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

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: The Changing Face of Orthopedic Education: Searching for the New Reality After COVID-19 Content: Abstract The COVID-19 pandemic has had immediate impact on the practice of medicine and orthopaedic education. As the practice of social distancing has been put into place to help slow the spread of disease as well as to conserve medical supplies and equipment, elective surgery has come to a grinding halt. This dramatic change has forced our leaders to critically evaluate the delivery of education and skills training for our residents, fellows as well as all orthopaedic surgeons. We must continue to develop technologies such as virtual meeting platforms, distance learning, simulation-based training, virtual reality andaugmented reality to augment the new world of orthopaedic education.

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: 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: 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: Impact of self-imposed prevention measures and short-term government intervention on mitigating and delaying a COVID-19 epidemic Content: Background: With new cases of COVID-19 surging around the world, many countries have to prepare for moving beyond the containment phase. Prediction of the effectiveness of non-case-based interventions for mitigating, delaying or preventing the epidemic is urgent, especially for countries affected by the ongoing seasonal influenza activity. Methods: We developed a transmission model to evaluate the impact of self-imposed prevention measures (handwashing, mask-wearing, and social distancing) due to the spread of COVID-19 awareness and of short-term government-imposed social distancing on the peak number of diagnoses, attack rate and time until the peak number of diagnoses. Findings: For fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate, diminish and postpone the peak number of diagnoses. A large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government interventions can only delay the peak (by at most 7 months for a 3-month intervention). Interpretation: Handwashing, mask-wearing and social distancing as a reaction to information dissemination about COVID-19 can be effective strategies to mitigate and delay the epidemic. We stress the importance of rapidly spreading awareness on the use of these self-imposed prevention measures in the population. Early-initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden. Keywords: SARS-CoV-2, COVID-19, mathematical model, prevention measures, mitigation, epidemic control, disease awareness, social distancing, handwashing, mask-wearing

Title: Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life Content: The COVID-19 pandemic has resulted in the widespread implementation of extraordinary physical distancing interventions (e.g., stay-at-home orders) to slow the spread of the virus. Although vital, these interventions may be socially and economically disruptive, contributing to adverse psychological outcomes. This study examined relations of both stay-at-home orders and the perceived impact of COVID-19 on daily life to psychological outcomes (depression, health anxiety, financial worry, social support, and loneliness) in a nationwide U.S. community adult sample (N = 500; 47% women, mean age = 40). Participants completed questionnaires assessing psychological outcomes, stay-at-home order status, and COVID-19’s impact on their daily life. Being under a stay-at-home order was associated with greater health anxiety, financial worry, and loneliness. Moreover, the perceived impact of COVID-19 on daily life was positively associated with health anxiety, financial worry, and social support, but negatively associated with loneliness. Findings highlight the importance of social connection to mitigate negative psychological consequences of the COVID-19 pandemic.

Title: Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life Content: The COVID-19 pandemic has resulted in the widespread implementation of extraordinary physical distancing interventions (e.g., stay-at-home orders) to slow the spread of the virus. Although vital, these interventions may be socially and economically disruptive, contributing to adverse psychological outcomes. This study examined relations of both stay-at-home orders and the perceived impact of COVID-19 on daily life to psychological outcomes (depression, health anxiety, financial worry, social support, and loneliness) in a nationwide U.S. community adult sample (N = 500; 47% women, mean age = 40). Participants completed questionnaires assessing psychological outcomes, stay-at-home order status, and COVID-19's impact on their daily life. Being under a stay-at-home order was associated with greater health anxiety, financial worry, and loneliness. Moreover, the perceived impact of COVID-19 on daily life was positively associated with health anxiety, financial worry, and social support, but negatively associated with loneliness. Findings highlight the importance of social connection to mitigate negative psychological consequences of the COVID-19 pandemic.

Title: Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions Content: When 2020 began, we had no idea what was to unfold globally as we learnt about the Novel-Coronavirus in Wuhan, in the Hubei province of China. As this virus spread rapidly, it became a matter of time before many countries began to implement measures to try and contain the spread of the disease. COVID-19 as it is referred to, resulted in two main approaches to fighting the viral pandemic, either through a progressive set of measures to slow down the number of identified cases designed to ‘flatten the curve’ over time (anticipated to be at least six months), or to attack it by the severest of measures including a total lock-down and/or herding exposure to fast track ‘immunisation’ while we await a vaccine. The paper reports the findings from the first phase of an ongoing survey designed to identify the changing patterns in travel activity of Australian residents as a result of the stage 2 restrictions imposed by the Australian government. The main restrictions, in addition to social distancing of at least 1.5 m, are closure of entry to Australia (except residents returning), and closure of non-essential venues such as night clubs, restaurants, mass attendee sporting events, churches, weddings, and all social gatherings in any circumstance. With some employers encouraging working from home and others requiring it, in addition to job losses, and many children attending school online from home, the implications on travel activity is extreme. We identify the initial impacts associated with the first month of stricter social distancing measures introduced in Australia.

Title: Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France Content: Most European countries have responded to the COVID-19 threat by nationwide implementation of barrier measures and lockdown. However, assuming that population immunity will build up through the epidemic, it is likely to rebound once these measures are relaxed, possibly leading to a second or multiple repeated lockdowns. In this report, we present results of epidemiological modelling that has helped inform policy making in France. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in France, and examined the potential impact of post-quarantine measures, including social distancing, mask-wearing, and shielding of the population the most vulnerable to severe COVID-19 infection, on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is effective in containing the viral spread, it would be unlikely to prevent a rebound of the epidemic once lifted, regardless of its duration. Both social distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing the overwhelming of ICUs and a second lockdown. However, these measures coupled with shielding of vulnerable people would be associated with better outcomes, including lower cumulative incidence, mortality, and maintaining an adequate number of ICU beds to prevent a second lockdown. Benefits would nonetheless be markedly reduced if these measures were not applied by most people or not maintained for a sufficiently long period, as herd immunity progressively establishes in the less vulnerable population.

Title: The projected impact of mitigation and suppression strategies on the COVID-19 epidemic in Senegal: A modelling study Content: Background: Physical distancing measures that reduce social contacts have formed a key part of national COVID-19 containment and mitigation strategies. Many Sub-Saharan African nations are now facing increasing numbers of cases of COVID-19 and there is a need to understand what levels of measures may be required to successfully reduce transmission. Methods: We collated epidemiological data along with information on key COVID-19 specific response policies and health system capacity estimates for services needed to treat COVID-19 patients in Senegal. We calibrated an age-structured SEIR model to these data to capture transmission dynamics accounting for demography, contact patterns, hospital capacity and disease severity. We simulated the impact of mitigation and suppression strategies focussed on reducing social contact rates. Results: Senegal acted promptly to contain the spread of SARS-CoV-2 and as a result has reduced the reproduction number from 1.9 (95% CI 1.7-2.2) to 1.3 (95% CI 1.2-1.5), which has slowed but not fully interrupted transmission. We estimate that continued spread is likely to peak in October, and to overwhelm the healthcare system with an estimated 77,400 deaths(95% CI 55,270-100,700). Further reductions in contact rates to suppress transmission (Rt<1) could significantly reduce this burden on healthcare services and improve overall health outcomes. Conclusions: Our results demonstrate that Senegal has already significantly reduced transmission. Enhanced physical distancing measures and rapid scale up of hospital capacity is likely to be needed to reduce mortality and protect healthcare infrastructure from high levels of demand.

