Question: has social distancing had an impact on slowing the spread of COVID-19?

Title: Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates Content: The outbreak the SARS-CoV-2 (CoV-2) virus has resulted in over 2.5 million cases of COVID19, greatly stressing global healthcare infrastructure. Lacking medical prophylactic measures to combat disease spread, many nations have adopted social distancing policies in order to mitigate transmission of CoV-2. While mathematical models have suggested the efficacy of social distancing to curb the spread of CoV-2, there is a lack of systematic studies to quantify the real-world efficacy of these approaches. Here, we quantify the spread rate of COVID19 before and after national social distancing measures were implemented in 26 nations and compare this to the changes in COVID19 spread rate over equivalent time periods in 27 nations that did not enact social distancing policies. We find that social distancing policies significantly reduced the COVID19 spread rate. Using mixed linear regression models we estimate that social distancing policies reduced the spread of COVID19 by 66%. These data suggest that social distancing policies may be a powerful tool to prevent spread of COVID19 in real-world scenarios.

Title: Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States Content: In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180 countries. Estimating the effectiveness of these social-distancing strategies is challenging because surveillance of COVID-19 has been limited, with tests generally being prioritized for high-risk or hospitalized cases according to temporally and regionally varying criteria. Here we show that reductions in mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led to reductions in fever incidences, as captured by smart thermometers, after a mean lag of 6.5 days ($90\%$ within 3--10 days) that is consistent with the incubation period of COVID-19. Furthermore, counties with larger decreases in mobility subsequently achieved greater reductions in fevers ($p<0.01$), with the notable exception of New York City and its immediate vicinity. These results indicate that social distancing has reduced the transmission of influenza like illnesses, including COVID 19, and support social distancing as an effective strategy for slowing the spread of COVID-19.

Title: Social Distancing Has Merely Stabilized COVID-19 in the US Content: Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.

Title: Social Distancing Causally Impacts the Spread of SARS-CoV-2: A U.S. Nationwide Event Study Content: Background: To date, no study has examined the effectiveness of social distancing, while controlling for social mobility and social distancing restrictions in the United States. We utilize the quasi-experimental setting created by the nationwide protests precipitated by George Floyd's tragic death on May 25, 2020, to assess the causal impact of social distancing on the spread of SARS-CoV-2. Methods: Our sample period spans from January 22, 2020, to June 20, 2020, and consists of 474,422 county-days representing 3,142 counties from all 50 states and the District of Columbia. To assess the change in COVID-19 case counts following the protests, we employ a differences in differences estimation strategy in a multivariate setting, in which we control for social distancing restrictions and social mobility across counties. We also control for covariates that may influence COVID-19 transmission, and implement placebo tests using a Monte Carlo simulation. Findings: We document a country wide increase of over 3.06 cases per day, per 100,000 population, following the onset of the protests (95%CI: 2.47-3.65), and a further increase of 1.73 cases per day, per 100,000 population, in the counties in which the protests took place (95%CI: 0.59- 2.87). Relative to the week preceding the onset of the protests, this represents a 61.2% country wide increase in COVID-19 cases, and a further 34.6% increase in the protest counties. Interpretation: Our study documents a significant increase in COVID-19 case counts in counties that experienced a protest, and we conclude that social distancing practices causally impact the spread of SARS-CoV-2. The observed effect cannot be explained by changes in social distancing restrictions and social mobility, and placebo tests rule out the possibility that this finding is attributable to chance.

Title: Early Mandated Social Distancing is a Strong Predictor of Reduction in Highest Number of New COVID-19 cases per Day within Various Geographic Regions Content: Mandated social distancing has been globally applied to limit the spread of corona virus disease 2019 (COVID-19) from highly pathogenic severe acute respiratory syndrome (SARS) -associated coronavirus 2 (SARS-CoV-2). The benefit of this community-based intervention in limiting COVID-19 has not been proven nor quantified. We examined the effect of timing of mandated social distancing on the rate of COVID-19 in 119 geographic regions derived from 41 states within United States and 78 countries. We found that highest number of new COVID-19 cases per day per million persons was significantly associated with total number of COVID-19 cases per million persons on the day before mandated social distancing (Beta;=0.66, p<0.0001). Our findings suggest that the initiation of mandated social distancing for each doubling in number of existing COVID-19 cases would result in eventual peak with 58% higher number of COVID-19 infections per day. Subgroup analysis on those regions where the highest number of new COVID-19 cases per day have peaked increased Beta to .85 (p<0.0001). We demonstrate that initiating mandated social distancing at a 10 times smaller number of COVID-19 cases will reduce the number of daily new COVID-19 cases at peak by 80% highlighting the importance of this community-based intervention.

Title: A Modelling Analysis of Strategies for Relaxing COVID-19 Social Distancing Content: Abstract Background: The ability of countries to contain and control COVID-19 virus transmission via social distancing is critical in the absence of a vaccine. Early activation of robust measures has been shown to control the daily infection rate, and consequential pressure on the health care system. As countries begin to control COVID-19 spread an understanding of how to ease social distancing measures to prevent a rebound in cases and deaths is required. Methods: Using COVID-19 transmission data from the outbreak source in Hubei Province, China prior to activation of containment measures, we adapted an established individual-based simulation model of the city of Newcastle, Australia. Simulation of virus transmission in this model, with and without, social distancing measures activated permitted us to quantify social distancing effectiveness. Optimal strategies for relaxing social distancing were determined under two settings: with high numbers of daily cases, as in New York; and where early social distancing activation resulted in limited ongoing transmission, as in Perth, Australia. Findings: In countries where strong social distancing measures were activated after the COVID-19 virus had spread widely, our study found these measures are required to be maintained for significant periods before being eased, to return to a situation where daily case numbers become low. In countries where early responses to the COVID-19 pandemic have been highly successful, as in Australia, we show that a staged relaxation of social distancing prevents a rebound in cases. Interpretation: Modelling studies and direct observation have shown that robust and timely social distancing have the most effect in containing the spread of the COVID-19 virus. Questions arise as to the duration of strong social distancing measures, given they are highly disruptive to society and economic activity. This study demonstrates the necessity of holding robust social distancing in place until COVID-19 virus transmission has significantly decreased, and how they may then be safely eased.

Title: Dynamics of COVID-19 under social distancing measures are driven by transmission network structure Content: In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. These findings can improve future predictions of the timescale and efficacy of interventions needed to control similar outbreaks, and highlight the need for better quantification and control of household transmission.

Title: Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic Content: The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and travel distance decay rate and dwell time at home change rate was -0.586 (95% CI: -0.742 ~ -0.370) and 0.526 (95% CI: 0.293 ~ 0.700), respectively. Increases in state-specific doubling time of total cases ranged from 1.04 ~ 6.86 days to 3.66 ~ 30.29 days after social distancing orders were put in place, consistent with mechanistic epidemic prediction models. Social distancing mandates reduce the spread of COVID-19 when they are followed.

Title: Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study Content: BACKGROUND: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge. METHODS: We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county. FINDINGS: Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35–63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9–12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message. INTERPRETATION: This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future. FUNDING: None.

Title: Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study Content: BACKGROUND: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge. METHODS: We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county. FINDINGS: Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message. INTERPRETATION: This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future. FUNDING: None.

Title: Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States Content: Ever since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including the United States. It is one of the major community mitigation strategies, also known as non-pharmaceutical interventions. However, our understanding is remaining limited in how people practice social distancing. In this study, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19. We utilize an integrated dataset of mobile device location data for the contiguous United States plus Alaska and Hawaii over a 100-day period from January 1, 2020 to April 9, 2020. The major findings are: 1) the declaration of the national emergency concerning the COVID-19 outbreak greatly encouraged social distancing and the mandatory stay-at-home orders in most states further strengthened the practice; 2) the states with more confirmed cases have taken more active and timely responses in practicing social distancing; 3) people in the states with fewer confirmed cases did not pay much attention to maintaining social distancing and some states, e.g., Wyoming, North Dakota, and Montana, already began to practice less social distancing despite the high increasing speed of confirmed cases; 4) some counties with the highest infection rates are not performing much social distancing, e.g., Randolph County and Dougherty County in Georgia, and some counties began to practice less social distancing right after the increasing speed of confirmed cases went down, e.g., in Blaine County, Idaho, which may be dangerous as well.

Title: Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach Content: BACKGROUND: Across the U.S., various social distancing measures were implemented to control COVID-19 pandemic. However, there is uncertainty in the effectiveness of such measures for specific regions with varying population demographics and different levels of adherence to social distancing. The objective of this paper is to determine the impact of social distancing measures in unique regions. METHODS: We developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simulation model (ABM) that represents the social network and interactions among the people in a region considering population demographics, limited testing availability, imported infections from outside of the region, asymptomatic disease transmission, and adherence to social distancing measures. We adopted COVAM to represent COVID-19-associated events in Dane County, Wisconsin, Milwaukee metropolitan area, and New York City (NYC). We used COVAM to evaluate the impact of three different aspects of social distancing: 1) Adherence to social distancing measures; 2) timing of implementing social distancing; and 3) timing of easing social distancing. RESULTS: We found that the timing of social distancing and adherence level had a major effect on COVID-19 occurrence. For example, in NYC, implementing social distancing measures on March 5, 2020 instead of March 12, 2020 would have reduced the total number of confirmed cases from 191,984 to 43,968 as of May 30, whereas a 1-week delay in implementing such measures could have increased the number of confirmed cases to 1,299,420. Easing social distancing measures on June 1, 2020 instead of June 15, 2020 in NYC would increase the total number of confirmed cases from 275,587 to 379,858 as of July 31. CONCLUSION: The timing of implementing social distancing measures, adherence to the measures, and timing of their easing have major effects on the number of COVID-19 cases. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases Institute

Title: Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities Content: COVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: above median value, and median value and below for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

Title: U.S. county level analysis to determine If social distancing slowed the spread of COVID-19 Content: OBJECTIVE. To analyze the effectiveness of social distancing in the United States (U.S.). METHODS. A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties. RESULTS. Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19. CONCLUSIONS. As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19. These results might help policymakers to make the public understand the risks and benefits of the lockdown.

