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

COVID-19 and social distancing

A Modelling Analysis of Strategies for Relaxing COVID-19 Social Distancing

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.

COVID-19 social distancing in the Kingdom of Saudi Arabia: Bold measures in the face of political, economic, social and religious challenges

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

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

COVID-19: Getting ahead of the epidemic curve by early implementation of social distancing.

Social distancing alters the clinical course of COVID-19 in young adults: A comparative cohort study

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.

DOUBLE POWER LAW FOR COVID-19: PREDICTION OF NEW CASES AND DEATH RATES IN ITALY AND SPAIN

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.

COVID-19 and the Social Distancing Paradox: dangers and solutions

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

Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: A UK-based focus group study

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.

Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States

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.

Observed mobility behavior data reveal social distancing inertia

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.

Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate

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.

Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates

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

Early Mandated Social Distancing is a Strong Predictor of Reduction in Highest Number of New COVID-19 cases per Day within Various Geographic Regions

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

The impact of believing you have had COVID-19 on behaviour: Cross-sectional survey

Objectives: To investigate whether people who think they have had COVID-19 are less likely to engage in social distancing measures compared with those who think they have not had COVID-19. Design: On-line cross-sectional survey. Setting: Data were collected between 20th and 22nd April. Participants: 6149 participants living in the UK aged 18 years or over. Main outcome measures: Perceived immunity to COVID-19, self-reported adherence to social distancing measures (going out for essential shopping, nonessential shopping, and meeting up with friends/family; total out-of-home activity), worry about COVID-19 and perceived risk of COVID-19 to oneself and people in the UK. Knowledge that cough and high temperature / fever are the main symptoms of COVID-19. Results: In this sample, 1493 people (24.3%) thought they had had COVID-19. Only 245 (4.0%) reported receiving a test result saying they had COVID-19. Reported test results were often incongruent with participants' belief that they had had COVID-19. People who believed that they had had COVID-19 were: more likely to agree that they had some immunity to COVID-19; less likely to report adhering to social distancing measures; less worried about COVID-19; and less likely to know that cough and high temperature / fever are two of the most common symptoms of COVID-19. Conclusions: The number of people in the UK who think they have already had COVID-19 is about twice the rate of current prevalence estimates. People who think that they have had COVID-19 may contribute to transmission of the virus through non-adherence to social distancing measures. Clear communications to this growing group are needed to explain why protective measures continue to be important and to encourage sustained adherence.

COVID-19: Getting ahead of the epidemic curve by early implementation of social distancing

Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts

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.

A simple method to quantify country-specific effects of COVID-19 containment measures

Most of the world is currently fighting to limit the impact of the COVID-19 pandemic. Italy, the Western country with most COVID-19 related deaths, was the first to implement drastic containment measures in early March, 2020. Since then most other European countries, the USA, Canada and Australia, have implemented similar restrictions, ranging from school closures, banning of recreational activities and large events, to complete lockdown. Such limitations, and softer promotion of social distancing, may be more effective in one society than in another due to cultural or political differences. It is therefore important to evaluate the effectiveness of these initiatives by analyzing country-specific COVID-19 data. We propose to model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, since confirmed positive cases are isolated and do not transmit the disease. We provide an explicit formula that is easily implemented and permits us to fit official COVID-19 data in a series of Western countries. We found excellent agreement with data-driven estimation of the day-of-change in disease dynamics and the dates when official interventions were introduced. Our analysis predicts that for most countries only the more drastic restrictions have reduced virus spreading. Further, we predict that the number of unidentified COVID-19-positive individuals at the beginning of the epidemic is ∼10 times the number of confirmed cases. Our results provide important insight for future planning of non-pharmacological interventions aiming to contain spreading of COVID-19 and similar diseases.