Title: Releasing the lockdown in the UK Covid-19 epidemic: a stochastic model Content: Abstract Background In a classic epidemic, the infected population has an early exponential phase, before slowing and fading to its peak. Mitigating interventions may change the exponent during the rising phase and a plateau can replace a peak. With interventions comes the risk that relaxation causes a second-wave. In the UK Covid-19 epidemic, infections cannot be counted, but their influence is seen in the curve of the mortality data. This work simulated social distancing and the lockdown in the UK Covid-19 epidemic to explore strategies for relaxation. Methods Cumulative mortality data was transposed 20 days earlier to identify three doubling periods separated by the 17th March - social distancing, and 23rd March - lockdown. A set of stochastic processes simulated viral transmission between interacting individuals using Covid-19 incubation and illness durations. Social distancing and restrictions on interactions were imposed and later relaxed. Principal Findings Daily mortality data, consistent with that seen in the UK Covid-19 epidemic to 24th April 2020 was simulated. This output predicts that under a lockdown maintained till early July 2020, UK deaths will exceed 31,000, but leave a large susceptible population and a requirement for vaccination or quarantine. An earlier staged relaxation carries a risk of a second-wave. The model allows exploration of strategies for lifting the lockdown. Interpretation Social distancing and the lockdown have had an impressive impact on the UK Covid-19 epidemic and saved lives, caution is now needed in planning its relaxation.

Title: Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions Content: The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the US and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

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: Determining the optimal strategy for reopening schools, work and society in the UK: balancing earlier opening and the impact of test and trace strategies with the risk of occurrence of a secondary COVID-19 pandemic wave Content: Background In order to slow down the spread of SARS-CoV-2, the virus causing the COVID-19 pandemic, the UK government has imposed strict physical distancing (lockdown) measures including school 'dismissals' since 23 March 2020. As evidence is emerging that these measures may have slowed the spread of the pandemic, it is important to assess the impact of any changes in strategy, including scenarios for school reopening and broader relaxation of social distancing. This work uses an individual-based model to predict the impact of a suite of possible strategies to reopen schools in the UK, including that currently proposed by the UK government. Methods We use Covasim, a stochastic agent-based model for transmission of COVID-19, calibrated to the UK epidemic. The model describes individuals' contact networks stratified as household, school, work and community layers, and uses demographic and epidemiological data from the UK. We simulate a range of different school reopening strategies with a society-wide relaxation of lockdown measures and in the presence of different non-pharmaceutical interventions, to estimate the number of new infections, cumulative cases and deaths, as well as the effective reproduction number with different strategies. To account for uncertainties within the stochastic simulation, we also simulated different levels of infectiousness of children and young adults under 20 years old compared to older ages. Findings We found that with increased levels of testing of people (between 25% and 72% of symptomatic people tested at some point during an active COVID-19 infection depending on scenarios) and effective contact-tracing and isolation for infected individuals, an epidemic rebound may be prevented across all reopening scenarios, with the effective reproduction number (R) remaining below one and the cumulative number of new infections and deaths significantly lower than they would be if testing did not increase. If UK schools reopen in phases from June 2020, prevention of a second wave would require testing 51% of symptomatic infections, tracing of 40% of their contacts, and isolation of symptomatic and diagnosed cases. However, without such measures, reopening of schools together with gradual relaxing of the lockdown measures are likely to induce a secondary pandemic wave, as are other scenarios for reopening. When infectiousness of <20 year olds was varied from 100% to 50% of that of older ages, our findings remained unchanged. Interpretation To prevent a secondary COVID-19 wave, relaxation of social distancing including reopening schools in the UK must be implemented alongside an active large-scale population-wide testing of symptomatic individuals and effective tracing of their contacts, followed by isolation of symptomatic and diagnosed individuals. Such combined measures have a greater likelihood of controlling the transmission of SARS-CoV-2 and preventing a large number of COVID-19 deaths than reopening schools and society with the current level of implementation of testing and isolation of infected individuals.

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: The effect of COVID-19 and subsequent social distancing on travel behavior Content: Abstract The spread of the COVID-19 virus has resulted in unprecedented measures restricting travel and activity participation in many countries. Social distancing, i.e., reducing interactions between individuals in order to slow down the spread of the virus, has become the new norm. In this viewpoint I will discuss the potential implications of social distancing on daily travel patterns. Avoiding social contact might completely change the number and types of out-of-home activities people perform, and how people reach these activities. It can be expected that the demand for travel will reduce and that people will travel less by public transport. Social distancing might negatively affect subjective well-being and health status, as it might result in social isolation and limited physical activity. As a result, walking and cycling, recreationally or utilitarian, can be important ways to maintain satisfactory levels of health and well-being. Policymakers and planners should consequently try to encourage active travel, while public transport operators should focus on creating ways to safely use public transport.

Title: Evaluation of Turkish social distancing measures on the spread of COVID-19 Content: The coronavirus disease (COVID-19) affecting across the globe. The government of different countries has adopted various policies to contain this epidemic and the most common were social distancing and lockdown. We use a simple log-linear model with intercept and trend break to evaluate whether the measures are effective preventing/slowing down the spread of the disease in Turkey. We estimate the model parameters from the Johns Hopkins University (2020) epidemic data between 15th March and 16th April 2020. Our analysis revealed that the measures can slow down the outbreak. We can reduce the epidemic size and prolong the time to arrive at the epidemic peak by seriously following the measures suggested by the authorities.

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: 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.

Title: On the impact of early non-pharmaceutical interventions as containment strategies against the COVID-19 pandemic Content: Background The novel coronavirus SARS-CoV-2 (COVID-19) emerged in December 2019 in Wuhan, China and has spread since then to around 210 countries and territories by April 2020. Consequently, countries have adopted physical distance measures in an attempt to mitigate the uncontrolled spread of the virus. A critical question for policymakers to inform evidence-based practice is if and how physical distance measures slowed the propagation of COVID-19 in the early phase of the pandemic. Methods This study aims to quantify the effects of physical distance mitigation measures on the propagation of the COVID-19 pandemic. Data from John Hopkins University on confirmed cases and testing data from the Our World in Data were used in an interrupted time series analysis to estimate the effects of physical distance measures on the growth rates of the pandemic in 12 countries of Asia, Africa, and Europe. Findings We found that physical distance measures produced a significant decrease in the growth rates of the COVID-19 pandemic in five countries (Austria, Belgium, Italy, Malaysia, and South Korea). The test-positivity rate was significant in understanding the slowing growth rate of COVID-19 cases caused by the mitigation measures, as it provides important context that is missing from analysis based only on confirmed case data. Interpretation Physical distance interventions effectively slowed the progression of the COVID-19 pandemic. The results of this study could inform infectious disease mitigation policies based on physical distance measures by quantifying the differential health outcomes of a pandemic with and without physical distance interventions.

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: Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020. Content: Community mitigation activities (also referred to as nonpharmaceutical interventions) are actions that persons and communities can take to slow the spread of infectious diseases. Mitigation strategies include personal protective measures (e.g., handwashing, cough etiquette, and face coverings) that persons can use at home or while in community settings; social distancing (e.g., maintaining physical distance between persons in community settings and staying at home); and environmental surface cleaning at home and in community settings, such as schools or workplaces. Actions such as social distancing are especially critical when medical countermeasures such as vaccines or therapeutics are not available. Although voluntary adoption of social distancing by the public and community organizations is possible, public policy can enhance implementation. The CDC Community Mitigation Framework (1) recommends a phased approach to implementation at the community level, as evidence of community spread of disease increases or begins to decrease and according to severity. This report presents initial data from the metropolitan areas of San Francisco, California; Seattle, Washington; New Orleans, Louisiana; and New York City, New York* to describe the relationship between timing of public policy measures, community mobility (a proxy measure for social distancing), and temporal trends in reported coronavirus disease 2019 (COVID-19) cases. Community mobility in all four locations declined from February 26, 2020 to April 1, 2020, decreasing with each policy issued and as case counts increased. This report suggests that public policy measures are an important tool to support social distancing and provides some very early indications that these measures might help slow the spread of COVID-19.