Title: Isolation and contact tracing can tip the scale to containment of COVID-19 in populations with social distancing Content: Background: Novel coronavirus (SARS-CoV-2) has extended its range of transmission in all parts of the world, with substantial variation in rates of transmission and severity of associated disease. Many countries have implemented social distancing as a measure to control further spread. Methods: We evaluate whether and under which conditions containment or slowing down COVID-19 epidemics are possible by isolation and contact tracing in settings with various levels of social distancing. We use a stochastic transmission model in which every person generates novel infections according to a probability distribution that is affected by the incubation period distribution (time from infection to symptoms), distribution of the latent period (time from infection to onset of infectiousness), and overall transmissibility. The model distinguishes between close contacts (e.g., within a household) and other contacts in the population. Social distancing affects the number of contacts outside but not within the household. Findings: The proportion of asymptomatic or unascertained cases has a strong impact on the controllability of the disease. If the proportion of asymptomatic infections is larger than 30%, contact tracing and isolation cannot achieve containment for an R0 of 2.5. Achieving containment by social distancing requires a reduction of numbers of non-household contacts by around 90%. Depending on the realized level of contact reduction, tracing and isolation of only household contacts, or of household and non-household contacts are necessary to reduce the effective reproduction number to below 1. A combination of social distancing with isolation and contact tracing leads to synergistic effects that increase the prospect of containment. Interpretation: Isolation and contact tracing can be an effective means to slow down epidemics, but only if the majority of cases are ascertained. In a situation with social distancing, contact tracing can act synergistically and tip the scale towards containment, and can therefore be a tool for controlling COVID-19 epidemics as part of an exit strategy from current lockdown measures.

Title: COVID-19 related social distancing measures and reduction in city mobility Content: In the absence of any pharmacological intervention, one approach to slowing the COVID-19 pandemic is reducing the contact rate in the population through social distancing. Governments the world over have instituted different measures to increase social distancing but information on their effectiveness in reducing mobility is lacking. We analyzed the mobility data from 41 cities to look at the effect of these interventions. The median mobility across cities on March 2, 2020 was 100% (IQR: 94%, 107%), which decreased to a median of 10% (IQR: 7%, 17%) on March 26, 2020. We found that the mobility decreased on average by 3.4% (95%CI: 3.3%, 3.6%) per day from March 2 through March 26. Social distancing measures decreased the mobility by an additional 23% (95%CI: 20%, 27%). Our study provides initial evidence for the reduction in mobility in cities instituting social distancing measures.

Title: Social distancing: A non-pharmacological intervention for COVID-19 Content: Social distancing is one of the non-pharmacological measures to contain the infection of COVID-19. At this point in time, no vaccine is available to prevent the infection, no effective drugs are available to prevent and treat the disease, and none of the communities have acquired herd immunity. Various models have shown positive impact of social distancing, provided its implementation on vast majority of the population over a long period of time. Its effect is manifold. Besides flattening the curve, it impacts the political, fiscal, social, economic aspects of the society, along with socially vulnerable and economically underprivileged population. It becomes obsolete after the population develops herd immunity subsequent to widespread infection in the community, or after effective mass immunisation or specific drugs for its control, cure and prevention are available widely.

Title: Social distancing: A non-pharmacological intervention for COVID-19. Content: Social distancing is one of the non-pharmacological measures to contain the infection of COVID-19. At this point in time, no vaccine is available to prevent the infection, no effective drugs are available to prevent and treat the disease, and none of the communities have acquired herd immunity. Various models have shown positive impact of social distancing, provided its implementation on vast majority of the population over a long period of time. Its effect is manifold. Besides flattening the curve, it impacts the political, fiscal, social, economic aspects of the society, along with socially vulnerable and economically underprivileged population. It becomes obsolete after the population develops herd immunity subsequent to widespread infection in the community, or after effective mass immunisation or specific drugs for its control, cure and prevention are available widely.

Title: Dynamic coupling between the COVID epidemic timeline and the behavioral response to PAUSE in New York State counties Content: By many expert opinions, the COVID 19 epidemic is still much in its developing stages. While awaiting for effective clinical answers for the outbreak, the best approach remains that of social distancing. This raises a few crucial questions related to the extent to which social distancing measures were (1) well timed, (2) necessary and (3) efficient. By investigating correlations between epidemic measures such as daily infection rate, and social mobility measures (as reported by Apple and Google), our study aims to establish whether data identifies social distancing measures as a primary player in controlling the outbreak in New York State.

Title: A Decision Analytic Approach for Social Distancing Policies During the COVID-19 Pandemic Content: The COVID-19 pandemic has become a crucial public health issue in many countries including the United States. In the absence of the right vaccine strain and sufficient antiviral stockpiles on hand, nonpharmaceutical interventions have become valuable public health tools at the early stages of the pandemic and they are employed by many countries across the globe. These interventions are designed to increase social distancing between individuals to reduce the transmission of the virus and eventually dampen the burden on the healthcare system. The virus transmissibility is a function of the average number of contacts individuals have in their communities and it is highly dependent on population density and daily mobility patterns, along with other social factors. These show significant variation across the United States. In this article, we study the effectiveness of social distancing measures in communities with different population density. Specifically, first we show how the empirical estimation of reproduction number differs for two completely different states, thus the experience of the COVID-19 outbreak is drastically different, suggesting different outbreak growth rates in practice. Second, we develop an age-structured compartmental model for simulating the disease spread to demonstrate the variation in the observed outbreak characteristics. We find that early trigger and late trigger options present a trade-off between the peak magnitude and the overall death toll of the outbreak which may also vary across different populations.

Title: The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19 Content: Physical distancing measures are intended to mitigate the spread of COVID-19. However, the impact these measures have on social contact and disease transmission patterns remains unclear. We ran the first comparative contact survey (N=53,708) across eight countries (Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom, United States) for the period March 13 - April 13, 2020. Our results show that social contact numbers mainly decreased after governments issued physical distancing guidelines rather than after announcing national lockdown measures. Compared to pre-COVID levels, social contact numbers decreased by 48% - 85% across countries. Except in Italy, these reductions were smaller than those observed in Wuhan (China). However, they sufficed to bring the R0 below one in almost every context considered. Finally, in all countries studied, the numbers of contacts decreased more rapidly among older people than among younger people, indicating higher levels of protection for groups at greater risk.

Title: How long does a lockdown need to be? Content: Social distancing, often in the form of lockdown, has been adopted by many countries as a way to contrast the spreading of COVID-19. We discuss the temporal aspects of social distancing in contrasting an epidemic diffusion. We argue that a strategy based uniquely on social distancing requires to maintain the relative measures for a very long time, while a more articulate strategy, which also uses early detection and prompt isolation, can be both more efficient on reducing the epidemic peak and allow to relax the social distancing measures after a much shorter time. We consider in more detail the situation in Italy, simulating the effect of different strategies through a recently introduced SIR-type epidemiological model. The short answer to the question in the title is:"it depends on what else you do".

Title: The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19 Content: We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.

Title: No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic Content: BACKGROUND: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (ß) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and ß values. RESULTS: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

Title: Social distancing alters the clinical course of COVID-19 in young adults: A comparative cohort study Content: BACKGROUND: Social distancing and stringent hygiene seem effective in reducing the number of transmitted virus particles, and therefore the infectivity, of coronavirus disease 2019 (COVID-19) and could alter the mode of transmission of the disease. However, it is not known if such practices can change the clinical course in infected individuals. METHODS: We prospectively studied an outbreak of COVID-19 in Switzerland among a population of 508 predominantly male soldiers with a median age of 21 years. We followed the number of infections in two spatially separated cohorts with almost identical baseline characteristics with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before and after implementation of stringent social distancing. RESULTS: Of the 354 soldiers infected prior to the implementation of social distancing, 30% fell ill from COVID-19. While no soldier in a group of 154, in which infections appeared after implementation of social distancing, developed COVID-19 despite the detection of viral RNA in the nose and virus-specific antibodies within this group. CONCLUSIONS: Social distancing not only can slow the spread of SARS-CoV-2 in a cohort of young, healthy adults but can also prevent the outbreak of COVID-19 while still inducing an immune response and colonizing nasal passages. Viral inoculum during infection or mode of transmission may be key factors determining the clinical course of COVID-19.