Social Distancing Has Merely Stabilized COVID-19 in the US

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

Reduction in effective reproduction number of COVID-19 is higher in countries employing active case detection with prompt isolation

Countries that implemented liberal testing with active case finding and prompt isolation, combined with contact tracing and quarantine, were more successful in reducing the reproduction number compared to countries that primarily relied on social distancing and lockdown measures.

Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate.

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

Social Distancing in the Covid-19 Pandemic

Supporting Social Distancing for COVID-19 Mitigation Through Community-Based Volunteer Networks

Social Media and COVID-19: Can Social Distancing be Quantified without Measuring Human Movements?

The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (P<0.05) between our findings and the Google social distancing report.

Measures to Limit COVID-19 Outbreak Effects Among Military Personnel: Preliminary Data

INTRODUCTION: The COVID-19 outbreak posed a threat to the readiness of military forces as well as their ability to fulfill missions. Seeing that military forces have been encountering similar challenges, we found it eminent to share the Israeli Defense Force (IDF) Northern Command's (NC) preliminary experience. MATERIALS AND METHODS: We retrospectively summarized the actions that were taken by our team, focusing on 18 battalions at the Israeli NC. These actions included promoting a series of organizational changes in terms of social distancing and medical regulations as well as working to strengthen medical leadership through designated video meetings with medical commanders across our organization. Meetings included relevant clinical education, updates, and leadership building. These actions and others were aimed to increase our influence on the decision-making processes. While we conducted real-time reverse transcriptase polymerase chain reaction SARS-CoV-2 laboratory tests for soldiers who were suspected to have COVID-19 (those presenting with compatible signs and symptoms after having been exposed to a confirmed COVID-19 patient), we were not able to screen healthy populations, nor did we have serum antibody serologic tests available during the study period. We reviewed the COVID-19 outbreak national data, obtained from Ministry of Health publishings and the IDF databases. Data were included from February 26th, 2020 (day 0, first COVID-19 patient in Israel) to April 19th, 2020 (day 53, about 1 month after most of the COVID-19 regulation were issued in the NC). RESULTS: The mean age of the battalion soldiers was 21.29 ± 4.06 (range 18-50), 81.34% male. Most restrictions were issued on day 18. On day 53, 98.85% of the personnel in the battalions were kept active and asymptomatic in their units. CONCLUSIONS: Despite the limited availability of laboratory testing for COVID-19 our actions enabled us to lead a strict risk-management policy while maintaining most of the available workforce.

Willingness to Accept Tradeoffs among Covid-19 Cases, Social-Distancing Restrictions, and Economic Impact: A Nationwide US Study

We designed a discrete-choice experiment to quantify the extent to which US adults would accept greater risk of infection with SARS-CoV-2 in return for lifting social-distancing restrictions and diminishing the economic impact of the COVID-19 pandemic. 5953 adults representing all 50 states had 4 distinctly different preference patterns. About 37% were risk minimizers reluctant to accept any increases in risk of contracting the virus. Another group (26%) was primarily concerned about time required for economic recovery, accepting increases in COVID-19 risk levels up to 16% to shorten recovery from 3 to 2 years. The remaining two groups diverged on the relative importance of reopening nonessential businesses. The larger group (26%) strongly preferred delaying reopening while the smaller group (13%) would accept COVID-19 risks well beyond 20% to avoid a delay in reopening. Political affiliation, race, household income and employment status were predictive of group membership.

Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States

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

A decision support system for optimizing the cost of social distancing in order to stop the spread of COVID-19

Currently there are many attempts around the world to use computers, smartphones, tablets and other electronic devices in order to stop the spread of COVID-19. Most of these attempts focus on collecting information about infected people, in order to help healthy people avoid contact with them. However, social distancing decisions are still taken by the governments empirically. That is, the authorities do not have an automated tool to recommend which decisions to make in order to maximize social distancing and to minimize the impact for the economy. In this paper we address the aforementioned problem and we design an algorithm that provides social distancing methods (i.e., what schools, shops, factories, etc. to close) that are efficient (i.e., that help reduce the spread of the virus) and have low impact on the economy. On short: a) we propose several models (i.e., combinatorial optimization problems); b) we show some theoretical results regarding the computational complexity of the formulated problems; c) we give an algorithm for the most complex of the previously formulated problems; d) we implement and test our algorithm; and e) we show an integer linear program formulation for our problem.

