6 ). The overall ECM composition and stiffness have a direct impact on the degree of fibroblast migration, proliferation, and differentiation ( 4 ). Studies have shown that denser ECM substrates in later stages of disease show higher fibroblast migration levels compared with decreased fibroblasts migration in less stiff substrates as seen in earlier stages ( 6 ). One pathway with therapeutic implications in these physiological processes is the Rho GTPase signaling cascade ( 6 , 82 ).

Rho GTPase signaling has vast cellular implications in the control of actin and myosin stress fiber formation, regulation of cell adhesion molecules, cell migration, and common cellular functions ( 81 ). In addition, Rho GTPases play significant roles in cytoskeletal actin remodeling by polymerization and de-polymerization of monomeric G-actin leading to the conversion of F-actin ( 29 ). Increases in F-actin fibers causes stiffening of the ECM in patients suffering from ARDS leading to decreased vascular compliance ( 33 ). ARDS patients often require some form of oxygen supplementation due to severe hypoxemia. These measures often lead to hyperoxia and cause acute lung injury compounding damage to the lungs ( 30 , 44 ). Interestingly, hyperoxia in mice was found to activate the Rho/ROCK GTPase pathway and led to an increase in cell stiffness secondary to F-actin increase. However, when these mice were treated with Y-27632, a Rho inhibitor, the cytoskeletal changes in stiffness were prevented ( 81 ). These results suggest a possible connection in the control of GTPase signaling and ARDS and/or fibrosis complications seen in patients who require supplemental oxygen. Therefore, therapeutically modulating the increased activity of the GTPase cascade could decrease the adverse effects of ARDS pathogenesis secondary to ECM remodeling events.

As previously discussed, the Rho GTPase pathway regulates ECM density ( 81 , 82 ). This leads to the conclusion that higher activation levels of Rho and associated downstream targets lead to a higher levels of fibroblast proliferation. The ACE2 cascade is protective against lung fibrosis through activation of Rho GTPase pathways, while ACE is damaging and stimulates fibrosis in lung endothelial cells ( 46 ). These findings correlate to the virus’s predilection for patients with a history of obesity, hypertension, and CVD as these chronic conditions have been found to have lower levels of ACE2 at baseline ( 76 ). Therefore, the interplay between the ACE2/ACE and the Rho GTPase pathway may be an important association that could be a target for therapeutics to block lung fibrosis that results in ARDS and a majority of the mortality in COVID-19 patients. A study performed by Haung et al. proved this association by showing blockade of the Rho GTPase pathway inhibits matrix stiffness and alters stress fiber formation in fibroblasts ( 29 ). Therefore, Rho is actively involved in the underlying mechanism of pulmonary fibrosis by controlling proteins critical to modulating the ECM. A few significant trials have tested this theory for idiopathic pulmonary fibrosis by using nintedanib, a multikinase inhibitor, and pirfenidone, a small molecule antifibrotic, both of which were shown to reduce loss of lung functioning in pulmonary fibrosis patients ( 6 ).

STRATEGIES FOR SARS-C o V-2 THERAPEUTICS

Controlling the extensive spread and progression of SARS-CoV-2 has proven very difficult and will require a multidisciplinary approach with global collaboration. While certain areas of interest in SARS-CoV-2 remain unknown, past coronavirus knowledge provides scientists with the foundation for the development and/or repurposing of therapeutic interventions and vaccine development. Since the spike protein of each individual type of coronavirus is unique, this protein is currently being targeted in vaccine development as an approach to block initial entry of the virus ( 2 , 63 ). Multiple vaccines have entered clinical trials, the first of which is an RNA-based vaccine, mRNA-1273 ( 26 ). This vaccine entered phase I clinical trials on March 16, 2020 in collaboration with the National Institutes of Health (NIH), utilizing 45 healthy participants ranging in ages from 18 to 55 yr old ( 2 ). Although science has provided the foundational studies on vaccine development, the time needed to assess the safety and efficacy of vaccine candidates is a major bottleneck in the overall process.