Title: Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020 Content: Community mitigation activities (also referred to as nonpharmaceutical interventions) are actions that persons and communities can take to slow the spread of infectious diseases. Mitigation strategies include personal protective measures (e.g., handwashing, cough etiquette, and face coverings) that persons can use at home or while in community settings; social distancing (e.g., maintaining physical distance between persons in community settings and staying at home); and environmental surface cleaning at home and in community settings, such as schools or workplaces. Actions such as social distancing are especially critical when medical countermeasures such as vaccines or therapeutics are not available. Although voluntary adoption of social distancing by the public and community organizations is possible, public policy can enhance implementation. The CDC Community Mitigation Framework (1) recommends a phased approach to implementation at the community level, as evidence of community spread of disease increases or begins to decrease and according to severity. This report presents initial data from the metropolitan areas of San Francisco, California; Seattle, Washington; New Orleans, Louisiana; and New York City, New York* to describe the relationship between timing of public policy measures, community mobility (a proxy measure for social distancing), and temporal trends in reported coronavirus disease 2019 (COVID-19) cases. Community mobility in all four locations declined from February 26, 2020 to April 1, 2020, decreasing with each policy issued and as case counts increased. This report suggests that public policy measures are an important tool to support social distancing and provides some very early indications that these measures might help slow the spread of COVID-19.

Title: The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control Content: Non-pharmaceutical interventions to combat COVID-19 transmission have worked to slow the spread of the epidemic but can have high socio-economic costs. It is critical we understand the efficacy of non-pharmaceutical interventions to choose a safe exit strategy. Many current models are not suitable for assessing exit strategies because they do not account for epidemic resurgence when social distancing ends prematurely (e.g., statistical curve fits) nor permit scenario exploration in specific locations. We developed an SEIR-type mechanistic epidemiological model of COVID-19 dynamics to explore temporally variable non-pharmaceutical interventions. We provide an interactive tool and code to estimate the transmission parameter, β, and the effective reproduction number, [Formula: see text]. We fit the model to Santa Clara County, California, where an early epidemic start date and early shelter-in-place orders could provide a model for other regions. As of April 22, 2020, we estimate an R(E) of 0.982 (95% CI: 0.849 – 1.107) in Santa Clara County. After June 1 (the end-date for Santa Clara County shelter-in-place as of April 27), we estimate a shift to partial social distancing, combined with rigorous testing and isolation of symptomatic individuals, is a viable alternative to indefinitely maintaining shelter-in-place. We also estimate that if Santa Clara County had waited one week longer before issuing shelter-in-place orders, 95 additional people would have died by April 22 (95% CI: 7 – 283). Given early life-saving shelter-in-place orders in Santa Clara County, longer-term moderate social distancing and testing and isolation of symptomatic individuals have the potential to contain the size and toll of the COVID-19 pandemic in Santa Clara County, and may be effective in other locations.

Title: A Vision-based Social Distancing and Critical Density Detection System for COVID-19 Content: Social distancing has been proven as an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are not used to tracking the required 6-feet (2-meters) distance between themselves and their surroundings. An active surveillance system capable of detecting distances between individuals and warning them can slow down the spread of the deadly disease. Furthermore, measuring social density in a region of interest (ROI) and modulating inflow can decrease social distancing violation occurrence chance. On the other hand, recording data and labeling individuals who do not follow the measures will breach individuals' rights in free-societies. Here we propose an Artificial Intelligence (AI) based real-time social distancing detection and warning system considering four important ethical factors: (1) the system should never record/cache data, (2) the warnings should not target the individuals, (3) no human supervisor should be in the detection/warning loop, and (4) the code should be open-source and accessible to the public. Against this backdrop, we propose using a monocular camera and deep learning-based real-time object detectors to measure social distancing. If a violation is detected, a non-intrusive audio-visual warning signal is emitted without targeting the individual who breached the social distancing measure. Also, if the social density is over a critical value, the system sends a control signal to modulate inflow into the ROI. We tested the proposed method across real-world datasets to measure its generality and performance. The proposed method is ready for deployment, and our code is open-sourced.

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: Spread of Covid-19 in the United States is controlled Content: As of May 1, 2020, the number of cases of Covid-19 in the US passed 1,062,446, interventions to slow down the spread of Covid-19 curtailed most social activities. Meanwhile, an economic crisis and resistance to the strict intervention measures are rising. Some researchers proposed intermittent social distancing that may drive the outbreak of Covid-19 into 2022. Questions arise about whether we should maintain or relax quarantine measures. We developed novel artificial intelligence and causal inference integrated methods for real-time prediction and control of nonlinear epidemic systems. We estimated that the peak time of the Covid-19 in the US would be April 24, 2020 and its outbreak in the US will be over by the end of July and reach 1,551,901 cases. We evaluated the impact of relaxing the current interventions for reopening economy on the spread of Covid-19. We provide tools for balancing the risks of workers and reopening economy.

Title: DOUBLE POWER LAW FOR COVID-19: PREDICTION OF NEW CASES AND DEATH RATES IN ITALY AND SPAIN Content: The novel corona virus SARS-CoV-2 appeared at the end of 2019, spreading rapidly and causing a severe respiratory syndrome (COVID-19) with high mortality (2-5%). Until a vaccine or therapy is found, the most effective method of prophylaxis has been to minimize transmission via rigorous social distancing and seclusion of all but essential workers. Such measures, implemented at different times and to varying degrees worldwide, have reduced the rate of transmission compared with early phases of the pandemic, resulting in flattening of the curve followed by a gradual reduction in mortality after >6 weeks of rigorous social distancing measures. The cost of rigorous social distancing has been seen in radically reduced economic activity, job losses, disruption of schooling and social institutions. A key question facing policy makers and individuals is when to resume normal economic and social activity in the face of persistent community transmission of SARS-CoV-2 . To help address this question, we have developed a model that accurately describes the entire transmission and mortality curves in Italy and Spain, two hard-hit countries that have maintained severe social distancing measures for over 2 months. Our model quantitatively describes the rapid rise and slow decay of new cases and deaths observed under stringent social distancing (the long tail effect). We predict that even when social distancing is rigorously maintained, the number of COVID-19 deaths after peak mortality may be 2-3 times larger than the total number of deaths up to the peak. Our model has important policy implications for countries currently debating how to ease social distancing measures.

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: Agile Requirements Engineering and Software Planning for a Digital Health Platform to Engage the Effects of Isolation Caused by Social Distancing: Case Study Content: BACKGROUND: Social distancing and shielding measures have been put in place to reduce social interaction and slow the transmission of the coronavirus disease (COVID-19). For older people, self-isolation presents particular challenges for mental health and social relationships. As time progresses, continued social distancing could have a compounding impact on these concerns. OBJECTIVE: This project aims to provide a tool for older people and their families and peers to improve their well-being and health during and after regulated social distancing. First, we will evaluate the tool's feasibility, acceptability, and usability to encourage positive nutrition, enhance physical activity, and enable virtual interaction while social distancing. Second, we will be implementing the app to provide an online community to assist families and peer groups in maintaining contact with older people using goal setting. Anonymized data from the app will be aggregated with other real-world data sources to develop a machine learning algorithm to improve the identification of patients with COVID-19 and track for real time use by health systems. METHODS: Development of this project is occurring at the time of publication, and therefore, a case study design was selected to provide a systematic means of capturing software engineering in progress. The app development framework for software design was based on agile methods. The evaluation of the app's feasibility, acceptability and usability shall be conducted using Public Health England's guidance on evaluating digital health products, Bandura's model of health promotion, the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework and the Nonadoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework. RESULTS: Making use of a pre-existing software framework for health behavior change, a proof of concept was developed, and a multistage app development and deployment for the solution was created. Grant submissions to fund the project and study execution have been sought at the time of publication, and prediscovery iteration of the solution has begun. Ethical approval for a feasibility study design is being sought. CONCLUSIONS: This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. The app will be tested and evaluated in future studies to allow continuous improvement of the app. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness.

