Evidence map›Paper›PMID 32577703›Full record

ArticlemedRxiv : the preprint server for health sciences2020

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

Oguzhan Alagoz, Ajay K Sethi, Brian W Patterson, Matthew Churpek, Nasia Safdar

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Oguzhan AlagozDepartment of Industrial and Systems Engineering and Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Ajay K SethiDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Brian W PattersonBerbee Walsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
Matthew ChurpekPulmonary and Critical Care Division in the Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA.
Nasia SafdarInfectious Diseases Division in the Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA and the William S Middleton Memorial Veterans Hospital, Madison WI.

Funding

Modelling applications and systems engineering to reduce infections_the MASTERI study.DP2AI144244 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI SAFDAR, NASIA · 2018 to 2018
$2.3M
NIAID NIH HHS DP2 AI144244
6 · The paper itself

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. Primary Funding Source: National Institute of Allergy and Infectious Diseases Institute.

Indexed as

agent-based simulation modelingCOVID-19novel coronaviruspredicting COVID-19 burdenSARS-CoV-2social distancing measures

Identifiers

PMID32577703
PMCPMC7302402

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.