Impact of self-imposed prevention measures and short-term government intervention on mitigating and delaying a COVID-19 epidemic

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

Lack of sufficient public space can limit the effectiveness of COVID-19's social distancing measures

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.

Stemming the flow: how much can the Australian smartphone app help to control COVID-19?

OBJECTIVES Our objective is to assess the potential contribution of the Australian Government's mobile smartphone tracing app (COVIDSafe) to the sustained control of coronavirus disease 2019 (COVID-19). STUDY TYPE Development and analysis of a system dynamics model. METHODS To define the pandemic context and specify model-building parameters, we searched for literature on COVID-19, its epidemiology in Australia, case finding processes, and factors that might affect community acceptance of the COVIDSafe smartphone app for contact tracing. We then developed a system dynamics model of COVID-19 based on a modified susceptible-exposed-infected-recovered compartmental model structure, using initial pandemic data and published information on virus behaviour to determine parameter values. We applied the model to examine factors influencing the projected trends: the extent of viral testing, community participation in social distancing, and the level of uptake of the COVIDSafe app. RESULTS Modelling suggests that a second COVID-19 wave will occur if social distancing declines (i.e. if the average number of contacts made by each individual each day increases) and the rate of testing declines. The timing and size of the second wave will depend on the rate of decrease in social distancing and the decline in testing rates. At the app uptake level of approximately 27% (current at 20 May 2020), with a monthly 50% reduction in social distancing (i.e. the average number of contacts per day doubling every 30 days until they reach pre-social distancing rates) and a 5% decline in testing, the app would reduce the projected total number of new cases during April-December 2020 by one-quarter. If uptake reaches the possible maximum of 61%, the reduction could be more than half. CONCLUSIONS Maintenance of a large-scale testing regimen for COVID-19 and widespread community practice of social distancing are vital. The COVIDSafe smartphone app has the potential to be an important adjunct to testing and social distancing. Depending on the level of community uptake of the app, it could have a significant mitigating effect on a second wave of COVID-19 in Australia.

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

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

A Noncooperative Game Analysis for Controlling COVID-19 Outbreak

COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective to control this pandemic. In this paper, we provide an analytical model on the effectiveness of the sustainable lockdown policy that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we analyze a noncooperative game environment that provides an incentive for maintaining social distancing. Furthermore, the sustainability of the lockdown policy is also interpreted with the help of a game-theoretic incentive model for maintaining social distancing. Finally, an extensive numerical analysis is provided to study the impact of maintaining a social-distancing measure to prevent the Covid-19 outbreak. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

OBJECTIVES: Our objective is to assess the potential contribution of the Australian Government's mobile smartphone tracing app (COVIDSafe) to the sustained control of coronavirus disease 2019 (COVID-19). STUDY TYPE: Development and analysis of a system dynamics model. METHODS: To define the pandemic context and specify model-building parameters, we searched for literature on COVID-19, its epidemiology in Australia, case finding processes, and factors that might affect community acceptance of the COVIDSafe smartphone app for contact tracing. We then developed a system dynamics model of COVID-19 based on a modified susceptible-exposed-infected-recovered compartmental model structure, using initial pandemic data and published information on virus behaviour to determine parameter values. We applied the model to examine factors influencing the projected trends: the extent of viral testing, community participation in social distancing, and the level of uptake of the COVIDSafe app. RESULTS: Modelling suggests that a second COVID-19 wave will occur if social distancing declines (i.e. if the average number of contacts made by each individual each day increases) and the rate of testing declines. The timing and size of the second wave will depend on the rate of decrease in social distancing and the decline in testing rates. At the app uptake level of approximately 27% (current at 20 May 2020), with a monthly 50% reduction in social distancing (i.e. the average number of contacts per day doubling every 30 days until they reach pre-social distancing rates) and a 5% decline in testing, the app would reduce the projected total number of new cases during April-December 2020 by one-quarter. If uptake reaches the possible maximum of 61%, the reduction could be more than half. CONCLUSIONS: Maintenance of a large-scale testing regimen for COVID-19 and widespread community practice of social distancing are vital. The COVIDSafe smartphone app has the potential to be an important adjunct to testing and social distancing. Depending on the level of community uptake of the app, it could have a significant mitigating effect on a second wave of COVID-19 in Australia.