Title: Agile Requirements Engineering and Software Planning for a Digital Health Platform to Engage the Effects of Isolation Caused by Social Distancing: Case Study Content: BACKGROUND: Social distancing and shielding measures have been put in place to reduce social interaction and slow the transmission of the coronavirus disease (COVID-19). For older people, self-isolation presents particular challenges for mental health and social relationships. As time progresses, continued social distancing could have a compounding impact on these concerns. OBJECTIVE: This project aims to provide a tool for older people and their families and peers to improve their well-being and health during and after regulated social distancing. First, we will evaluate the tool’s feasibility, acceptability, and usability to encourage positive nutrition, enhance physical activity, and enable virtual interaction while social distancing. Second, we will be implementing the app to provide an online community to assist families and peer groups in maintaining contact with older people using goal setting. Anonymized data from the app will be aggregated with other real-world data sources to develop a machine learning algorithm to improve the identification of patients with COVID-19 and track for real time use by health systems. METHODS: Development of this project is occurring at the time of publication, and therefore, a case study design was selected to provide a systematic means of capturing software engineering in progress. The app development framework for software design was based on agile methods. The evaluation of the app’s feasibility, acceptability and usability shall be conducted using Public Health England's guidance on evaluating digital health products, Bandura’s model of health promotion, the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework and the Nonadoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework. RESULTS: Making use of a pre-existing software framework for health behavior change, a proof of concept was developed, and a multistage app development and deployment for the solution was created. Grant submissions to fund the project and study execution have been sought at the time of publication, and prediscovery iteration of the solution has begun. Ethical approval for a feasibility study design is being sought. CONCLUSIONS: This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. The app will be tested and evaluated in future studies to allow continuous improvement of the app. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness.

Title: Inter nation social lockdown versus medical care against COVID-19, a mild environmental insight with special reference to India Content: Infection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries. The dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229. Without much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids. Therefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection. India being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19. India has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers. Rolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high. The index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands. However, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty. It has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission. Slow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19. Therefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.

Title: Inter nation social lockdown versus medical care against COVID-19, a mild environmental insight with special reference to India Content: Abstract Infection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries. The dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229. Without much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids. Therefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection. India being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19. India has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers. Rolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high. The index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands. However, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty. It has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission. Slow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19. Therefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.

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: Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts Content: BACKGROUND: COVID-19 caused by a new form of coronavirus (SARS-CoV-2) first appeared in China end of 2019 and quickly spread to all counties of the world. To slow down the spread of the virus and to limit the pressure on the health care systems, different regulations and recommendations have been implemented by authorities, comprising amongst others the closure of all entertainment venues and social distancing. These measures have received mixed reactions, particularly from young individuals, with many not following available advice. Drawing on the information in social media discussion forums, the present study explores the reasons why people ignore the orders and recommendations of the authorities and why the authorities are unable to produce a shared sense of inclusion concerning protective measures against the COVID-19 outbreak. METHODS: Three open-access social media forums (Reddit, Twitter, and YouTube comments) were systematically searched with respect to COVID-19-related beliefs, attitudes, and behaviours of individuals. The data was retrieved in the first 3 weeks of March 2020. Qualitative document analysis and qualitative content analysis were used as the methodical approach. The data was reviewed by all authors and jointly interpreted to minimise inconsistencies. RESULTS: The study reveals that reasons such as information pollution on social media, the persistence of uncertainty about the rapidly spreading virus, the impact of the social environment on the individual, and fear of unemployment associated with inequality in the distribution of income lead people to ignore the orders and recommendations of the authorities. The findings suggest that government representatives and politicians could not produce a shared sense of inclusion concerning protective measures against the COVID-19 outbreak, due to not building trust among the public and taking concrete economic steps to satisfy them. CONCLUSION: In uncertain crises, transparency in the presentation of information and government policies emerge as influential determinants in creating social susceptibility and solidarity. The differences between social classes constitute one of the important factors that affect the decision-making mechanisms of individuals in determining the necessary steps to be undertaken in times of crisis.

Title: Senior Medical Students in the COVID‐19 Response: An Opportunity to Be Proactive Content: On March 13th, the United States (US) declared the novel coronavirus (COVID-19) pandemic a national emergency. By March 18th , according to the Centers for Disease Control and Prevention, COVID-19 had spread to all 50 US states, with 7,038 cases and 97 deaths.1 The trajectory of cases mirrors that of Italy, where doctors are forced to consider who is more deserving of a ventilator.2 In response, social distancing measures are being promoted across the US in the hopes of slowing the growth in new cases, i.e. "flattening the curve." This could maintain the demand for acute care within the healthcare system's capacity to treat.3 Travel has been curtailed, conferences and concerts cancelled, and schools and universities have moved students off campus and classes online. Medical schools are following suit, with added motivators. In canceling classes and rotations, medical schools hope to: promote social distancing, limit the risk of students contracting the virus, limit the number of healthcare workers who might spread the virus to unaffected patients, minimize the teaching burden on frontline providers, and preserve personal protective equipment (PPE) for essential personnel. These are logical reasons for removing students from hospitals. But, despite our best efforts, there may come a point in the US when, as is set to happen in Italy, medical demand outpaces medical capacity.2 If the same happens here, is there a plan in place for incorporating senior medical students into emergency relief efforts?

Title: A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions Content: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we suggest that pre-symptomatic transmission could be a significant driver of the infection spread. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China.

Title: Debate: COVID-19 and psychological well-being of children and adolescents in Italy Content: Italy was the first country in Europe to deal with COVID-19. Measures taken by the government to contain the spread of the virus were based mainly on quarantine and social distancing, with dramatic economic, social and psychological consequences. Since March, Italian children and adolescents are facing school closures, which have caused a disruption in the daily lives of millions of young people and their families. To date, despite the slow reopening, the government has decided to maintain school closures for the entire academic year, leaving the future of young people in uncertainty. There is already some evidence that quarantine and social isolation are having negative impact on children's and adolescents' psychological well-being. Moreover, this situation will mainly affect those children and adolescents with pre-existing vulnerabilities and those suffering of mental disorders. It is imperative to keep young people's needs at the core of reconstruction plans, allowing them to return to school safely, and providing them with some strategies to heal and dealing with this stressful and potentially traumatic situation.

Title: Debate: COVID-19 and psychological well-being of children and adolescents in Italy. Content: Italy was the first country in Europe to deal with COVID-19. Measures taken by the government to contain the spread of the virus were based mainly on quarantine and social distancing, with dramatic economic, social and psychological consequences. Since March, Italian children and adolescents are facing school closures, which have caused a disruption in the daily lives of millions of young people and their families. To date, despite the slow reopening, the government has decided to maintain school closures for the entire academic year, leaving the future of young people in uncertainty. There is already some evidence that quarantine and social isolation are having negative impact on children's and adolescents' psychological well-being. Moreover, this situation will mainly affect those children and adolescents with pre-existing vulnerabilities and those suffering of mental disorders. It is imperative to keep young people's needs at the core of reconstruction plans, allowing them to return to school safely, and providing them with some strategies to heal and dealing with this stressful and potentially traumatic situation.

Title: Comparative Analysis of the Application of Behavioural Insights of 33 Worldwide Governments on the Landing Pages of their COVID-19 Official Websites and their Impact on the Growth Scale of the Pandemic Content: The COVID-19 crisis has seen over a third of the world population locked down and this article has sought to understand human behaviour in response to a historical and unprecedented global pandemic. Through the analysis 18 behavioural mechanisms present on the landing pages of the websites of 33 institutional governments from March 1st til May 1st 2020 compared to the WHO data on the number of COVID-19 cases and deaths per million for each country, the authors show that a behavioural consensus was observed across all 33 countries and that Individual and Social nudges had no impact. Whilst the decisions in essentially every country on Earth, were taken with the same aim: to limit population movements and social life, two aggravating factors of the spread of the virus, only the environmental nudges effectively helped slow the virus growth scale. The authors explain the rationale behind these results and suggest that people seek information beyond governmental websites that they generally mistrust. They further suggest using Scientists as role models to encourage governmental website's traffic and designing recursive nudges to increase the impact of individual and social interventions. Together with the new phases of the spread of the virus will come new rules and guidance. Public health policies need to address behavioural change of the population on a global scale in a more targeted manner and it is hoped that this paper will provide some insight on how to do so.