Title: Protocol for an Observational Study on the Effects of Social Distancing on Influenza-Like Illness and COVID-19 Content: The novel coronavirus disease (COVID-19) is a highly contagious respiratory disease that was first detected in Wuhan, China in December 2019, and has since spread around the globe, claiming more than 69,000 lives by the time this protocol is written. It has been widely acknowledged that the most effective public policy to mitigate the pandemic is \emph{social and physical distancing}: keeping at least six feet away from people, working from home, closing non-essential businesses, etc. There have been a lot of anecdotal evidences suggesting that social distancing has a causal effect on disease mitigation; however, few studies have investigated the effect of social distancing on disease mitigation in a transparent and statistically-sound manner. We propose to perform an optimal non-bipartite matching to pair counties with similar observed covariates but vastly different average social distancing scores during the first week (March 16th through Match 22nd) of President's \emph{15 Days to Slow the Spread} campaign. We have produced a total of $302$ pairs of two U.S. counties with good covariate balance on a total of $16$ important variables. Our primary outcome will be the average observed illness collected by Kinsa Inc. two weeks after the intervention period. Although the observed illness does not directly measure COVID-19, it reflects a real-time aspect of the pandemic, and unlike confirmed cases, it is much less confounded by counties' testing capabilities. We also consider observed illness three weeks after the intervention period as a secondary outcome. We will test a proportional treatment effect using a randomization-based test with covariance adjustment and conduct a sensitivity analysis.

Title: Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States Content: COVID-19 is present in every state and over 90 percent of all counties in the United States. Decentralized government efforts to reduce spread, combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the U.S. is a challenge. We generate a novel metric to represent social distancing behavior derived from mobile phone data, and examine its relationship with COVID-19 case reports at the county level. Our analysis reveals that social distancing is strongly correlated with decreased COVID-19 case growth rates for the 25 most affected counties in the United States, with a lag period consistent with the incubation time of SARS-CoV-2. We also demonstrate evidence that social distancing was already under way in many U.S. counties before county and state-level policies were implemented. This study strongly supports social distancing as an effective way to mitigate COVID-19 transmission in the United States.

Title: An analysis of the ethics of lockdown in India Content: Over the past 6 months, coronavirus-induced disease (COVID-19) has spread across 212 countries, affecting millions of people. As it has no known cure, social distancing is highly recommended for prevention of spread of the disease. Here, we have described the impact of the social distancing measures implemented by the Government of India on various sections of the society, especially the vulnerable sections. Furthermore, we have presented an analysis of these measures, according to the World Health Organization´s Guidance for Managing Ethical Issues in Infectious Disease Outbreaks (2016); we have also applied principles, as applicable, from the Indian Council of Medical Research’s National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017). Finally, we have presented several measures that should have been adopted before and in addition to implementing the lockdown to improve its effectiveness.

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate Content: State and local governments imposed social distancing measures in March and April 2020 to contain the spread of the novel coronavirus disease (COVID-19). These measures included bans on large social gatherings; school closures; closures of entertainment venues, gyms, bars, and restaurant dining areas; and shelter-in-place orders. We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020, and April 27, 2020. An event study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate of confirmed COVID-19 cases by 5.4 percentage points after one to five days, 6.8 percentage points after six to ten days, 8.2 percentage points after eleven to fifteen days, and 9.1 percentage points after sixteen to twenty days. Holding the amount of voluntary social distancing constant, these results imply that there would have been ten times greater spread of COVID-19 by April 27 without shelter-in-place orders (ten million cases) and more than thirty-five times greater spread without any of the four measures (thirty-five million cases). Our article illustrates the potential danger of exponential spread in the absence of interventions, providing information relevant to strategies for restarting economic activity.

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate Content: State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate. Content: State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].

Title: Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19 Content: The U.S. is the epicenter of the coronavirus disease 2019 (COVID-19) pandemic. In response, governments have implemented measures to slow transmission through "social distancing." However, the practice of social distancing may depend on prevailing socioeconomic conditions and beliefs. Using 15-17 million anonymized cell phone records, we find that lower per capita income and greater Republican orientation were associated with significantly reduced social distancing among U.S. counties. These associations persisted after adjusting for county-level sociodemographic and labor market characteristics as well as state fixed effects. These results may help policymakers and health professionals identify communities that are most vulnerable to transmission and direct resources and communications accordingly.

Title: Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis Content: Background: Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as SARS-CoV-2 during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to explore the impact on the epidemic curve of fewer contacts when individuals reduce the time they spend on selected daily activities. Methods: We combined the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0-15, 16-24, 25-44, 45-64, 65+) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). We assumed that reductions in time are proportional to reductions in contacts. The derived matrices were then applied in an age-structured dynamic-transmission model of COVID-19 to explore the effects. Findings: The relative reductions in the derived contact matrices were highest when closing schools (in ages 0-14 years), workplaces (15-64 years), and stopping social visits (65+ years). For COVID-19, the closure of workplaces, schools, and stopping social visits had the largest impact on reducing the epidemic curve and delaying its peak, while the predicted impact of fewer contacts in parks, bars/cafes/restaurants, and non-essential shopping were minimal. Interpretation: We successfully augmented contact matrices with time-use data to predict the highest impact of social distancing measures from reduced contacts when spending less time at work, school, and on social visits. Although the predicted impact from other leisure activities with potential for close physical contact were minimal, changes in mixing patterns and time-use immediately after re-allowing social activities may pose increased short-term transmission risks, especially in potentially crowded environments indoors.

Title: Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries Content: Abstract From the end of 2019, an unprecedented novel coronavirus, which was named COVID-19 by the World Health Organization (WHO) emerged from Wuhan city, China. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 has continued to rise, with over 4 million confirmed-cases and over 300,000 deaths worldwide until mid-May. This study aims to present the effect of the promulgation of social distancing measures on the spread of COVID-19 in the cases of 10 highly infected countries. The authors focus on the statistics of the COVID-19 confirmed-cases and deaths in 10 highly infected countries, including The U.S., Spain, Italy, The U.K., France, Germany, Russia, Turkey, Iran and China, and the response to the pandemic of these countries in the period from January 11 to May 2, 2020. The relationships between the social distancing measures and the statistics of COVID-19 confirmed-cases and deaths were analyzed in order to elucidate the effectiveness of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. The results showed it took1–4 weeks since the highest level of social distancing measures promulgation until the daily confirmed-cases and deaths showed signs of decreasing. The effectiveness of the social distancing measures on the spread of COVID-19 was different between the 10 focused countries. This variation is due to the difference in the levels of promulgated social distancing measures, as well as the difference in the COVID-19 spread situation at the time of promulgation between the countries.

Title: The Benefits and Costs of Social Distancing in Rich and Poor Countries Content: Social distancing is the primary policy prescription for combating the COVID-19 pandemic, and has been widely adopted in Europe and North America. We estimate the value of disease avoidance using an epidemiological model that projects the spread of COVID-19 across rich and poor countries. Social distancing measures that"flatten the curve"of the disease to bring demand within the capacity of healthcare systems are predicted to save many lives in high-income countries, such that practically any economic cost is worth bearing. These social distancing policies are estimated to be less effective in poor countries with younger populations less susceptible to COVID-19, and more limited healthcare systems, which were overwhelmed before the pandemic. Moreover, social distancing lowers disease risk by limiting people's economic opportunities. Poorer people are less willing to make those economic sacrifices. They place relatively greater value on their livelihood concerns compared to contracting COVID-19. Not only are the epidemiological and economic benefits of social distancing much smaller in poorer countries, such policies may exact a heavy toll on the poorest and most vulnerable. Workers in the informal sector lack the resources and social protections to isolate themselves and sacrifice economic opportunities until the virus passes. By limiting their ability to earn a living, social distancing can lead to an increase in hunger, deprivation, and related mortality and morbidity. Rather than a blanket adoption of social distancing measures, we advocate for the exploration of alternative harm-reduction strategies, including universal mask adoption and increased hygiene measures.

Title: On the benefits of flattening the curve: A perspective() Content: The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. And some reports understate one benefit of reducing the frequency or proximity of interpersonal encounters, a reduction in the total number of infections. In hopes that understanding will increase compliance, we describe how social distancing a) reduces the peak incidence of infections, b) delays the occurrence of this peak, and c) reduces the total number of infections during epidemics. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.

Title: No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic. Content: BACKGROUND In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness. OBJECTIVE In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over the course of five weeks. METHODS Relying on the SIR model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google's and Apple's mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values. RESULTS Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of the variation in the disease transmission rate. CONCLUSIONS Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down the spread of the disease. Apart from providing less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy-makers as to what locations should be given a higher priority for enforcing social distancing measures. CLINICALTRIAL

Title: Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors Content: BACKGROUND: Social distancing-when people limit close contact with others outside their household-is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed. PURPOSE: We examined whether one type of social distancing behavior-reduced movement outside the home-was associated with conventional health behaviors. METHOD: We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents' conventional health behaviors. RESULTS: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors-particularly less obesity and greater physical activity-evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA. CONCLUSIONS: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

Title: A simplified model for expected development of the SARS-CoV-2 (Corona) spread in Germany and US after social distancing Content: Widespread opinions and discussion exist regarding the efficiency of social distancing after crucial spread of the SARS-CoV-2 virus during the actual Covid-19 pandemic. While Germany has released a federal law that prohibits any type of direct contact for more than 2 people other countries including the US released curfews. People are now wondering whether these measures are helpful to stop or hamper the Covid-19 pandemic and to limit the spread of the new corona virus. A quantitative statement on this question depends on many parameters that are difficult to grasp mathematically and cannot therefore be made conclusively (they include consistent adherence to the measures decided, the estimated number of unreported cases, the possible limitation by test capacities, possible mutations of the virus, etc ...). However, it turns out that a reduction in the actual daily new infection rate (actual daily growth rate of reported cases, in short: infection rate) from the current value of 30-35% in the US to 10% would be extremely effective in stopping the spread of the virus. The severe restrictions in Germany which closed any public events, schools and universities a week ago might already have contributed to a reduction of the growth rate of reported cases below 30%.