On the benefits of flattening the curve: A perspective

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

Could fighting airborne transmission be the next line of defence against COVID-19 spread?

Abstract The World Health Organization declared the infectious spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) an epidemic during its initial outbreak in Wuhan (China) and has since declared it a pandemic and, more recently, an endemic infection that may remain in our communities. A vaccine for COVID-19 is expected to take several months, meaning that the spread may continue in future, in the absence of the most effective measures of social distancing and self-isolation. While these measures have worked well under lockdowns, the potential of airborne transmission of COVID-19 under the eased restrictions has not been considered important enough. We discuss the need to acknowledge the airborne spread of COVID-19 inside built spaces under eased movement restrictions and the potential steps that can be taken to control it.

INTRODUCTION: The COVID-19 outbreak posed a threat to the readiness of military forces as well as their ability to fulfill missions. Seeing that military forces have been encountering similar challenges, we found it eminent to share the Israeli Defense Force (IDF) Northern Command’s (NC) preliminary experience. MATERIALS AND METHODS: We retrospectively summarized the actions that were taken by our team, focusing on 18 battalions at the Israeli NC. These actions included promoting a series of organizational changes in terms of social distancing and medical regulations as well as working to strengthen medical leadership through designated video meetings with medical commanders across our organization. Meetings included relevant clinical education, updates, and leadership building. These actions and others were aimed to increase our influence on the decision-making processes. While we conducted real-time reverse transcriptase polymerase chain reaction SARS-CoV-2 laboratory tests for soldiers who were suspected to have COVID-19 (those presenting with compatible signs and symptoms after having been exposed to a confirmed COVID-19 patient), we were not able to screen healthy populations, nor did we have serum antibody serologic tests available during the study period. We reviewed the COVID-19 outbreak national data, obtained from Ministry of Health publishings and the IDF databases. Data were included from February 26th, 2020 (day 0, first COVID-19 patient in Israel) to April 19th, 2020 (day 53, about 1 month after most of the COVID-19 regulation were issued in the NC). RESULTS: The mean age of the battalion soldiers was 21.29 ± 4.06 (range 18–50), 81.34% male. Most restrictions were issued on day 18. On day 53, 98.85% of the personnel in the battalions were kept active and asymptomatic in their units. CONCLUSIONS: Despite the limited availability of laboratory testing for COVID-19 our actions enabled us to lead a strict risk-management policy while maintaining most of the available workforce.

Retweeting for COVID-19: Consensus building, information sharing, dissent, and lockdown life

Purpose: Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions, information dissemination and consensus building in all major forms, including social media. This article investigates important issues reflected on Twitter. Design/methodology/approach: A thematic analysis of the most retweeted English-language tweets on Twitter mentioning COVID-19 during March 10-29, 2020. Findings: The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications: Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers, and helped each other to cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications: Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value: This is the first qualitative analysis of general COVID-19-related retweeting.

Protocol for an Observational Study on the Effects of Social Distancing on Influenza-Like Illness and COVID-19

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.

Social distancing: A non-pharmacological intervention for COVID-19.

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

Stopping the Spread of COVID-19.