Title: Challenges in control of Covid-19: short doubling time and long delay to effect of interventions Content: Early assessments of the spreading rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but more reliable inferences can now be made. Here, we estimate from European data that COVID-19 cases are expected to double initially every three days, until social distancing interventions slow this growth, and that the impact of such measures is typically only seen nine days - i.e. three doubling times - after their implementation. We argue that such temporal patterns are more critical than precise estimates of the basic reproduction number for initiating interventions. This observation has particular implications for the low-and middle-income countries currently in the early stages of their local epidemics.

Title: Challenges in control of Covid-19: short doubling time and long delay to effect of interventions Content: Early assessments of the spreading rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but more reliable inferences can now be made. Here, we estimate from European data that COVID-19 cases are expected to double initially every three days, until social distancing interventions slow this growth, and that the impact of such measures is typically only seen nine days - i.e. three doubling times - after their implementation. We argue that such temporal patterns are more critical than precise estimates of the basic reproduction number for initiating interventions. This observation has particular implications for the low- and middle-income countries currently in the early stages of their local epidemics.

Title: SHELTER IN PLACE ORDER CONTAINED COVID-19 GROWTH RATE IN GREECE Content: Background: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate. Objectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources. An interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects. Results: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003). Based on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04). However, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction. Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate. These findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.

Title: Strong Effects of Population Density and Social Characteristics on Distribution of COVID-19 Infections in the United States Content: Coronavirus disease 2019 (Covid-19) has devastated global populations and has had a large impact in the United States with the number of infections and deaths growing exponentially. Using a smooth generalized additive model with quasipoisson counts for total infections and deaths, we developed a county-level predictive model that included population demographics, social characteristics, social distancing, and testing data. This model strongly predicted the actual US distribution of Covid-19, accounting for 94.8% of spatial-temporal variation in total infections and 99.3% in Covid-19 related fatalities from March 15, 2020. US counties with higher population density, poverty index, civilian population, and minorities, especially African Americans had a higher number of confirmed infections adjusted for county population. Social distancing measured by the change in the rate of human encounter per km2 relative to pre-covid-19 national average was associated with slower rate of Covid-19 infections, whereas higher testing was associated with higher number of infections. The number of people infected was increasing, however, the rate of increase in new infections was starting to show signs of plateauing starting from the second week of April. Our model projects 2.11 million people to test positive for Covid-19 and 122,951 fatalities by June 1, 2020. Importantly, our model suggests strong social differences in the infections and deaths across US communities, and inequities in areas with larger African American minorities and higher poverty index expected to show higher rates of Covid-19 infections and deaths. Preventive steps including social distancing and community closures have been a cornerstone in stopping the transmission and potentially reducing the spread of the disease. Crucial knowledge of the role of social characteristics in the disease transmission is essential to understand current disease distribution, predict future distribution, and plan additional preventive steps.

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: 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: Decline in global COVID-19 transmission Content: We analyzed COVID-19 data through April 16, 2020 using a partially observed Markov process. Our method uses a hybrid deterministic and stochastic formalism that allows for time variable transmission rates and detection probabilities. The model was fit using iterated particle filtering to case count and death count time series from 51 countries. We found evidence for a declining transmission rate in 42 of the 51 examined countries. Of those 42 countries 34 have significant evidence for subcritical transmission rates, although the decline in new cases are relatively slow compared to the initial growth rates. This suggests that global scale social distancing efforts to slow the spread of COVID-19 are effective although they need to be strengthened in many regions and maintained in others to avoid further resurgence of COVID-19. The slow decline also suggests alternative strategies to control the virus are needed before social distancing efforts are partially relaxed.

Title: Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships Content: In the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as the human interaction within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19 using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact future epidemic curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results will establish the foundation for community-level assessment and better preparation for COVID-19.

Title: A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions Content: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we reveal that pre-symptomatic transmission accelerates the onset of the exponential growth of cases. After that, we demonstrate that the persistence of SARS-CoV-2 on hard surfaces determines the number of cases reached during the peak of the epidemic. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China.

Title: From predictions to prescriptions: A data-driven response to COVID-19 Content: The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control's pandemic forecast.

Title: From predictions to prescriptions: A data-drivenresponse to COVID-19 Content: The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to reallocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control's pandemic forecast.

Title: Impact of policy interventions and social distancing on SARS-CoV-2 transmission in the United States Content: Background: Policymakers have employed various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures to limit the spread of Coronavirus disease (COVID-19). However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states. Methods: In order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables. Results: States that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002). Discussion: Few studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics. Key words: COVID-19, SARS-CoV-2, Coronavirus, Public Policy, Social Distancing, Non-pharmaceutical Interventions, Stay-at-home Order, Shelter-in-place.

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: Tracking COVID-19 by Tracking Infectious Trajectories Content: Nowadays, the coronavirus pandemic has and is still causing large numbers of deaths and infected people. Although governments all over the world have taken severe measurements to slow down the virus spreading (e.g., travel restrictions, suspending all sportive, social, and economic activities, quarantines, social distancing, etc.), a lot of persons have died and a lot more are still in danger. Indeed, a recently conducted study~\cite{ref2} has reported that 79\% of the confirmed infections in China were caused by undocumented patients who had no symptoms. In the same context, in numerous other countries, since coronavirus takes several days before the emergence of symptoms, it has also been reported that the known number of infections is not representative of the real number of infected people (the actual number is expected to be much higher). That is to say, asymptomatic patients are the main factor behind the large quick spreading of coronavirus and are also the major reason that caused governments to lose control over this critical situation. To contribute to remedying this global pandemic, in this paper, we propose an IoT (Internet of Things) investigation system that was specifically designed to spot both undocumented patients and infectious places. The goal is to help the authorities to disinfect high-contamination sites and confine persons even if they have no apparent symptoms. The proposed system also allows determining all persons who had close contact with infected or suspected patients. Consequently, rapid isolation of suspicious cases and more efficient control over any pandemic propagation can be achieved.

Title: Age-structured impact of social distancing on the COVID-19 epidemic in India Content: The outbreak of the novel coronavirus, COVID-19, has been declared a pandemic by the WHO. The structures of social contact critically determine the spread of the infection and, in the absence of vaccines, the control of these structures through large-scale social distancing measures appears to be the most effective means of mitigation. Here we use an age-structured SIR model with social contact matrices obtained from surveys and Bayesian imputation to study the progress of the COVID-19 epidemic in India. The basic reproductive ratio R0 and its time-dependent generalization are computed based on case data, age distribution and social contact structure. The impact of social distancing measures - workplace non-attendance, school closure, lockdown - and their efficacy with durations are then investigated. A three-week lockdown is found insufficient to prevent a resurgence and, instead, protocols of sustained lockdown with periodic relaxation are suggested. Forecasts are provided for the reduction in age-structured morbidity and mortality as a result of these measures. Our study underlines the importance of age and social contact structures in assessing the country-specific impact of mitigatory social distancing.

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: 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: AN INTERACTIVE COVID-19 MOBILITY IMPACT AND SOCIAL DISTANCING ANALYSIS PLATFORM Content: The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy and scaled to the entire population of each county and state. The research team are making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public in order to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

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: Model of a Testing-and-Quarantine Strategy to Slow-Down the COVID-19 Outbreak in Guadeloupe Content: Using a stochastic epidemic model explicitly considering the entire population of Guadeloupe (1), we explore the domain of solutions presenting an efficient slowing down of the COVID-19 epidemic spread during the post-containment period. The considered model parameters are the basic reproduction number R0 to simulate the effects of social distancing, the time delay {delta}TQ elapsed between the detection of a symptomatic person and her/his placement in quarantine to suppress her/his contagiousness, and the number Na of asymptomatic people tested positively and isolated. We show that acceptable solutions are obtained for a wide range of parameter values. Thanks to a good control of the initial epidemic spread resulting from an early containment and efficient communication by the sanitary and administrative authorities, the present situation corresponds to a pre-epidemic state. The most safe solutions are a combinations of social distancing, numerous testing to perform a systematic isolation of symptomatic patients and guided detection of asymptomatic people in the entourage of localised symptomatic patients.

