Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Alagoz O

PROVIDER: S-EPMC7598093 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

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

Annals of internal medicine 20201027 1

<h4>Background</h4>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.<h4>Objective</h4>To determine the effect of social distancing measures in unique regions.<h4>Design</h4>An agent-based simulation model.<h4>Setting</h4>Age ...[more]

PMID: 33105091

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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.

Project description:BackgroundSocial distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data.ObjectiveWe propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices.MethodsA differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered.ResultsMobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from -0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=-0.37 and r=-0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from -0.12 to -0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect.ConclusionsChronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

Dataset Information

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

Publications

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

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