Title: The science of social distancing and total lock down: Does it work? whom does it benefit? Content: COVID-19 is spiralling like wild fire and has caused a global mayhem. Many lives have already been lost, and the global economy is at stake. In the absence of vaccine, social distancing and total lock down is the current mainstay in efforts to mitigate and flatten the epidemic curves. Several countries across the globe are currently implementing social distancing and total lock downs. However, what is social distancing and lock down? Does it work? Whom does it benefit? We expound on these questions and conclude that social distancing and lock down benefit the local and international community alike. Moreover, we hypothesis that evacuations may facilitate international spread of a highly contagious disease of pandemic potential.

Title: On the benefits of flattening the curve: A perspective Content: The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. And some reports understate one benefit of reducing the frequency or proximity of interpersonal encounters, a reduction in the total number of infections. In hopes that understanding will increase compliance, we describe how social distancing (a) reduces the peak incidence of infections, (b) delays the occurrence of this peak, and (c) reduces the total number of infections during epidemics. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.

Title: Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States Content: The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.

Title: Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries Content: From the end of 2019, an unprecedented novel coronavirus, which was named COVID-19 by the World Health Organization (WHO) emerged from Wuhan city, China. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 has continued to rise, with over 4 million confirmed-cases and over 300,000 deaths worldwide until mid-May. This study aims to present the effect of the promulgation of social distancing measures on the spread of COVID-19 in the cases of 10 highly infected countries. The authors focus on the statistics of the COVID-19 confirmed-cases and deaths in 10 highly infected countries, including The U.S., Spain, Italy, The U.K., France, Germany, Russia, Turkey, Iran and China, and the response to the pandemic of these countries in the period from January 11 to May 2, 2020. The relationships between the social distancing measures and the statistics of COVID-19 confirmed-cases and deaths were analyzed in order to elucidate the effectiveness of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. The results showed it took1-4 weeks since the highest level of social distancing measures promulgation until the daily confirmed-cases and deaths showed signs of decreasing. The effectiveness of the social distancing measures on the spread of COVID-19 was different between the 10 focused countries. This variation is due to the difference in the levels of promulgated social distancing measures, as well as the difference in the COVID-19 spread situation at the time of promulgation between the countries.

Title: The trajectory of loneliness in response to COVID-19 Content: Social distancing and "stay-at-home" orders are essential to contain the coronavirus outbreak (COVID-19), but there is concern that these measures will increase feelings of loneliness, particularly in vulnerable groups. The present study examined change in loneliness in response to the social restriction measures taken to control the coronavirus spread. A nationwide sample of American adults (N = 1,545; 45% women; ages 18 to 98, M = 53.68, SD = 15.63) was assessed on three occasions: in late January/early February 2020 (before the outbreak), in late March (during the President's initial "15 Days to Slow the Spread" campaign), and in late April (during the "stay-at-home" policies of most states). Contrary to expectations, there were no significant mean-level changes in loneliness across the three assessments (d = .04, p > .05). In fact, respondents perceived increased support from others over the follow-up period (d = .19, p < .01). Older adults reported less loneliness overall compared to younger age groups but had an increase in loneliness during the acute phase of the outbreak (d = .14, p < .05). Their loneliness, however, leveled off after the issuance of stay-at-home orders. Individuals living alone and those with at least one chronic condition reported feeling lonelier at baseline but did not increase in loneliness during the implementation of social distancing measures. Despite some detrimental impact on vulnerable individuals, in the present sample, there was no large increase in loneliness but remarkable resilience in response to COVID-19. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Title: The trajectory of loneliness in response to COVID-19. Content: Social distancing and "stay-at-home" orders are essential to contain the coronavirus outbreak (COVID-19), but there is concern that these measures will increase feelings of loneliness, particularly in vulnerable groups. The present study examined change in loneliness in response to the social restriction measures taken to control the coronavirus spread. A nationwide sample of American adults (N = 1,545; 45% women; ages 18 to 98, M = 53.68, SD = 15.63) was assessed on three occasions: in late January/early February 2020 (before the outbreak), in late March (during the President's initial "15 Days to Slow the Spread" campaign), and in late April (during the "stay-at-home" policies of most states). Contrary to expectations, there were no significant mean-level changes in loneliness across the three assessments (d = .04, p > .05). In fact, respondents perceived increased support from others over the follow-up period (d = .19, p < .01). Older adults reported less loneliness overall compared to younger age groups but had an increase in loneliness during the acute phase of the outbreak (d = .14, p < .05). Their loneliness, however, leveled off after the issuance of stay-at-home orders. Individuals living alone and those with at least one chronic condition reported feeling lonelier at baseline but did not increase in loneliness during the implementation of social distancing measures. Despite some detrimental impact on vulnerable individuals, in the present sample, there was no large increase in loneliness but remarkable resilience in response to COVID-19. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Title: Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors Content: BACKGROUND: Social distancing—when people limit close contact with others outside their household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed. PURPOSE: We examined whether one type of social distancing behavior—reduced movement outside the home—was associated with conventional health behaviors. METHOD: We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors. RESULTS: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA. CONCLUSIONS: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

Title: Impact of non-pharmaceutical interventions against COVID-19 in Europe: a quasi-experimental study Content: The current epidemic of COVID-19 is unparalleled in recent history as are the social distancing interventions that have led to a significant halt on the economic and social life of so many countries. However, there is very little empirical evidence about which social distancing measures have the most impact. We report a quasi-experimental study of the impact of various interventions for control of the outbreak. Data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our primary analyses were modelled in R using Bayesian generalised additive mixed models (GAMM). We found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay at home orders, closure of all non-businesses and requiring the wearing of facemasks or coverings in public was not associated with any independent additional impact. Our results could help inform strategies for coming out of lockdown.

Title: Correcting misperceptions of exponential coronavirus growth increases support for social distancing Content: The most effective way to stem the spread of a pandemic such as coronavirus disease 2019 (COVID-19) is social distancing, but the introduction of such measures is hampered by the fact that a sizeable part of the population fails to see their need. Three studies conducted during the mass spreading of the virus in the United States toward the end of March 2020 show that this results partially from people's misperception of the virus's exponential growth in linear terms and that overcoming this bias increases support for social distancing. Study 1 shows that American participants mistakenly perceive the virus's exponential growth in linear terms (conservatives more so than liberals). Studies 2 and 3 show that instructing people to avoid the exponential growth bias significantly increases perceptions of the virus's growth and thereby increases support for social distancing. Together, these results show the importance of statistical literacy to recruit support for fighting pandemics such as the coronavirus.

Title: Correcting misperceptions of exponential coronavirus growth increases support for social distancing. Content: The most effective way to stem the spread of a pandemic such as coronavirus disease 2019 (COVID-19) is social distancing, but the introduction of such measures is hampered by the fact that a sizeable part of the population fails to see their need. Three studies conducted during the mass spreading of the virus in the United States toward the end of March 2020 show that this results partially from people's misperception of the virus's exponential growth in linear terms and that overcoming this bias increases support for social distancing. Study 1 shows that American participants mistakenly perceive the virus's exponential growth in linear terms (conservatives more so than liberals). Studies 2 and 3 show that instructing people to avoid the exponential growth bias significantly increases perceptions of the virus's growth and thereby increases support for social distancing. Together, these results show the importance of statistical literacy to recruit support for fighting pandemics such as the coronavirus.

Title: The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets Content: Social distancing is an important component of the response to the novel Coronavirus (COVID-19) pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads, and"flattens the curve"such that the medical system can better treat infected individuals. However, it remains unclear how the public will respond to these policies. This paper presents the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We use public geolocated Twitter data to measure how much a user travels in a given week. We find a large reduction in travel in the United States after the implementation of social distancing policies, with larger reductions in states that were early adopters and smaller changes in states without policies. Our findings are presented on http://socialmobility.covid19dataresources.org and we will continue to update our analysis during the pandemic.

Title: Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data Content: A better understanding of how the COVID-19 epidemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number (R0) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the epidemic.

Title: Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply Content: Social distancing has been adopted as a non-pharmaceutical intervention to prevent the COVID-19 pandemic from overwhelming the medical resources across the United States (US). The catastrophic socio-economic impacts of this intervention could outweigh its benefits if the timing and duration of implementation are left uncontrolled and ill-strategized. Here we investigate the dynamics of social distancing on age-stratified US population and benchmark its effectiveness in reducing the burden on hospital and ICU beds. Our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. We determine an optimal intermittent social-to-no-distancing ratio of 5:1 corresponding to ~80% reduction in healthcare demands; beyond this ratio, benefit of social distancing diminishes to a negligible level. COVID-19 Medical Forecast: https://eece.wustl.edu/chakrabarty-group/covid/

Title: Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis Content: Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.

