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

ABSTRACT: 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.

SUBMITTER: Alagoz O

PROVIDER: S-EPMC7302402 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Alagoz Oguzhan O Sethi Ajay K AK Patterson Brian W BW Churpek Matthew M Safdar Nasia N

medRxiv : the preprint server for health sciences 20200609

<h4>Background</h4>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.<h4>Methods</h4>We developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simula ...[more]

PMID: 32577703

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Project description:BACKGROUND:Across the United States, various social distancing measures were implemented to control the spread of coronavirus disease 2019 (COVID-19). However, the effectiveness of such measures for specific regions with varying population demographic characteristics and different levels of adherence to social distancing is uncertain. OBJECTIVE:To determine the effect of social distancing measures in unique regions. DESIGN:An agent-based simulation model. SETTING:Agent-based model applied to Dane County, Wisconsin; the Milwaukee metropolitan (metro) area; and New York City (NYC). PATIENTS:Synthetic population at different ages. INTERVENTION:Different times for implementing and easing social distancing measures at different levels of adherence. MEASUREMENTS:The model represented the social network and interactions among persons in a region, considering population demographic characteristics, limited testing availability, "imported" infections, asymptomatic disease transmission, and age-specific adherence to social distancing measures. The primary outcome was the total number of confirmed COVID-19 cases. RESULTS:The timing of and adherence to social distancing had a major effect on COVID-19 occurrence. In NYC, implementing social distancing measures 1 week earlier would have reduced the total number of confirmed cases from 203 261 to 41 366 as of 31 May 2020, whereas a 1-week delay could have increased the number of confirmed cases to 1 407 600. A delay in implementation had a differential effect on the number of cases in the Milwaukee metro area versus Dane County, indicating that the effect of social distancing measures varies even within the same state. LIMITATION:The effect of weather conditions on transmission dynamics was not considered. CONCLUSION:The timing of implementing and easing social distancing measures has major effects on the number of COVID-19 cases. PRIMARY FUNDING SOURCE:National Institute of Allergy and Infectious Diseases.

Project description:IntroductionGovernments and public health authorities across many jurisdictions implemented social (physical) distancing measures to contain the spread of the 2019 novel coronavirus disease (COVID-19). Adherence to these measures is variable and likely influenced by various factors. This study aimed to 1) identify the individual sociodemographic, COVID-19 and social distancing related, and psychological determinants of social distancing adherence, and 2) explore regional differences in social distancing adherence in the United States (U.S.) and English-speaking Canada based on each region's discrepant response to social distancing restrictions.MethodsA web-based repeated cross-sectional survey was conducted in 4,942 English-speaking participants from the four most populous U.S. states, specifically New York, California, Texas, and Florida, and Canada (www.covid19-database.com). The study was conducted at two timepoints, from May 1 to 5, 2020 (n = 1,019, Canadian participants only) and from July 6 to 10, 2020 (n = 3,923). Separate univariate models were computed for individual sociodemographic, COVID-19 and social distancing related, and psychological determinants of social distancing adherence. To determine the total variance explained, a univariate analysis including all of the determinants was performed. Regional differences in social distancing were compared between the four U.S. states and Canada, and between the U.S. as a whole and Canada.ResultsAdherence to social distancing was higher in May (mean = 4.4/5.0±0.7) compared to July (mean = 4.3/5.0±0.7) [t (4940) = 6.96, p < 0.001], likely a reflection of relaxing restrictions. There were no regional differences in adherence. Sociodemographic, COVID-19 and social distancing related, and psychological determinants explained 10, 36, and 23% of the variance of social distancing adherence, respectively. Higher perceived seriousness of COVID-19 [β (SE) = 0.39 (0.01), p < 0.001, partial η2 = 0.22], lower risk propensity [β (SE) = -0.15 (0.01), p < 0.001, partial η2 = 0.06], germ aversion [β (SE) = 0.12 (0.01), p < 0.001, partial η2 = 0.03], age [β (SE) = 0.01 (0.00), p < 0.001, partial η2 = 0.02], and greater social support [β (SE) = 0.03 (0.00), p < 0.001, partial η2 = 0.02] had the largest effects on social distancing adherence.ConclusionPublic service initiatives to emphasize the serious consequences of infection and targeted interventions toward certain sociodemographic groups, such as younger adults and vulnerable individuals in greater need of social support, may help enhance the public's adherence to social distancing measures during subsequent waves of COVID-19 and future pandemics.

Project description:Social distancing measures implemented by governments worldwide during the COVID-19 pandemic have proven an effective intervention to control the transmission of SARS-CoV-2. There is a growing literature on predictors of adherence behaviours to social distancing measures, however, there are no comprehensive insights into the nature and types of non-adherence behaviours. To address this gap in the literature, we studied non-adherence in terms of counts of infringements and people's accounts on their behaviours in a sample of North London residents. We focused on the following social distancing rules: keeping 2 mts. distancing, meeting family and friends, and going out for non-essential reasons. A mixed-methods explanatory sequential design was used comprising an online survey (May 1-31, 2020) followed by semi-structured in-depth interviews held with a purposive sample of survey respondents (August 5 -September 21, 2020). A negative binomial regression model (quantitative) and Framework Analysis (qualitative) were undertaken.681 individuals completed the survey, and 30 individuals were interviewed. We integrated survey and interview findings following three levels of the Social Ecological model: individual, interpersonal and community levels. We identified non-adherence behaviours as unintentional (barriers beyond individual's control) and intentional (deliberate decision). Unintentional adherence was reported by interviewees as, lack of controllability in keeping 2 mts. distancing, environmental constraints, social responsibility towards the community and feeling low risk. Intentional non-adherence was statistically associated with and reported as lack of trust in Government, support from friends, and lack of knowledge about rules. In addition, interviewees reported individual risk assessment and decision making on the extent to following the rules, and perceived lack of adherence in the local area. Our findings indicate that unintentional and intentional non-adherence should be improved by Government partnerships with local communities to build trust in social distancing measures; tailored messaging to young adults emphasising the need of protecting others whilst clarifying the risk of transmission; and ensuring COVID-secured environments by working with environmental health officers.

Project description:Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-)data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that overall population-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (> 65+), susceptibility for the youngest age group (0-17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18-44 and 0.75 [0.68, 0.82] for 45-64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (> 45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 6+ group). Finally, we find heterogeneity in case reporting among different age groups, with the lowest rate occurring among the 0-17 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

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

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

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