Covid-19 Tweeting in English: Gender Differences

At the start of 2020, COVID-19 became the most urgent threat to global public health. Uniquely in recent times, governments have imposed partly voluntary, partly compulsory restrictions on the population to slow the spread of the virus. In this context, public attitudes and behaviors are vitally important for reducing the death rate. Analyzing tweets about the disease may therefore give insights into public reactions that may help guide public information campaigns. This article analyses 3,038,026 English tweets about COVID-19 from March 10 to 23, 2020. It focuses on one relevant aspect of public reaction: gender differences. The results show that females are more likely to tweet about the virus in the context of family, social distancing and healthcare whereas males are more likely to tweet about sports cancellations, the global spread of the virus and political reactions. Thus, women seem to be taking a disproportionate share of the responsibility for directly keeping the population safe. The detailed results may be useful to inform public information announcements and to help understand the spread of the virus. For example, failure to impose a sporting bans whilst encouraging social distancing may send mixed messages to males.

Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world

Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.

The Immediate Effect of COVID-19 Policies on Social Distancing Behavior in the United States

In the absence of a vaccine and effective antiviral medications, most of the non-pharmaceutical interventions focus on reducing social contact rates through different social distancing policies. However, the effectiveness of different policies and their relative impact vis-a-vis that of mechanisms driven by public awareness and voluntary actions have not been studied. This is crucial since in most places we observe significant reductions in social interaction before any policy was implemented. Variations in types and effective dates of different social distancing policies across different states in the US create a natural experiment to study the causal impact of each policy during the early stage of the outbreak. Using these policy variations and the aggregate human mobility and location trends published by Google for the month of March 2020, we employ a quasi-experimental approach to measure the impact of six common policies on people's presence at home and their mobility in different types of public places. Our results rank six common social distancing policies based on the magnitude and significance of their impact, beyond what has already been achieved through voluntary actions. They show that while strong policies such as statewide stay home mandate and non-essential business closure have strong causal impacts on reducing social interactions, most of the expected impact of more lenient policies (such as large gathering ban and school closure mandates) are already reaped from non-policy mechanisms such as voluntary actions and public awareness.

Is one- or two-meters social distancing enough for COVID-19? Evidence for reassessing

The Benefits and Costs of Social Distancing in Rich and Poor Countries

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

Forecasting the Spread of COVID-19 under Different Reopening Strategies

We combine COVID-19 case data with demographic and mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the spread of this disease in the United States. We find that the incidence of infectious COVID-19 individuals has a concave effect on contagion, as would be expected if people have inter-related social networks. We also demonstrate that social distancing and population density have large effects on the rate of contagion. The social distancing in late March and April substantially reduced the number of COVID-19 cases. However, the concave contagion pattern means that when social distancing measures are lifted, the growth rate is considerable but will not be exponential as predicted by standard SIR models. Furthermore, counties with the lowest population density could likely avoid high levels of contagion even with no social distancing. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19, about double what would occur if the US only restored to 50% of the way to normalcy.

Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.

Social distancing: A non-pharmacological intervention for COVID-19

Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries

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

Is one- or two-meters social distancing enough for COVID-19?

Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States

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

'Distancers' and 'non-distancers'? The potential social psychological impact of moralizing COVID-19 mitigating practices on sustained behaviour change

COVID-19 mitigating practices such as 'hand-washing', 'social distancing', or 'social isolating' are constructed as 'moral imperatives', required to avert harm to oneself and others. Adherence to COVID-19 mitigating practices is presently high among the general public, and stringent lockdown measures supported by legal and policy intervention have facilitated this. In the coming months, however, as rules are being relaxed and individuals become less strict, and thus, the ambiguity in policy increases, the maintenance of recommended social distancing norms will rely on more informal social interactional processes. We argue that the moralization of these practices, twinned with relaxations of policy, may likely cause interactional tension between those individuals who do vs. those who do not uphold social distancing in the coming months: that is, derogation of those who adhere strictly to COVID-19 mitigating practices and group polarization between 'distancers' and 'non-distancers'. In this paper, we explore how and why these processes might come to pass, their impact on an overall societal response to COVID-19, and the need to factor such processes into decisions regarding how to lift restrictions.