Title: COVID-19 and the Social Distancing Paradox: dangers and solutions Content: Background: Without proven effect treatments and vaccines, Social Distancing is the key protection factor against COVID-19. Social distancing alone should have been enough to protect again the virus, yet things have gone very differently, with a big mismatch between theory and practice. What are the reasons? A big problem is that there is no actual social distancing data, and the corresponding people behavior in a pandemic is unknown. We collect the world-first dataset on social distancing during the COVID-19 outbreak, so to see for the first time how people really implement social distancing, identify dangers of the current situation, and find solutions against this and future pandemics. Methods: Using a sensor-based social distancing belt we collected social distance data from people in Italy for over two months during the most critical COVID-19 outbreak. Additionally, we investigated if and how wearing various Personal Protection Equipment, like masks, influences social distancing. Results: Without masks, people adopt a counter-intuitively dangerous strategy, a paradox that could explain the relative lack of effectiveness of social distancing. Using masks radically changes the situation, breaking the paradoxical behavior and leading to a safe social distance behavior. In shortage of masks, DIY (Do It Yourself) masks can also be used: even without filtering protection, they provide social distancing protection. Goggles should be recommended for general use, as they give an extra powerful safety boost. Generic Public Health policies and media campaigns do not work well on social distancing: explicit focus on the behavioral problems of necessary mobility are needed.

Title: Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19 Content: COVID-19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health-protective behaviors such as social distancing. For this purpose, a questionnaire-based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases.

Title: Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19 Content: Social distancing policies are critical but economically painful measures to flatten the curve against emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government issued social distancing recommendations but left to the states the most difficult and consequential decisions restricting behavior, such as canceling events, closing schools and businesses, and issuing stay-at-home orders. We present an original dataset of state-level social distancing policy responses to the epidemic and explore how political partisanship, COVID-19 caseload, and policy diffusion explain the timing of governors' decisions to mandate social distancing. An event history analysis of five social distancing policies across all fifty states reveals the most important predictors are political: all else equal, Republican governors and governors from states with more Trump supporters were slower to adopt social distancing policies. These delays are likely to produce significant, on-going harm to public health.

Title: Targeted adaptive isolation strategy for COVID-19 pandemic Content: We investigate the effects of social distancing in controlling the impact of the COVID-19 epidemic using a simple susceptible-infected-removed epidemic model. We show that an alternative or complementary approach based on targeted isolation of the vulnerable sub-population may provide a more efficient and robust strategy at a lower economic and social cost within a shorter timeframe resulting in a collectively immune population.

Title: Targeted adaptive isolation strategy for Covid-19 pandemic Content: We investigate the effects of social distancing in controlling the impact of the COVID-19 epidemic using a simple susceptible-infected-removed epidemic model. We show that an alternative or complementary approach based on targeted isolation of the vulnerable sub-population may provide a more efficient and robust strategy at a lower economic and social cost within a shorter timeframe resulting in a collectively immune population.

Title: Social distancing, population density, and spread of COVID-19 in England: a longitudinal study Content: BACKGROUND: The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM: To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING: Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD: Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ≥65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS: Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION: After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.

Title: Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics Content: The infectious diseases are spreading due to human interactions enabled by various social networks. Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading. To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks. Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading. The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one. Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values. Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy. Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement. We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time. We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.

Title: Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 Pandemic Content: Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the public's response to social distancing campaigns has been lacking. We used anonymized and aggregated mobility data from Google Location History users to estimate the impact of social distancing recommendations on bulk mobility among users who have opted into this service. We found that state-of-emergency declarations resulted in approximately a 10% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 25% reduction in mobility the following week. Subsequent shelter-in-place mandates provided an additional 29% reduction. Our findings provide evidence that state-wide mandates are effective in promoting social distancing within this study group.

Title: Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources Content: The outbreak of SARS-CoV-2 in China has spread around the world, infecting millions and causing governments to implement strict policies to counteract the spread of the disease. One of the most effective strategies in reducing the severity of the pandemic is social distancing, where members of the population systematically reduce their interactions with others to limit the transmission rate of the virus. However, the implementation of social distancing can be difficult and costly, making it imperative that both policy makers and the citizenry understand the potential benefits if done correctly and the risks if not. In this work, a mathematical model is developed to study the effects of social distancing on the spread of the SARS-CoV-2 virus in Canada. The model is based upon a standard epidemiological SEIRD model that has been stratified to directly incorporate the proportion of individuals who are following social distancing protocols. The model parameters characterizing the disease are estimated from current epidemiological data on COVID-19 using machine learning techniques. The results of the model show that social distancing policies in Canada have already saved thousands of lives and that the prolonged adherence to social distancing guidelines could save thousands more. Importantly, our model indicates that social distancing can significantly delay the onset of infection peaks, allowing more time for the production of a vaccine or additional medical resources. Furthermore, our results stress the importance of easing social distancing restrictions gradually, rather than all at once, in order to prevent a second wave of infections. Model results are compared to the current capacity of the Canadian healthcare system by examining the current and future number of ventilators available for use, emphasizing the need for the increased production of additional medical resources.

Title: Defining Facets of Social Distancing during the COVID-19 Pandemic: Twitter Analysis Content: Social distancing has been one of the primary mitigation strategies in the United States to control the spread of novel coronavirus disease (COVID-19) and can be viewed as a multi-faceted public health measure. Using Twitter data, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine the most amplified tweets among social distancing facets. We analyzed a total of 259,529 unique tweets containing "coronavirus" from 115,485 unique users between January 23, 2020 and March 24, 2020 that were identified by the Twitter API as English and U.S.-based. Tweets containing specified keywords (determined a priori) were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. Tweets about social disruptiveness were most retweeted, and implementation tweets were most favorited. Social distancing tweets became overall more prevalent in the U.S. from late January to March but were not geographically uniform. In January and February, facets of social distancing appeared in Los Angeles, San Francisco, and Seattle, which were among the first cities impacted by the COVID-19 outbreak. Tweets related to the "implementation" and "negative emotions" facets of social distancing largely dominated in combination with topics of "social disruption" and "adaptation", albeit to a lesser degree. Social distancing can be defined in terms of facets that respond and represent certain moments and events in a pandemic, including travel restrictions and rising COVID-19 case counts. For example, in February, Miami, FL had a low volume of social distancing tweets but grew in March which corresponded with the rise of COVID-19 cases in the city. This suggests that overall volume of social distancing tweets can reflect the relative case count in respective locations.

Title: COVID-19 and social distancing Content: AIM: COVID-19 has spread rapidly worldwide since it began, greatly affecting peoples' lives, social economies and medical systems. At present, little is known about the disease, and vaccines are still under development. Therefore, in the face of severe outbreaks, previous effective experience can help people better protect themselves and their families. The aim of this article is to discuss the social distancing measures for COVID-19. SUBJECTS AND METHODS: Literature and document search. RESULTS: Recent research and a novel coronavirus pneumonia prediction model revealed social distancing measures and wearing masks are required to mitigate hospital system overload and prevent pathogen exposure. After a series of social distancing measures, there are 309 cities with zero cases and 34 cities with confirmed cases in China as of April 13, 2020. CONCLUSION: From China's experience with novel coronavirus pneumonia, we know that social distancing is the most effective measure at present. We need to win more time to allow limited medical resources to save lives.

Title: Modeling the dynamics of COVID19 spread during and after social distancing Content: Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of COVID19. In the United States, strict social distancing has resulted in different types infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. While these plateau dynamics cannot be readily reproduced with standard SIR infection models, we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a degree of 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a potential second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

Title: Social Distancing During the COVID‐19 Pandemic: Who Are the Present and Future Non‐compliers? Content: Social distancing is an effective means for containing the spread of COVID‐19, but only so if we all participate. Who are the individuals who are the least likely to adhere to social distancing recommendations, presently and in the long‐term? Such knowledge is important for policymakers looking to sustain the public's buy‐in to social distancing. Using survey data from sample U.S. residents (n = 1,449), we show that some demographic factors (gender, age, race, political party) help predict intent to adhere to social distancing. Yet demographic factors are relatively poor predictors compared to individual attitudes and media diets. We argue that public officials should make efforts to inform and persuade the public of the importance of social distancing, targeting mediums like TV and radio where audiences are less likely to currently engage in social distancing or are less likely to envision themselves sustaining strict social distancing for several weeks or months. This article is protected by copyright. All rights reserved.

Title: The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis Content: Following the introduction of unprecedented “stay-at-home” national policies, the COVID-19 pandemic recently started declining in Europe. Our research aims were to characterize the changepoint in the flow of the COVID-19 epidemic in each European country and to evaluate the association of the level of social distancing with the observed decline in the national epidemics. Interrupted time series analyses were conducted in 28 European countries. Social distance index was calculated based on Google Community Mobility Reports. Changepoints were estimated by threshold regression, national findings were analyzed by Poisson regression, and the effect of social distancing in mixed effects Poisson regression model. Our findings identified the most probable changepoints in 28 European countries. Before changepoint, incidence of new COVID-19 cases grew by 24% per day on average. From the changepoint, this growth rate was reduced to 0.9%, 0.3% increase, and to 0.7% and 1.7% decrease by increasing social distancing quartiles. The beneficial effect of higher social distance quartiles (i.e., turning the increase into decline) was statistically significant for the fourth quartile. Notably, many countries in lower quartiles also achieved a flat epidemic curve. In these countries, other plausible COVID-19 containment measures could contribute to controlling the first wave of the disease. The association of social distance quartiles with viral spread could also be hindered by local bottlenecks in infection control. Our results allow for moderate optimism related to the gradual lifting of social distance measures in the general population, and call for specific attention to the protection of focal micro-societies enriching high-risk elderly subjects, including nursing homes and chronic care facilities.

