The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
01 2021
Historique:
received: 30 04 2020
accepted: 27 10 2020
entrez: 7 1 2021
pubmed: 8 1 2021
medline: 15 1 2021
Statut: epublish

Résumé

Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration.

Identifiants

pubmed: 33411742
doi: 10.1371/journal.pcbi.1008470
pii: PCOMPBIOL-D-20-00729
pmc: PMC7790237
doi:

Substances chimiques

COVID-19 Vaccines 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1008470

Subventions

Organisme : NIH HHS
ID : 5R01HD086013-02
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM127512
Pays : United States
Organisme : NIH HHS
ID : 1 R01 GM127512-01A1
Pays : United States
Organisme : NICHD NIH HHS
ID : U01 HD086861
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD086013
Pays : United States
Organisme : AHRQ HHS
ID : R01 HS023317
Pays : United States

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Bruce Y Lee (BY)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Sarah M Bartsch (SM)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Marie C Ferguson (MC)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Patrick T Wedlock (PT)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Kelly J O'Shea (KJ)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Sheryl S Siegmund (SS)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

Sarah N Cox (SN)

Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York Graduate School of Public Health and Health Policy, New York City, New York, United States of America.

James A McKinnell (JA)

Infectious Disease Clinical Outcomes Research Unit (ID-CORE), Lundquist Institute, Harbor-UCLA Medical Center, Torrance, California, United States of America.
Torrance Memorial Medical Center, Torrance, California, United States of America.

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Classifications MeSH