Title: The COVID-19 Pandemic Confronts the Motivation Fallacy within Pulmonary Rehabilitation Programs. Content: Social distancing and quarantines have been implemented worldwide to reduce the spread of Coronavirus Disease (COVID-19). However, social distancing has had far-reaching health consequences, considering that the COVID-19 pandemic has exposed people to the hazard of physical inactivity and sedentary behavior. For patients with Chronic Obstructive Pulmonary Disease (COPD), which is one of the main diseases at risk for COVID-19, the impact is even greater since outpatient pulmonary rehabilitation (PR) programs are temporarily closed. More than ever, patients' behavior change to exercise calls for urgent debate. We propose a theoretical discussion in light of Self-Determination Theory, aiming to make PR a setting that supports autonomous forms of motivation. The scenario will not be changed in the short-term; but if other conditions hinder the development of PR in its most traditional form, the PR community will be better prepared to overcome the barriers to maintain physical exercise.

Title: Social distancing to slow the U.S. COVID-19 epidemic: an interrupted time-series analysis Content: Background: Social distancing measures to address the U.S. COVID-19 epidemic may have significant health, social, and economic impacts. Objective: To estimate the mean change in state-level COVID-19 epidemic growth before versus after the implementation of statewide social distancing measures. Design: Interrupted time-series analysis. Setting: United States. Measurements: Our primary exposure was time in relation to implementation of the first statewide social distancing measure. The pre-implementation period began 14 days prior to implementation and included up to 3 days after implementation to account for incubation. Post-implementation began 4 days after, up to and including March 30. Our primary outcome was the COVID-19 growth rate, calculated as the log of daily COVID-19 cases minus the log of daily COVID-19 cases on the prior day. Results: All states applied some form of statewide social distancing between March 10-27. The mean daily COVID-19 growth rate decreased beginning four days after implementation of the first statewide social distancing measures, by an additional 0.8% per day; 95% CI, -1.4% to -0.2%; P=0.002). This reduction corresponds to an increase in doubling time of the epidemic from 3.3 days (before) to 5.0 days (at 14 days after implementation). Limitations: Potential bias due to the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states. Conclusion: Statewide social distancing measures were associated with a decrease in U.S. COVID-19 epidemic growth. Based on the size of the epidemic at the time of implementation in each state, social distancing measures were associated with a decrease of 3,090 cases at 7 days, and 68,255 cases at 14 days, after implementation.

Title: Progression of COVID-19 in Indian States - Forecasting Endpoints Using SIR and Logistic Growth Models Content: COVID-19 has led to the most widespread public health crisis in recent history. The first case of the disease was detected in India on 31 January 2019, and confirmed cases stand at 74,281 as of 13 May 2020. Mathematical modeling can be utilized to forecast the final numbers as well as the endpoint of the disease in India and its states, as well as assess the impact of social distancing measures. In the present work, the Susceptible-Infected-Recovered (SIR) model and the Logistic Growth model have been implemented to predict the endpoint of COVID-19 in India as well as three states accounting for over 55% of the total cases - Maharashtra, Gujarat and Delhi. The results using the SIR model indicate that the disease will reach an endpoint in India on 12 September, while Maharashtra, Gujarat and Delhi will reach endpoints on 20 August, 30 July and 9 September respectively. Using the Logistic Regression model, the endpoint for India is predicted on 23 July, while that for Maharashtra, Gujarat and Delhi is 5 July, 23 June and 10 August respectively. It is also observed that the case numbers predicted by the SIR model are greater than those for the Logistic Growth model in each case. The results suggest that the lockdown enacted by the Government of India has had only a moderate impact on the spread of COVID-19, and emphasize the need for firm implementation of social distancing guidelines.

Title: ‘What is Inconvenient for You is Life-saving for Me’: How Health Inequities are playing out during the COVID-19 Pandemic Content: The COVID-19 pandemic has had a significant impact globally. Most affected, however, are those individuals and groups routinely disadvantaged by the social injustice created by the misdistribution of power, money, and resources. Simple measures that prevent the spread of COVID-19, such as frequent hand washing and social distancing, are unavailable to millions of people in the wealthiest of nations and in the poorest of nations. Disadvantaged groups are impacted more directly and in disproportionately higher numbers due to existing poor health, and the disruption of services central to securing an income and an education will have lasting consequences for their futures. The unintended effect of exclusionary government policies is that privileged citizens and healthcare systems are also at greater risk. This paper seeks to highlight the impact of COVID-19 on those already suffering health inequities through consideration of some of the social determinants of health on groups in affluent and poorer nations. It also highlights some of the factors that may assist in tackling health inequities as we emerge from this pandemic.

Title: 'What is Inconvenient for You is Life-saving for Me': How Health Inequities are playing out during the COVID-19 Pandemic Content: The COVID-19 pandemic has had a significant impact globally. Most affected, however, are those individuals and groups routinely disadvantaged by the social injustice created by the misdistribution of power, money, and resources. Simple measures that prevent the spread of COVID-19, such as frequent hand washing and social distancing, are unavailable to millions of people in the wealthiest of nations and in the poorest of nations. Disadvantaged groups are impacted more directly and in disproportionately higher numbers due to existing poor health, and the disruption of services central to securing an income and an education will have lasting consequences for their futures. The unintended effect of exclusionary government policies is that privileged citizens and healthcare systems are also at greater risk. This paper seeks to highlight the impact of COVID-19 on those already suffering health inequities through consideration of some of the social determinants of health on groups in affluent and poorer nations. It also highlights some of the factors that may assist in tackling health inequities as we emerge from this pandemic.

Title: Using VERA to explain the impact of social distancing on the spread of COVID-19 Content: COVID-19 continues to spread across the country and around the world. Current strategies for managing the spread of COVID-19 include social distancing. We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the spread of COVID-19. Then, VERA automatically spawns agent-based simulations from the conceptual models, and, given a data set, automatically fills in the values of the simulation parameters from the data. Next, the user can view the simulation results, and, if needed, revise the simulation parameters and run another experimental trial, or build an alternative conceptual model. We describe the use VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity.

Title: Forecasting COVID 19 growth in India using Susceptible-Infected-Recovered (S.I.R) model Content: This work covers the analysis of the COVID 19 spread in different countries and dealing the main feature of COVID 19 growth, which is the spread due to the social-contact structure, which is governed by the parameter \b{eta}. The dependency of this parameter \b{eta} on the transmission level in society gives a sense of the effectiveness of the measures taken for social distancing. A separate algorithm is hardcoded in python using Scipy which learns the social-contact structure and gives a suitable value for \b{eta}, which has a major impact on the outcome of the result. Forecasting for the epidemic spread in India was done, and it was found that the strictness at which social distancing in India is done, is insufficient for the growth of COVID 19.

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: Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease Content: The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

Title: Public health interventions in India slowed the spread of COVID-19 epidemic dynamics Content: Background The government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood. Methods Rates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented. Findings Daily case rates ranged from 0.03 to 30.05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2.18/10 million/day). Average Rt was 1.99 (95% CI 1.93-2.06) for India; it ranged from 1.38 to 2.78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value. Interpretation The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates and Rt will aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India. Funding Non-funded.

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: Quantifying what could have been – the impact of the Australian and New Zealand governments’ response to COVID-19 Content: 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. 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. This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Title: Lack of sufficient public space can limit the effectiveness of COVID-19's social distancing measures Content: One of the primary strategies of slowing down the COVID-19 pandemic has been the establishment of social distancing rules that recommend keeping a buffer distance between individuals, and this has proven effective in helping in reducing the basic reproduction number [R 0] . However, social distancing rules have put the use of public spaces in densely populated places under strain, and this is especially important as some of the most virulent outbreaks of the COVID-19 pandemic have been in compact cities. It is therefore fundamental to take into account each neighbourhood's morphological characteristics and the potential population densities each street, square or park can accommodate under such new regulations in order to effectively enforce social distancing rules. Otherwise, certain areas may be rapidly overwhelmed by crowds with citizens unable to maintain the minimum safe distance between individuals. In this paper, we develop a method to identify the potential public space accessibility if social distancing rules are followed and we apply it to three global and highly affected by COVID-19 cities. Our research finds that, at micro level there are important inequalities between neighbourhoods, so people will struggle to comply with social distancing rules and consequently it will make controlling infection rates more difficult.