Title: The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis Content: Background The novel coronavirus COVID19 has been classified by the World Health Organisation as a pandemic due to its worldwide spread. The ability of countries to contain and control transmission is critical in the absence of a vaccine. We evaluated a range of social distancing measures to determine which strategies are most effective in reducing the peak daily infection rate, and consequential pressure on the health care system. Methods Using COVID19 transmission data from the outbreak source in Hubei Province, China, collected prior to activation of containment measures, we adapted an established individual based simulation model of the city of Newcastle, Australia, population 272,409. Simulation of virus transmission in this community model without interventions provided a baseline from which to compare alternative social distancing strategies. The infection history of each individual was determined, as was the time infected. From this model generated data, the rate of growth in cases, the magnitude of the epidemic peak, and the outbreak duration were obtained. Findings The application of all four social distancing interventions: school closure, workplace non-attendance, increased case isolation, and community contact reduction is highly effective in flattening the epidemic curve, reducing the maximum daily case numbers, and lengthening outbreak duration. These were also found to be effective even after 10 weeks delay from index case arrivals. The most effective single intervention was found to be increasing case isolation, to 100 percent of children and 90 percent of adults. Interpretation As strong social distancing intervention strategies had the most effect in reducing the epidemic peak, this strategy may be considered when weaker strategies are first tried and found to be less effective. Questions arise as to the duration of strong social distancing measures, given they are highly disruptive to society. Tradeoffs may need to be made between the effectiveness of social distancing strategies and population willingness to adhere to them.

Title: COVID-19 social distancing in the Kingdom of Saudi Arabia: Bold measures in the face of political, economic, social and religious challenges Content: Abstract Social distancing at its various levels has been a key measure to mitigate the transmission of COVID-19. The implementation of strict measures for social distancing is challenging, including in the Kingdom of Saudi Arabia (KSA) due to its level of urbanizations, its social and religious norms and its annual hosting of high visibility international religious mass gatherings. KSA started introducing decisive social distancing measures early before the first case of COVID-19 was confirmed in the Kingdom. These ranged from suspension or cancelations of religious, entertainment and sporting mass gatherings and events such as the Umrah, shutting of educational establishments and mosques and postponing all non-essential gatherings, to imposing a partial curfew. These measures were taken in spite of their socio-economic, political and religious challenges in the interest of public and global health. The effect of these actions on the epidemic curve of the Kingdom and on the global fight against COVID-19 remains to be seen. However, given the current COVID-19 situation, further bold and probably unpopular measures are likely to be introduced in the future.

Title: The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis Content: Following the introduction of unprecedented "stay-at-home" national policies, the COVID-19 pandemic recently started declining in Europe. Our research aims were to characterize the changepoint in the flow of the COVID-19 epidemic in each European country and to evaluate the association of the level of social distancing with the observed decline in the national epidemics. Interrupted time series analyses were conducted in 28 European countries. Social distance index was calculated based on Google Community Mobility Reports. Changepoints were estimated by threshold regression, national findings were analyzed by Poisson regression, and the effect of social distancing in mixed effects Poisson regression model. Our findings identified the most probable changepoints in 28 European countries. Before changepoint, incidence of new COVID-19 cases grew by 24% per day on average. From the changepoint, this growth rate was reduced to 0.9%, 0.3% increase, and to 0.7% and 1.7% decrease by increasing social distancing quartiles. The beneficial effect of higher social distance quartiles (i.e., turning the increase into decline) was statistically significant for the fourth quartile. Notably, many countries in lower quartiles also achieved a flat epidemic curve. In these countries, other plausible COVID-19 containment measures could contribute to controlling the first wave of the disease. The association of social distance quartiles with viral spread could also be hindered by local bottlenecks in infection control. Our results allow for moderate optimism related to the gradual lifting of social distance measures in the general population, and call for specific attention to the protection of focal micro-societies enriching high-risk elderly subjects, including nursing homes and chronic care facilities.

Title: Social distancing, population density, and spread of COVID-19 in England: a longitudinal study. Content: BACKGROUND The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ≥65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.

Title: Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study Content: BACKGROUND: Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. METHODS: To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. RESULTS: At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. CONCLUSIONS: Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

Title: Adoption and impact of non-pharmaceutical interventions for COVID-19 Content: Background: Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19. Methods: We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources. Results: Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs. Conclusions: The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies.

Title: COVID-19 social distancing in the Kingdom of Saudi Arabia: Bold measures in the face of political, economic, social and religious challenges Content: Social distancing at its various levels has been a key measure to mitigate the transmission of COVID-19. The implementation of strict measures for social distancing is challenging, including in the Kingdom of Saudi Arabia (KSA) due to its level of urbanization, its social and religious norms and its annual hosting of high visibility international religious mass gatherings. KSA started introducing decisive social distancing measures early before the first case of COVID-19 was confirmed in the Kingdom. These ranged from suspension or cancelations of religious, entertainment and sporting mass gatherings and events such as the Umrah, temporary closure of educational establishments and mosques and postponing all non-essential gatherings, to imposing a curfew. These measures were taken in spite of their socio-economic, political and religious challenges in the interest of public and global health. The effect of these actions on the epidemic curve of the Kingdom and on the global fight against COVID-19 remains to be seen. However, given the current COVID-19 situation, further bold and probably unpopular measures are likely to be introduced in the future.

Title: A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing Content: Motivated by the current COVID-19 epidemic, this work introduces an epidemiological model in which separate compartments are used for susceptible and asymptomatic "socially distant" populations. Distancing directives are represented by rates of flow into these compartments, as well as by a reduction in contacts that lessens disease transmission. The dynamical behavior of this system is analyzed, under various different rate control strategies, and the sensitivity of the basic reproduction number to various parameters is studied. One of the striking features of this model is the existence of a critical implementation delay in issuing separation mandates: while a delay of about four weeks does not have an appreciable effect, issuing mandates after this critical time results in a far greater incidence of infection. In other words, there is a nontrivial but tight "window of opportunity" for commencing social distancing. Different relaxation strategies are also simulated, with surprising results. Periodic relaxation policies suggest a schedule which may significantly inhibit peak infective load, but that this schedule is very sensitive to parameter values and the schedule's frequency. Further, we considered the impact of steadily reducing social distancing measures over time. We find that a too-sudden reopening of society may negate the progress achieved under initial distancing guidelines, if not carefully designed.

Title: Effectiveness and Compliance to Social Distancing During COVID-19 Content: In the absence of pharmaceutical interventions to curb the spread of COVID-19, countries relied on a number of nonpharmaceutical interventions to fight the first wave of the pandemic. The most prevalent one has been stay-at-home orders, whose the goal is to limit the physical contact between people, which consequently will reduce the number of secondary infections generated. In this work, we use a detailed set of mobility data to evaluate the impact that these interventions had on alleviating the spread of the virus in the US as measured through the COVID-19-related deaths. To establish this impact, we use the notion of Granger causality between two time-series. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks. We further analyze the mobility patterns at the census block level to identify which parts of the population might encounter difficulties in adhering and complying with social distancing measures. This information is important, since it can consequently drive interventions that aim at helping these parts of the population.

Title: Effect of social distancing on COVID-19 incidence and mortality in the US Content: Social distancing policies were implemented in most US states as a containment strategy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The effectiveness of these policy interventions on morbidity and mortality remains unknown. Our analysis examined the associations between statewide policies and objective measures of social distancing, and objective social distancing and COVID-19 incidence and mortality. We used nationwide, de-identified smartphone GPS data to estimate county-level social distancing. COVID-19 incidence and mortality data were from the Johns Hopkins Coronavirus Resource Center. Generalized linear mixed models were used to estimate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between objective social distancing and COVID-19 incidence and mortality. Stay-at-home orders were associated with a 35% increase in social distancing. Higher social distancing was associated with a 29% reduction in COVID-19 incidence (adjusted IRR 0.71; 95% CI 0.57–0.87) and a 35% reduction in COVID-19 mortality (adjusted IRR 0.65; 95% CI 0.55–0.76). These findings provide evidence to inform ongoing national discussions on the effectiveness of these public health measures and the potential implications of returning to normal social activity.

Title: Quantifying what could have been - The impact of the Australian and New Zealand governments' response to COVID-19 Content: BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Title: The term "physical distancing" is recommended rather than "social distancing" during the COVID-19 pandemic for reducing feelings of rejection among people with mental health problems Content: As COVID-19 has plagued our world, the term "social distancing" has been widely used with the aim to encourage the general population to physically distance themselves from others in order to reduce the spread of the virus. However, this term can have unintended but detrimental effects, as it evokes negative feelings of being ignored, unwelcome, left alone with one's own fears, and even excluded from society. These feelings may be stronger in people with mental illnesses and in socio-economically disadvantaged groups, such as stigmatized minorities, migrants, and homeless persons [1], many of them also having high risk for suicidal behaviors [2]. Mental health disorders are pervasive worldwide; the global burden accounting for approximately 21.2-32.4% of years lived with disability-more than any other group of illnesses [3]. So, the vulnerable group of people with mental health disorders represents a considerable share of the total global population.