The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis

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

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

Physical Distancing in COVID-19 May Exacerbate Experiences of Social Isolation among People Living with HIV

COVID-19, Pandemic, and Social Distancing

How long will social distancing take to work? Experts weigh in on Canada's COVID-19 response.

Dynamic coupling between the COVID epidemic timeline and the behavioral response to PAUSE in New York State counties

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

The effect of COVID-19 and subsequent social distancing on travel behavior

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.

Six Feet Apart: Lessons Learned from COVID-19 and Social Distancing

The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19

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

Vexing, Veiled, and Inequitable: Social Distancing and the "Rights" Divide in the Age of COVID-19

Although unprecedented in scope and beyond all our life experiences, sweeping social distancing measures are not without historical precedent. Historically, racism, stigma, and discrimination resulted in grossly inequitable application of disease containment measures. But history also provides examples in which broad measures enjoyed remarkable public support. When it comes to COVID-19, blame and division continue to shape containment responses. But the COVID-19 pandemic also resonates with moments in which there was broad social support for containment precisely because lockdowns or stay at home orders are, on the surface, remarkably equitable. Yet even in a context in which a majority of Americans support social distancing, small but coordinated conservative groups are challenging social distancing as a matter of individual rights. In sharp contrast, vulnerable populations, who bear the heaviest burden of disease, have claimed a right to social distancing as a matter of protection.

Modeling the dynamics of COVID19 spread during and after social distancing

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.

Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

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

Further analysis of the impact of distancing upon the COVID-19 pandemic

This paper questions various claims from the paper "Social distancing strategies for curbing the COVID-19 epidemic" by Kissler, Tedijanto, Lipsitch, and Grad: most importantly, the claim that China's "intense" distancing measures achieved only a 60% reduction in R0.

On the benefits of flattening the curve: A perspective()

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

Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19

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

Covid-19: social distancing or social isolation?

Spreading of COVID-19 in Brazil: Impacts and uncertainties in social distancing strategies

Brazil's continental dimension poses a challenge to the control of the spread of COVID-19. Due to the country specific scenario of high social and demographic heterogeneity, combined with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy, the focus of this study is twofold: (i) to develop a generalized SEIRD model that implicitly takes into account the quarantine measures, and (ii) to estimate the response of the COVID-19 spread dynamics to perturbations/uncertainties. By investigating the projections of cumulative numbers of confirmed and death cases, as well as the effective reproduction number, we show that the model parameter related to social distancing measures is one of the most influential along all stages of the disease spread and the most influential after the infection peak. Due to such importance in the outcomes, different relaxation strategies of social distancing measures are investigated in order to determine which strategies are viable and less hazardous to the population. The results highlight the need of keeping social distancing policies to control the disease spread. Specifically, the considered scenario of abrupt social distancing relaxation implemented after the occurrence of the peak of positively diagnosed cases can prolong the epidemic, with a significant increase of the projected numbers of confirmed and death cases. An even worse scenario could occur if the quarantine relaxation policy is implemented before evidence of the epidemiological control, indicating the importance of the proper choice of when to start relaxing social distancing measures.

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29(th)–March 30(th), 2020.

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.

A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing

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

Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak

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.

Vexing, Veiled, and Inequitable: Social Distancing and the "Rights" Divide in the Age of COVID-19.

