Evidence map›Paper›PMID 34650141›Full record

ArticleScientific reports2021

Epidemiological and economic impact of COVID-19 in the US.

Jiangzhuo Chen, Anil Vullikanti, Joost Santos, Srinivasan Venkatramanan, Stefan Hoops, Henning Mortveit, Bryan Lewis, Wen You, Stephen Eubank, Madhav Marathe and 2 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
5.9field-weighted citation impact, top 3% of its field
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

33 citing papers in PubMed, 73 citations in OpenAlex.

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  14. Stabilizing the return to normal behavior in an epidemic.medRxiv : the preprint server for health sciences · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Jiangzhuo ChenNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Anil VullikantiNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Joost SantosDepartment of Engineering Management and Systems Engineering, George Washington University, Washington, DC, 20052, USA.
Srinivasan VenkatramananNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Stefan HoopsNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Henning MortveitNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Bryan LewisNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Wen YouDepartment of Public Health Sciences, University of Virginia, Charlottesville, USA.
Stephen EubankNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Madhav MaratheNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Chris BarrettNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.
Achla MaratheNetwork Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA. achla@virginia.edu.
University of Virginia · USBiocom · USEngineering Systems (United States) · USGeorge Washington University · US

Funding

Systems Analysis of Social Pathways of Epidemics to Reduce Health DisparitiesR01GM109718 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI MARATHE, ACHLA, VULLIKANTI, ANIL · 2014 to 2022
$3.6M
Chinese Center for Disease Control and Prevention (China CDC) 75D30119C05935NIGMS NIH HHS R01 GM109718U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (CDC) 75D30119C05935
6 · The paper itself

Abstract

This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of the lockdown. Sectors that are worst hit are not the labor-intensive sectors such as the Agriculture sector and the Construction sector, but the ones with high valued jobs such as the Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.

Indexed as

AgricultureCommunicable Disease ControlConstruction IndustryCOVID-19EmploymentHumansIndustryModels, EconomicSARS-CoV-2TeleworkingUnited States

Identifiers

PMID34650141
PMCPMC8517017
OpenAlexW3205855894

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.