Title: Neighborhood income and physical distancing during the COVID-19 pandemic in the U.S. Content: Introduction: Although physical distancing has been the primary strategy to reduce the spread of COVID-19 in the U.S., people's ability to distance may vary by socioeconomic characteristics, leading to higher transmission risk in low-income neighborhoods. Methods: We used mobility data from a large, anonymized sample of smartphone users to assess the relationship between neighborhood median household income and physical distancing during the COVID-19 epidemic. We assessed changes in several behaviors including: spending the day entirely at home; working outside the home; and visits to supermarkets, parks, hospitals, and other locations. We also assessed differences in effects of state policies on physical distancing across neighborhood income levels. Results: We found a strong gradient between neighborhood income and physical distancing. Compared to January and February 2020, the proportion of individuals spending the day entirely at home in April 2020 increased by 10.9 percentage points in low-income neighborhoods and by 27.1 percentage points in high-income neighborhoods. During April 2020, people in low-income neighborhoods were more likely to work outside the home, compared to people in higher-income neighborhoods, but not more likely to visit non-work locations. State physical distancing orders were associated with a 1.5 percentage-point increase (95% CI [0.9, 2.1], p < 0.001) in staying home in low-income neighborhoods and a 2.4 percentage point increase (95% CI [1.4, 3.4], p < 0.001) in high-income neighborhoods. Discussion: People in lower-income neighborhoods have faced barriers to physical distancing, particularly the need to work outside the home. State physical distancing policies have not mitigated these disparities.

Title: Voluntary Cyclical Distancing: A potential alternative to constant level mandatory social distancing, relying on an 'infection weather report' Content: COVID-19 has significantly changed our daily lives. Stay-at-home orders and forced closings of all non-essential businesses has had a significant impact on our economy. While it is important to ensure that the healthcare system is not overwhelmed, there are many questions that remain about the efficacy of extreme social distancing, and whether there are alternatives to mandatory lockdowns. This paper analyzes the utility of various levels of social distancing, and suggests an alternative approach using voluntary distancing informed by an infectious load index or 'infection weather report.'

Title: Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study. Content: BACKGROUND Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. METHODS To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. RESULTS At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. CONCLUSIONS Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

Title: Estimating the impact of COVID-19 control measures using a Bayesian model of physical distancing Content: Extensive physical distancing measures are currently the primary intervention against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing measures, with the timing of these measures informed by survey data on attitudes to distancing and COVID-19.We fit our model to reported COVID-19 cases in British Columbia, Canada, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimate the impact that physical distancing (also known as social distancing)has had on the contact rate and examine the projected impact of relaxing distancing measures. We find that distancing has had a strong impact, consistent with declines in reported cases and in hospitalization and intensive care unit numbers. We estimate that approximately 0.78 (0.66-0.89 90% CI) of contacts have been removed for individuals in British Columbia practising physical distancing and that this fraction is above the threshold of 0.45 at which prevalence is expected to grow. However, relaxing distancing measures beyond this threshold re-starts rapid exponential growth. Because the extent of underestimation is unknown, the data are consistent with a wide range in the prevalence of COVID-19 in the population; changes to testing criteria over time introduce additional uncertainty. Our projections indicate that intermittent distancing measures - if sufficiently strong and robustly followed - could control COVID-19 transmission, but that if distancing measures are relaxed too much, the epidemic curve would grow to high prevalence.

Title: Modelling the potential impact of social distancing on the COVID-19 epidemic in South Africa Content: The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to model the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to the currently available data on the cumulative number of infected cases and a scenario analysis on different levels of social distancing are presented. The results show a continued rise in the number of cases in the lock down period with the current levels of social distancing albeit at a lower rate. The model shows that the number of cases will rise to above 4000 cases by the end of the lockdown. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lock down measures. A relaxation of the social distancing by 2% can result in a 23% rise in the number of cumulative cases while on the other hand increasing the levels of social distancing by 2% would reduce the number of cumulative cases by about 18%. These results have implications on the management and policy direction in the early phases of the epidemic.

Title: Proactive social distancing mitigates COVID-19 outbreaks within a month across 58 mainland China cities Content: In early 2020, cities across China enacted strict social distancing measures to contain emerging coronavirus (COVID-19) outbreaks. We estimated the speed with which these measures contained community transmission in each of 58 Chinese cities. On average, containment was achieved 7.83 days (SD 6.79 days) after the implementation of social distancing interventions, with an average reduction in the reproduction number (R(t)) of 54.3% (SD 17.6%) over that time period. A single day delay in the implementation of social distancing led to a 2.41 (95% CI: 0.97, 3.86) day delay in containment. Swift social distancing interventions may thus achieve rapid containment of newly emerging COVID-19 outbreaks.

Title: Social distancing, social justice, and risk during the COVID-19 pandemic Content: Social distancing is an important and necessary measure to help arrest the spread of SARS-CoV-2 during the COVID-19 pandemic. However, it does place persons who are socially or politically marginalized, including those who are of lower socio-economic status, at risk of further harms. In other words, marginalized or disadvantaged persons are at risk of both contracting SARS-CoV-2 and the risk of harms that may come about because of the social distancing measures themselves. Finally, a third layer of risk faced by marginalized persons would be the overuse of utility (i.e., maximize the benefit of resource x) as the primary ethics principle upon which to make allocation decisions, since oftentimes it is resource-intensive to help those in positions of social marginality. This three-fold risk of harm to which marginalized persons are subjected runs counter to the very notion of social justice that underpins public health. Social distancing in a socially just manner requires dialoguing with affected populations and providing social supports to marginalized persons, regardless of the associated costs.

Title: Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China Content: Intense non-pharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0-14 years are less susceptible to SARS-CoV-2 infection than adults 15-64 years of age (odd ratio 0.34, 95%CI 0.24-0.49), while in contrast, individuals over 65 years are more susceptible to infection (odd ratio 1.47, 95%CI: 1.12-1.92). Based on these data, we build a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40-60% and delay the epidemic.

Title: Impact of social distancing measures for preventing coronavirus disease 2019 : A systematic review and meta-analysis protocol Content: Abstract Introduction: Social distancing measures (SDMs) protect public health from the outbreak of coronavirus disease 2019 (COVID-19). However, the impact of SDMs has been inconsistent and unclear. This study aims to assess the effects of SDMs (e.g. isolation, quarantine) for reducing the transmission of COVID-19. Methods and analysis: We will conduct a systematic review meta-analysis research of both randomised controlled trials and non-randomised controlled trials. We will search MEDLINE, EMBASE, Allied & Complementary Medicine, COVID-19 Research and WHO database on COVID-19 for primary studies assessing the enablers and barriers associated with SDMs, and will be reported in accordance with PRISMA statement. The PRISMA-P checklist will be used while preparing this protocol. We will use Joanna Briggs Institute guidelines (JBI Critical Appraisal Checklists) to assess the methodological qualities and synthesised performing thematic analysis. Two reviewers will independently screen the papers and extracted data. If sufficient data are available, the random-effects model for meta-analysis will be performed to measure the effect size of SDMs or the strengths of relationships. To assess the heterogeneity of effects, I2 together with the observed effects (Q-value, with degrees of freedom) will be used to provide the true effects in the analysis. Ethics and dissemination: Ethics approval and consent will not be required for this systematic review of the literature as it does not involve human participation. We will be able to disseminate the study findings using the following strategies: we will be publishing at least one paper in peer-reviewed journals, and an abstract will be presented at suitable national/international conferences or workshops. We will also share important information with public health authorities as well as with the World Health Organization.

Title: Changes in Subway Ridership in Response to COVID-19 in Seoul, South Korea: Implications for Social Distancing Content: Introduction While numerous episodes of Coronavirus disease 2019 (COVID-19) infection and subsequent government announcements in South Korea were accompanied by widespread social distancing efforts by the people, it is unclear whether these episodes and government announcements were actually influential in improving social distancing, or whether the level of response among different demographic groups varied. Methods Data were downloaded from Seoul Data Open Plaza, and changes in the number of passengers on the Seoul Metropolitan Subway network between January 1, 2020, and March 31, 2020, were used to assess the extent to which people in Seoul practiced social distancing. Five events regarding COVID-19 that received wide public attention between January and March 2020 were identified and the changes in the number of passengers before and after each event were analyzed. Also, similar analyses were performed for 16 stations that were specific in either the age or purpose of the visit of the passengers. Results Compared to the third week of January 2020 (January 13-19), the mean daily number of passengers in all stations decreased by 2,984,857.4 or 40.6% by the first week of March (March 2-8). The percentage decrease in individual stations between this period was not significantly different between “young” and “old” stations (46.3% vs. 49.2%; p = 0.551) but was significantly smaller in “work” stations than in “leisure” stations (36.2% vs. 51.6%; p = 0.021). Of the five events, the first reported death due to COVID-19 in South Korea and the identification of a mass infection cluster in Daegu on February 20 were accompanied by the greatest decrease of the mean daily number of passengers (1,352,153.3 or 20.8%), while the first mass infection in Seoul on March 10 and the announcement of aggressive social distancing campaign on March 22 were accompanied by an increase in the number of passengers. Conclusions The number of subway passengers in Seoul decreased markedly during late February but slowly increased afterward, suggesting decreasing levels of risk perception and adherence to social distancing. Understanding the differing patterns of subway use by age or purpose of the visit may guide policymakers and the general public in shaping their future response to the current pandemic.