Evaluating the effectiveness of social distancing interventions against COVID-19

SARS-CoV-2 has infected over 140,000 people as of March 14, 2020. We use a mathematical model to investigate the effectiveness of social distancing interventions lasting six weeks in a middle-sized city in the US. We explore four social distancing strategies by reducing the contacts of adults over 60 years old, adults over 60 years old and children, all adults (25, 75 or 95% compliance), and everyone in the population. Our results suggest that social distancing interventions can avert cases by 20% and hospitalizations and deaths by 90% even with modest compliance within adults as long as the intervention is kept in place, but the epidemic is set to rebound once the intervention is lifted. Our models suggest that social distancing interventions will buy crucial time but need to occur in conjunction with testing and contact tracing of all suspected cases to mitigate transmission of SARS-CoV-2.

How long will social distancing take to work? Experts weigh in on Canada's COVID-19 response

Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis

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.

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

SurviveCovid-19 -- A Game for Improving Awareness of Social Distancing and Health Measures for Covid-19 Pandemic

Pandemics have threatened human race many a times. One of the most important tasks during a pandemic is to bring awareness among people. Bringing awareness contributes a lot in controlling any pandemic. Covid-19 has been causing severe loss to the human race. Considering the mode of spread and the level of severity of this disease, it is extremely important to make people aware of various safety precautions such as using sanitizers and masks and maintaining social distancing, that are to be followed to prevent the disease and break the chain of spread. This mode of educating individuals about the disease is being widely carried out as announcements through online or physical awareness campaigns, advertisements in the media and so on. The younger generations in the present day spend considerably more time on mobile phones and games. However, there are very few mobile applications or games, aimed to bring awareness about a pandemic, which is much lesser in case of Covid-19. Also, considering the lockdown scenario across the world, games also act as a good pass time indoors. Hence, we propose a 2D survival based game, SurviveCovid-19, aimed to educate people about safety precautions to be taken for Covid-19 outside their homes, by incorporating social distancing and usage of masks and sanitizers in the game. SurviveCovid-19 has been designed as an Android based mobile game and has been evaluated through a remote qualitative user survey, with 20 volunteers. The results of the survey are promising with all the questions of survey having mean value greater than 3.6.

Physical Distancing and Emotional Closeness Amidst COVID-19.

Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach

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

Physical distancing amidst social connectivity: time to re-visit ‘social distancing’ as India fights COVID-19

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

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

COVID-19 and the re-opening of schools: a policy maker's dilemma

The epidemic of coronavirus disease 2019 (COVID-19) broke out in Wuhan, China, in December 2019 and rapidly spread across the world. In order to counter this epidemic, several countries put in place different restrictive measures, such as the school's closure and a total lockdown. However, as the knowledge on the disease progresses, clinical evidence showed that children mainly have asymptomatic or mild disease and it has been suggested that they are also less likely to spread the virus. Moreover, the lockdown and the school closure could have negative consequences on children, affecting their social life, their education and their mental health. As many countries have already entered or are planning a phase of gradual lifting of the containment measures of social distancing, it seems plausible that the re-opening of nursery schools and primary schools could be considered a policy to be implemented at an early stage of recovery efforts, putting in place measures to do it safely, such as the maintenance of social distance, the reorganisation of classes into smaller groups, the provision of adequate sanitization of spaces, furniture and toys, the prompt identification of cases in the school environment and their tracing. Therefore, policy makers have the task of balancing pros and cons of the school re-opening strategy, taking into account psychological, educational and social consequences for children and their families. Another issue to be considered is represented by socio-economic disparities and inequalities which could be amplified by school's closure.

Defining Facets of Social Distancing during the COVID-19 Pandemic: Twitter Analysis

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.

The COVID-19 pandemic calls for spatial distancing and social closeness: not for social distancing!