Title: Risk Management of COVID-19 by Universities in China Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020 Content: The rapid spread of new coronaviruses throughout China and the world in 2019–2020 has had a great impact on China’s economic and social development As the backbone of Chinese society, Chinese universities have made significant contributions to emergency risk management Such contributions have been made primarily in the following areas: alumni resource collection, medical rescue and emergency management, mental health maintenance, control of staff mobility, and innovation in online education models Through the support of these methods, Chinese universities have played a positive role in the prevention and control of the epidemic situation However, they also face the problems of alumni’s economic development difficulties, the risk of deadly infection to medical rescue teams and health workers, infection of teachers and students, and the unsatisfactory application of information technology in resolving the crisis In response to these risks and emergency problems, we propose some corresponding solutions for public dissemination, including issues related to medical security, emergency research, professional assistance, positive communication, and hierarchical information-based teaching The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang;however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020 In the best-case scenario, current data suggest that transmission in both provinces is slowing down

Title: Public activities preceding the onset of acute respiratory infection syndromes in adults in England - implications for the use of social distancing to control pandemic respiratory infections. Content: Background: Social distancing measures may reduce the spread of emerging respiratory infections however, there is little empirical data on how exposure to crowded places affects risk of acute respiratory infection. Methods: We used a case-crossover design nested in a community cohort to compare self-reported measures of activities during the week before infection onset and baseline periods. The design eliminates the effect of non-time-varying confounders. Time-varying confounders were addressed by exclusion of illnesses around the Christmas period and seasonal adjustment. Results: 626 participants had paired data from the week before 1005 illnesses and the week before baseline. Each additional day of undertaking the following activities in the prior week was associated with illness onset: Spending more than five minutes in a room with someone (other than a household member) who has a cold (Seasonally adjusted OR 1·15, p=0·003); use of underground trains (1·31, p=0·036); use of supermarkets (1·32, p<0·001); attending a theatre, cinema or concert (1·26, p=0·032); eating out at a café, restaurant or canteen (1·25, p=0·003); and attending parties (1·47, p<0·001). Undertaking the following activities at least once in the previous week was associated with illness onset: using a bus, (aOR 1.48, p=0.049), shopping at small shops (1.9, p<0.002) attending a place of worship (1.81, p=0.005). Conclusions: Exposure to potentially crowded places, public transport and to individuals with a cold increases risk of acquiring circulating acute respiratory infections. This suggests social distancing measures can have an important impact on slowing transmission of emerging respiratory infections.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: Background: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases generated per case). Methods: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire documents the age and location of contacts and as well as a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday 24th March, one day after a “lockdown” was implemented across the UK. We compared measured contact patterns during the lockdown to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. Findings: We found a 73% reduction in the average daily number of contacts observed per participant (from 10.2 to 2.9). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37 - 0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22 - 0.53) for physical contacts only. Interpretation: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: BACKGROUND: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases generated per case). METHODS: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a "lockdown" was implemented across the UK. We compared measured contact patterns during the "lockdown" to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. RESULTS: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37-0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22-0.53) for physical (skin to skin) contacts only. CONCLUSIONS: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: BACKGROUND: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R(0), the average number of secondary cases generated per case). METHODS: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a “lockdown” was implemented across the UK. We compared measured contact patterns during the “lockdown” to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. RESULTS: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R(0) from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37–0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22–0.53) for physical (skin to skin) contacts only. CONCLUSIONS: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

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: 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: 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: Modeling, state estimation, and optimal control for the US COVID-19 outbreak Content: The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that “on-off” policies alternating between strict social distancing and relaxing such restrictions can be effective at “flattening” the curve while likely minimizing social and economic cost.

Title: Modeling, state estimation, and optimal control for the US COVID-19 outbreak Content: The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost.

Title: Early transmission dynamics of COVID-19 in Chile: From sub-exponential ascending growth dynamics to a stationary disease wave, March-April, 2020 Content: The COVID-19 pandemic reached Latin America in February 2020, with the first case in Chile identified on March 3rd, 2020. Since then Chile has accumulated a total of 14365 cases as of April 28th, 2020. We estimate the reproduction number during the early transmission phase in Chile and study the effectiveness of control interventions by conducting short-term forecasts based on early transmission dynamics of COVID-19. The incidence curve displays sub-exponential growth dynamics with the scaling of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number estimated at 1.6 (95% CI: 1.5, 1.6). The sub-epidemic model indicates a stationary wave of stable epidemic size. Our current findings point to sustained transmission of SARS-CoV-2 in Chile. While the social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing efforts to control the epidemic.

Title: Intersecting Pandemics: Impact of SARS-CoV-2 (COVID-19) Protective Behaviors on People Living with HIV, Atlanta, Georgia. Content: BACKGROUND COVID-19 and its social responses threaten the health of people living with HIV. We conducted a rapid-response interview to assess COVID-19 protective behaviors of people living with HIV and the impact of their responses on HIV related healthcare. METHOD Men and women living with HIV (N = 162) ages 20 to 37 participating in a longitudinal study of HIV treatment and care completed routine study measures and an assessment of COVID-19-related experiences. RESULTS At baseline, a majority of participants demonstrated HIV viremia, markers indicative of renal disorders, and biologically confirmed substance use. At follow-up, in the first month of responding to COVID-19, engaging in more social distancing behaviors was related to difficulty accessing food and medications, and increased cancelation of healthcare appointments, both by self and providers. We observed ART adherence had improved during the initial month of COVID-19 response. CONCLUSIONS Factors that may pose added risk for COVID-19 severity were prevalent among people living with HIV and those with greater risk factors did not practice more COVID-19 protective behaviors. Social distancing and other practices intended to mitigate the spread of COVID-19 interfered with HIV care, and impeded access to food and medications, although an immediate adverse impact on medication adherence was not evident. These results suggest social responses to COVID-19 adversely impacted the healthcare of people living with HIV, supporting continued monitoring to determine the long-term effects of co-occurring HIV and COVID-19 pandemics.

Title: Intersecting Pandemics: Impact of SARS-CoV-2 (COVID-19) Protective Behaviors on People Living with HIV, Atlanta, Georgia Content: BACKGROUND: COVID-19 and its social responses threaten the health of people living with HIV. We conducted a rapid-response interview to assess COVID-19 protective behaviors of people living with HIV and the impact of their responses on HIV related healthcare. METHOD: Men and women living with HIV (N = 162) ages 20 to 37 participating in a longitudinal study of HIV treatment and care completed routine study measures and an assessment of COVID-19-related experiences. RESULTS: At baseline, a majority of participants demonstrated HIV viremia, markers indicative of renal disorders, and biologically confirmed substance use. At follow-up, in the first month of responding to COVID-19, engaging in more social distancing behaviors was related to difficulty accessing food and medications, and increased cancelation of healthcare appointments, both by self and providers. We observed ART adherence had improved during the initial month of COVID-19 response. CONCLUSIONS: Factors that may pose added risk for COVID-19 severity were prevalent among people living with HIV and those with greater risk factors did not practice more COVID-19 protective behaviors. Social distancing and other practices intended to mitigate the spread of COVID-19 interfered with HIV care, and impeded access to food and medications, although an immediate adverse impact on medication adherence was not evident. These results suggest social responses to COVID-19 adversely impacted the healthcare of people living with HIV, supporting continued monitoring to determine the long-term effects of co-occurring HIV and COVID-19 pandemics.

Title: Observed mobility behavior data reveal social distancing inertia Content: The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. This suggests that a portion of population who could and were willing to practice social distancing voluntarily and naturally reacted to the emergence of COVID-19 cases. However, after about two weeks, the statistics saturated and stopped improving, despite the continuous rise in COVID-19 cases. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics. The national data showed that the inertia phenomenon is universal, happening in all the U.S. states and for all the studied statistics. The U.S. states showed a synchronized trend, regardless of the timeline of their statewide COVID-19 case spreads or government orders.