Title: Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak Content: Governments around the world have enacted strict social distancing policies in order to slow the spread of COVID-19. The next step is figuring out when to relax these restrictions and to what degree. Our results predict potentially disastrous implications of ending these policies too soon, based on projections made from a Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. Even when infection rates appear to be slowing down or decreasing, prematurely returning to "business as usual" produces a severe second peak far worse than the first. Furthermore, such a second peak is made more likely when very severe restrictions are initially enacted. Only an appropriately measured and committed set of restrictions can appropriately control COVID-19 outbreak levels.

Title: Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29(th)–March 30(th), 2020. Content: The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15(th), 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6(th), 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30(th), 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Title: Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th–March 30th, 2020. Content: The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Title: Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020 Content: The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Title: Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: A UK-based focus group study Content: OBJECTIVE: Explore the perceptions and experiences of the UK public of social distancing and social isolation measures related to the COVID-19 pandemic. DESIGN: Qualitative study comprising five focus groups carried out online during the early stages of the UK's social distancing and isolation measures (5-12 days post lockdown). SETTING: Online video-conferencing PARTICIPANTS: 27 participants, all UK residents aged 18 years and older, representing a range of gender, ethnic, age and occupational backgrounds. RESULTS: The social distancing and isolation associated with COVID-19 policy has had having substantial negative impacts on the mental health and wellbeing of the UK public within a short time of policy implementation. It has disproportionately negatively affected those in low-paid or precarious employment. Practical social and economic losses - the loss of (in-person) social interaction, loss of income and loss of structure and routine - led to psychological and emotional 'losses' - the loss of motivation, loss of meaning, and loss of self-worth. Participants reported high adherence to distancing and isolation guidelines but reported seeing or hearing of non-adherence in others. A central concern for participants was the uncertainty duration of the measures, and their ability to cope longer-term. Some participants felt they would have lingering concerns over social contact while others were eager to return to high levels of social activity. CONCLUSIONS: A rapid response is necessary in terms of public health programming to mitigate the mental health impacts of COVID-19 social distancing and isolation. Initial high levels of support for, and adherence to, social distancing and isolation is likely to wane over time, particularly where end dates are uncertain. Social distancing and isolation 'exit strategies' must account for the fact that, although some individuals will voluntarily or habitually continue to socially distance, others will seek high levels of social engagement as soon as possible.

Title: Modeling COVID19 mortality in the US: Community context and mobility matter Content: The United States has become an epicenter for the coronavirus disease 2019 (COVID-19) pandemic. However, communities have been unequally affected and evidence is growing that social determinants of health may be exacerbating the pandemic. Furthermore, the impact and timing of social distancing at the community level have yet to be fully explored. We investigated the relative associations between COVID-19 mortality and social distancing, sociodemographic makeup, economic vulnerabilities, and comorbidities in 24 counties surrounding 7 major metropolitan areas in the US using a flexible and robust time series modeling approach. We found that counties with poorer health and less wealth were associated with higher daily mortality rates compared to counties with fewer economic vulnerabilities and fewer pre-existing health conditions. Declines in mobility were associated with up to 15% lower mortality rates relative to pre-social distancing levels of mobility, but effects were lagged between 25-30 days. While we cannot estimate causal impact, this study provides insight into the association of social distancing on community mortality while accounting for key community factors. For full transparency and reproducibility, we provide all data and code used in this study.

Title: COVID-19 Growth Rate Decreases with Social Capital Content: Background: Social capital has been associated with many public health variables including mortality, obesity, diabetes, and sexually-transmitted disease rates. However, the relationship of social capital to the spread of infectious disease like COVID-19 is lacking. The COVID-19 pandemic presents an unprecedented threat to global health and economy, for which control strategies have relied on aggressive social distancing. However, an understanding of how social capital is related to changes in human mobility patterns for adherence to social distancing is lacking. Objective: This study examines the association between state- and county-level social capital indices and community health indices in the United States, and the growth rate of COVID-19 cases. It also examines changes in human mobility. Methods: Using publicly available state- and county-specific time series data for COVID-19 cases from March 13 to March 31, we used exponential fits to determine growth rate. We obtained publicly available mobility change data, originally measured from GPS-enabled mobile devices. The design was then state- and county-level correlation analysis with social capital and community health indices from the Social Capital Project (United States Senate). Results: In bivariate linear correlation analyses, we find social capital and community health indices were negatively associated with COVID-19 growth rates at both the state and county levels. The correlation was strongest at the county level for the community health index: a one-unit increase in the county community health index was associated with a decrease in the COVID-19 growth rate exponent by 0.045. In further bivariate correlation analyses, we find that social capital indices were negatively associated with retail/recreation movement and positively associated with residential movement. That is, an increase in social capital is correlated with slower COVID-19 infection spread and more adherence to social distancing protocols. Conclusion: Our results indicate the potential benefit of incorporating social capital concepts in planning policies to control the spread of COVID-19, e.g. different social distancing requirements in different communities. The results also indicate a need for further research into this potentially causal relationship, including examining interventions to increase social capital, community health, and institutional health.

Title: A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment : The case of Jakarta, Indonesia Content: The aim of this study is to investigate the effects of rapid testing and social distancing in controlling the spread of COVID-19, particularly in the city of Jakarta, Indonesia. We formulate a modified susceptible exposed infectious recovered compartmental model considering asymptomatic individuals. Rapid testing is intended to trace the existence of asymptomatic infected individuals among the population. This asymptomatic class is categorized into two subclasses: detected and undetected asymptomatic individuals. Furthermore, the model considers the limitations of medical resources to treat an infected individual in a hospital. The model shows two types of equilibrium point: COVID-19 free and COVID-19 endemic. The COVID-19-free equilibrium point is locally and asymptotically stable if the basic reproduction number [Formula: see text] is less than unity. In contrast, COVID-19-endemic equilibrium always exists when [Formula: see text]. The model can also show a backward bifurcation at [Formula: see text] whenever the treatment saturation parameter, which describes the hospital capacity, is larger than a specific threshold. To justify the model parameters, we use the incidence data from the city of Jakarta, Indonesia. The data pertain to infected individuals who self-isolate in their homes and visit the hospital for further treatment. Our numerical experiments indicate that strict social distancing has the potential to succeed in reducing and delaying the time of an outbreak. However, if the strict social distancing policy is relaxed, a massive rapid-test intervention should be conducted to avoid a large-scale outbreak in the future.

Title: Social Media and COVID-19: Can Social Distancing be Quantified without Measuring Human Movements? Content: The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (P<0.05) between our findings and the Google social distancing report.

Title: A Statistical Model for Quantifying the Needed Duration of Social Distancing for the COVID-19 Pandemic Content: Understanding the effectiveness of strategies such as social distancing is a central question in attempts to control the COVID-19 pandemic. A key unknown in social distancing strategies is the duration of time for which such strategies are needed. Answering this question requires an accurate model of the transmission trajectory. A challenge in fitting such a model is the limited COVID-19 case data available from a given location. To overcome this challenge, we propose fitting a model of SARS-CoV-2 transmission jointly across multiple locations. We apply the model to COVID-19 case data from Spain, UK, Germany, France, Denmark, and New York to estimate the time needed for social distancing to end to be around October 2020. Our method is not specific to COVID-19, and it can also be applied to future pandemics.

Title: Impact of Social Distancing Measures on COVID-19 Healthcare Demand in Central Texas Content: A novel coronavirus (SARS-CoV-2) emerged in Wuhan, China in late 2019 and rapidly spread worldwide. In the absence of effective antiviral drugs and vaccines, well-targeted social distancing measures are essential for mitigating the COVID-19 pandemic, reducing strain on local health systems, and preventing mortality. Here, we provide a quantitative assessment of the efficacy of social distancing to slow COVID-19 transmission and reduce hospital surge, depending on the timing and extent of the measures imposed for a metropolitan region and its health care systems. We built a granular mathematical model of COVID-19 transmission that incorporated age-specific and risk-stratified heterogeneity, estimates for the transmission, and severity of COVID-19 using current best evidence. We performed thousands of stochastic simulations of COVID-19 transmission in the Austin-Round Rock Metropolitan Area to project the impact of school closures coupled with social distancing measures that were estimated to reduce non-household contacts by 0%, 25%, 50%, 75% or 90%. We compare early versus late implementation and estimate the number of COVID-19 hospitalizations, ICU patients, ventilator needs and deaths through mid-August, 2020. We queried local emergency services and hospital systems to estimate total hospital bed, ICU, and ventilator capacity for the region. We expected COVID-19 hospital beds and ICU requirements would surpass local capacity by mid-May if no intervention was taken. Assuming a four-day epidemic doubling time, school closures alone would be expected to reduce peak hospitalizations by only 18% and cumulative deaths by less than 3%. Immediate social distancing measures that reduced non-household contacts by over 75%, such as stay-at-home orders and closing of non-essential businesses, would be required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. Peak ICU bed demand prior to mid August 2020 would be expected to be reduced from 2,121 (95% CI: 2,018-2,208) with no intervention to 698 (95% CI: 204-1,100) with 75% social distancing and 136 (95% CI: 38-308) with 90% social distancing; current ICU bed capacity was estimated at 680. A two-week delay in implementation of such measures is projected to accelerate a local ICU bed shortage by four weeks. School closures alone hardly impact the epidemic curve. Immediate social distancing measures that reduce non-household contacts by over 75% were required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. These findings helped inform the Stay Home-Work Safe order enacted by the city of Austin, Texas on March 24, 2020 as a means of mitigating the emerging COVID-19 epidemic.