Social distancing and movement constraint as the most likely factors for COVID-19 outbreak control in Brazil

As thousands of new cases of COVID-19 have been confirmed, there is an increasing demand to understand the factors underlying the spread of this disease. Using country-level data, we modeled the early growth in the number of cases for over 480 cities in all Brazilian states. As the main findings, we found that the percentage of people respecting social distancing protocols was the main explanatory factor for the observed growth rate of COVID-19. Those cities that presented the highest spread of the new coronavirus were also those that had lower averages of social distancing. We also underline that total population of cities and connectivity, represented by the city-level importance to the air transportation of people across the country, plays important roles in the dissemination of SARS-CoV-2. Climate and socioeconomic predictors had little contribution to the big-picture scenario. Our results show that different States had high variability in their growth rates, mostly due to quite different public health strategies to retain the outbreak of COVID-19. In spite of all limitations of such a large-scale approach, our results underline that climatic conditions are likely weak limiting factors for the spread of the new coronavirus, and the circulation of people in the city- and country-level are the most responsible factors for the early outbreak of COVID-19 in Brazil. Moreover, we reinforce that social distancing protocols are fundamental to avoid critical scenarios and the collapse of healthcare systems. We also predict that economic-induced decisions for relaxing social distancing might have catastrophic consequences, especially in large cities.

Measuring voluntary social distancing behavior during the COVID-19 pandemic

Staying home is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States has enacted policies to encourage distancing and staying home. An important question is how these policies interact with individuals voluntary responses to COVID-19 cases and deaths. We find evidence of a non-trivial voluntary response to local and nationally reported COVID-19 cases and deaths.

Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020

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.

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020

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.

NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset

Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019, it has affected over 200 countries and billions of people worldwide. This has affected the social life of people owing to enforcements, such as"social distancing"and"stay at home."This has resulted in an increasing interaction through social media. Given that social media can bring us valuable information about COVID-19 at a global scale, it is important to share the data and encourage social media studies against COVID-19 or other infectious diseases. Therefore, we have released a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. This paper also provides a quantitative as well as qualitative analysis of these datasets by creating daily word clouds as an example of text-mining analysis. The dataset is now available on Github. This dataset can be analyzed in a multitude of ways and is expected to help in efficient communication of precautions related to COVID-19.

COVID-19 Datasets: A Survey and Future Challenges

In December 2019, a novel virus named as COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. On the other hand, the medical service providers are the first responders for infected persons with severe symptoms of COVID-19. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open-source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open-source efforts that can be mainly categorized as: (a) COVID-19 diagnosis from CT scans and X-ray images, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We review and critically analyze works in these directions that are accompanied by open-source data and code. We hope that the article will provide the scientific community with an initiative to start open-source extensible and transparent research in the collective fight against COVID-19.

Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China

Strict interventions were successful to control the novel coronavirus (COVID-19) outbreak in China. As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection and disease, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-9 fold during the COVID-19 social distancing period, with most interactions restricted to the household. Children 0-14 years were 59% (95% CI 7-82%) less susceptible than individuals 65 years and over. A transmission model calibrated against these data indicates that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they reduce peak incidence by half and delay the epidemic. These findings can help guide global intervention policies.

COVID-19: How to Relax Social Distancing If You Must

Following the April 16, 2020 release of the Opening Up America Again guidelines for relaxing COVID-19 social distancing policies, local leaders are concerned about future pandemic waves and lack robust strategies for tracking and suppressing transmission. Here, we present a framework for monitoring COVID-19 hospitalization data to project risks and trigger shelter-in-place orders to prevent overwhelming healthcare surges while minimizing the duration of costly lockdowns. Assuming the relaxation of social distancing increases the risk of infection ten-fold, the optimal strategy for Austin, Texas--the fastest-growing large city in the US--will trigger a total of 135 [90% prediction interval: 126-141] days of sheltering, allow schools to open in the fall, and result in an expected 2929 deaths [90% prediction interval: 2837-3026] by September 2021, which is 29% the annual mortality rate. In the months ahead, policy makers are likely to face difficult choices and the extent of public restraint and cocooning of vulnerable populations may save or cost thousands of lives.

COVID-19—does social distancing include species distancing?

Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020.

Stopping the Spread of COVID-19

COVID-19 and rationally layered social distancing

Estimating the impact of COVID-19 control measures using a Bayesian model of physical distancing

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.