Title: Orthopaedic Surgical Selection and Inpatient Paradigms During the Coronavirus (COVID-19) Pandemic Content: The novel coronavirus pandemic, also known as SARS-CoV-2, has placed an immense strain on healthcare systems across the entire world. Consequently, multiple federal and state governments have placed restrictions on hospitals such as limiting “elective surgery” and recommending social or physical distancing. We review the literature on several areas that have been affected including surgical selection, inpatient care, and physician well-being. These areas affecting inpatient paradigms include surgical priority, physical or social distancing, file sharing for online clinical communications, and physician wellness. During this crisis, it is important that orthopaedic departments place an emphasis on personnel safety and slowing the spread of the virus so that the department can still maintain vital functions. Physical distancing and emerging technologies such as inpatient telemedicine and online file sharing applications can enable orthopaedic programs to still function while attempting to protect medical staff and patients from the novel coronavirus spread. This literature review sought to provide evidence-based guidance to orthopaedic departments during an unprecedented time. Orthopaedic surgeons should follow the Centers for Disease Control and Prevention guidelines, wear PPE when appropriate, have teams created using physical distancing, understand the department's policy on elective surgery, and engage in routines which enhance physician wellness.

Title: Spring in London with Covid-19: a personal view Content: This is a personal view from London as the Covid-19 pandemic continues to spread here and the situation changes from day to day. As such it can only be a snapshot caught in time; it is not a diary of events. The Coronavirus Act 2020 gives Government enormous powers and was passed by Parliament in one day of debate immediately before it closed early for the Easter break. In March, the government imposed a "lockdown: the closure of all" but "essential" businesses and people other than essential workers must work from home but are allowed out for exercise and food shopping but must maintain 2 m apart, the "social distancing rule". The aim is to suppress the spread of the virus, reduce the death toll and "protect the National Health Service (NHS)" which needed time to empty wards and expand its intensive care unit (ICU) capability to deal with an expected influx of thousands of very sick patients. I discuss whether this strategy is working, how and why it has rapidly been altered to respond to criticism. Why was the Government so slow to seek the help of private laboratories to assist with testing? Why was the personal protective equipment (PPE) guidance altered only after criticism? I look at the impact of the lockdown on the UK economy, the changes to practice of medicine and speeding of scientific research. Cooperating with the lockdown has its price; is it harming the health and mental health of children, people living in households with potentially abusive partners or parents and those who are disabled or financially desperate? Is the cure worse than the disease? The Economy is being devastated by the lockdown and each day of lockdown it is worse. Is litigation being seeded even now by the pandemic? Notwithstanding unprecedented Government financial help many businesses are on the edge of collapse, people will lose their jobs and pensioners income. The winners include pharmacies, supermarkets, online food retailers, Amazon, online apps, providers of video games, services, streaming and scientific research laboratories, manufacturers of testing kits, ventilators, hand sanitisers, coffins, undertakers, etc. The British public is cooperating with lockdown but are we less productive at home? Parents with babies and children often child minders, school, grandparents or paid help which is not now available. Will current reliance on video-conferencing and video calls permanently change the way we work and will we need smaller city offices? Will we travel less? Will medical and legal practice and civil and criminal trials be generally carried out remotely? Will social distancing with self-isolation and job losses and business failures fuel depression? Is Covid-19 comparable to past epidemics like the Plague and Spanish flu?

Title: Spring in London with Covid-19: a personal view. Content: This is a personal view from London as the Covid-19 pandemic continues to spread here and the situation changes from day to day. As such it can only be a snapshot caught in time; it is not a diary of events. The Coronavirus Act 2020 gives Government enormous powers and was passed by Parliament in one day of debate immediately before it closed early for the Easter break. In March, the government imposed a "lockdown: the closure of all" but "essential" businesses and people other than essential workers must work from home but are allowed out for exercise and food shopping but must maintain 2 m apart, the "social distancing rule". The aim is to suppress the spread of the virus, reduce the death toll and "protect the National Health Service (NHS)" which needed time to empty wards and expand its intensive care unit (ICU) capability to deal with an expected influx of thousands of very sick patients. I discuss whether this strategy is working, how and why it has rapidly been altered to respond to criticism. Why was the Government so slow to seek the help of private laboratories to assist with testing? Why was the personal protective equipment (PPE) guidance altered only after criticism? I look at the impact of the lockdown on the UK economy, the changes to practice of medicine and speeding of scientific research. Cooperating with the lockdown has its price; is it harming the health and mental health of children, people living in households with potentially abusive partners or parents and those who are disabled or financially desperate? Is the cure worse than the disease? The Economy is being devastated by the lockdown and each day of lockdown it is worse. Is litigation being seeded even now by the pandemic? Notwithstanding unprecedented Government financial help many businesses are on the edge of collapse, people will lose their jobs and pensioners income. The winners include pharmacies, supermarkets, online food retailers, Amazon, online apps, providers of video games, services, streaming and scientific research laboratories, manufacturers of testing kits, ventilators, hand sanitisers, coffins, undertakers, etc. The British public is cooperating with lockdown but are we less productive at home? Parents with babies and children often child minders, school, grandparents or paid help which is not now available. Will current reliance on video-conferencing and video calls permanently change the way we work and will we need smaller city offices? Will we travel less? Will medical and legal practice and civil and criminal trials be generally carried out remotely? Will social distancing with self-isolation and job losses and business failures fuel depression? Is Covid-19 comparable to past epidemics like the Plague and Spanish flu?

Title: Differences in power-law growth over time and indicators of COVID-19 pandemic progression worldwide Content: An automated statistical and error analysis of 45 countries or regions with more than 1000 cases of COVID-19 as of March 28, 2020, has been performed. This study reveals differences in the rate of disease spreading rate over time in different countries. This survey observes that most countries undergo a beginning exponential growth phase, which transitions into a power-law phase, as recently suggested by Ziff and Ziff. Tracking indicators of growth, such as the power-law exponent, are a good indication of the relative danger different countries are in and show when social measures are effective towards slowing the spread. The data compiled here are usefully synthesizing a global picture, identifying country to country variation in spreading, and identifying countries most at risk. This analysis may factor into how best to track the effectiveness of social distancing policies and quarantines in real-time as data is updated each day.

Title: Containing the Spread of Coronavirus Disease 2019 (COVID-19): Meteorological Factors and Control Strategies Content: The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We found that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten onset-to-isolation time is markedly reduced and the importance of contact tracing and quarantine and social distancing increases. The analytic results also highlight the importance of early intervention to contain the spread of COVID-19.

Title: Network model and analysis of the spread of Covid-19 with social distancing Content: The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human-human interactions that could be mediators of disease spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection in each of these scenarios.

Title: Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies Content: Abstract The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.

Title: Understanding the impact of the COVID-19 pandemic on well-being and virtual care for people living with dementia and care partners living in the community Content: The COVID-19 pandemic has necessitated public health measures that have impacted the provision of care for people living with dementia and their families. Additionally, the isolation that results from social distancing may be harming well-being for families, as formal and informal supports become less accessible. For those with living with dementia and experiencing agitation, social distancing may be even harder to maintain, or social distancing could potentially aggravate dementia-related neuropsychiatric symptoms. To understand the lived experience of social and physical distancing during the COVID-19 pandemic in Canada we remotely interviewed 21 participants who normally attend a dementia specialty clinic in Calgary, Alberta, during a period where essential businesses were closed and healthcare had abruptly transitioned to telemedicine. The impacts of the public health measures in response to the pandemic emerged in three main categories of experience: 1) personal; 2) health services; and 3) health status (of both person living with dementia and care partner). This in-depth understanding of the needs and experiences of the pandemic for people living with dementia suggests that innovative means are urgently needed to facilitate provision of remote medicine and also social interaction and integration.

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.