Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan;174(1):50-57.
doi: 10.7326/M20-4096. Epub 2020 Oct 27.

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

Oguzhan Alagoz  1 , Ajay K Sethi  1 , Brian W Patterson  2 , Matthew Churpek  2 , Nasia Safdar  3
Affiliations

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

Oguzhan Alagoz et al. Ann Intern Med. 2021 Jan.

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.

PubMed Disclaimer

Conflict of interest statement

Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-4096.

Figures

Visual Abstract.
Visual Abstract.. Predicting COVID-19 Trends Over Time
During the COVID-19 pandemic, various social distancing measures were implemented to reduce transmission of the virus. The effect of easing these measures on SARS-CoV-2 transmission is uncertain. This study, which involved an agent-based simulation model, considers the effect of implementing and easing social distancing measures at different levels of adherence on the total number of COVID-19 cases in 3 urban communities.
Figure 1.
Figure 1.. Progression of COVID-19 in persons in COVAM.
Ovals and rectangles represent the transient and absorbing states, respectively, that a person could be in; arrows show possible transitions among various states; and values on arrows represent the probability and mean duration of transitioning from one state to another. The notation used in the figure is described in Appendix Table. COVAM = COVID-19 Agent-based simulation Model; COVID-19 = coronavirus disease 2019.
Figure 2.
Figure 2.. Comparison of total number of confirmed cases time for implementation of social distancing at different dates in Dane County (
top ), Milwaukee metropolitan area ( middle ), and New York City ( bottom ).
See this image and copyright information in PMC

Update of

Comment in

References

    1. Davies NG, Kucharski AJ, Eggo RM, et al; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet Public Health. 2020;5:e375-e385. [PMID: 32502389] doi:10.1016/S2468-2667(20)30133-X - PMC - PubMed
    1. The White House. Guidelines for opening up America again. Accessed at www.whitehouse.gov/openingamerica on 29 April 2020.
    1. Modeling COVID-19 spread vs healthcare capacity. Accessed at https://alhill.shinyapps.io/COVID19seir on 2 April 2020.
    1. Predictive Healthcare at Penn Medicine. COVID-19 hospital impact model for epidemics (CHIME). Accessed at https://penn-chime.phl.io on 2 April 2020.
    1. Institute for Health Metrics and Evaluation. COVID-19 projections. Accessed at https://covid19.healthdata.org/united-states-of-america on 26 April 2020.

Publication types