While vaccines are being tested and manufactured, novel therapeutic treatments for the control and clinical management of COVID-19 infection are needed. Numerous approaches for treatment have been anecdotally reviewed in mainstream media; however, there are currently no Food and Drug Administration-approved medications for the treatment of COVID-19 infections ( 13 ). Still, there are a number of medications under evaluation for their effectiveness as potential antivirals that are recommended for use in the National Institutes of Health COVID-19 treatment guidelines ( 13 ). A noteworthy example of a current therapeutic intervention includes the use of convalescent plasma therapy ( 15 , 64 ). In this process, plasma-containing-neutralizing antibodies, removed from a donor who has previously recovered from a SARS-CoV-2 infection, are administered to infected patients to impart protection. Another unique therapeutic method involves treatment with soluble recombinant human ACE2 to disrupt viral entry via the spike protein-ACE2 interaction. Initial testing with recombinant ACE2 in simian cell lines and engineered human tissues shows promise in reducing viral load in a dose-dependent fashion ( 48 ). Finally, due to the high sense of urgency in clinical treatment of COVID-19 infection, the repurposing of known antiviral drugs has been explored with extreme caution, and the rationale are outlined in the NIH COVID-19 Treatment Guidelines ( 13 ). The treatment guidelines have the current recommendations either for or against the use of known antiviral drugs and the existing clinical trial data from the National Institutes of Health ( 13 ). Furthermore, implications for the use of some drugs have been identified using in silico databases that predict protein-protein interactions ( 23 , 74 ). Antiviral therapies contained in these studies include remdesivir, ivermectin, favipiravir, kaletra, and chloroquine/hydroxychloroquine with or without azithromycin ( 13 , 23 , 74 ).

The COVID-19 pandemic continues to pose a serious public health threat to nations around the world, as effective antiviral therapeutics or vaccines are yet to be developed. The primary goal in the COVID-19 pandemic is to limit transmission and define clinical management that improves the cure rate and effectively reduces the overall mortality rate. To achieve this goal, a complete understanding of all aspects of coronaviruses is needed to prevent or lessen their threat to society in the future. A thorough understanding of the epidemiology, pathophysiology and pandemic response efforts to combat COVID-19 is an invaluable lesson to society providing a protocol to fight future pandemics should they occur. Most importantly, scientific insights gained in the fight against COVID-19 will provide the evidence needed to develop vaccines and antiviral therapeutics that target viral entry, immune response and activation, and clinical management of secondary complications associated with severe infections.

M.P.M. and A.L.N-K. acknowledge funding support from the University of Toledo University Research Funding Opportunities (URFO) Program - Interdisciplinary Research Initiation Award I-127366-01.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

C.A.P., M.P.M., and A.L.N-K. prepared figures; C.A.P., M.P.M., and A.L.N-K. drafted manuscript; C.A.P., M.P.M., and A.L.N-K. edited and revised manuscript; C.A.P., M.P.M., and A.L.N-K. approved final version of manuscript.

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Who Are the Far-Right Groups Behind the U.K. Riots?

After a deadly stabbing at a children’s event in northwestern England, an array of online influencers, anti-Muslim extremists and fascist groups have stoked unrest, experts say.

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By Esther Bintliff and Eve Sampson

Esther Bintliff reported from London, and Eve Sampson from New York.

Violent unrest has erupted in several towns and cities in Britain in recent days, and further disorder broke out on Saturday as far-right agitators gathered in demonstrations around the country.

The violence has been driven by online disinformation and extremist right-wing groups intent on creating disorder after a deadly knife attack on a children’s event in northwestern England, experts said.

A range of far-right factions and individuals, including neo-Nazis, violent soccer fans and anti-Muslim campaigners, have promoted and taken part in the unrest, which has also been stoked by online influencers .

Prime Minister Keir Starmer has vowed to deploy additional police officers to crack down on the disorder. “This is not a protest that has got out of hand,” he said on Thursday. “It is a group of individuals who are absolutely bent on violence.”

Here is what we know about the unrest and some of those involved.

Where have riots taken place?

The first riot took place on Tuesday evening in Southport, a town in northwestern England, after a deadly stabbing attack the previous day at a children’s dance and yoga class. Three girls died of their injuries, and eight other children and two adults were wounded.

The suspect, Axel Rudakubana , was born in Britain, but in the hours after the attack, disinformation about his identity — including the false claim that he was an undocumented migrant — spread rapidly online . Far-right activists used messaging apps including Telegram and X to urge people to take to the streets.

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