Evidence map›Paper›PMID 32632012›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

The implications of silent transmission for the control of COVID-19 outbreaks.

Seyed M Moghadas, Meagan C Fitzpatrick, Pratha Sah, Abhishek Pandey, Affan Shoukat, Burton H Singer, Alison P Galvani

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 267 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
267citing papers in PubMed, 4 pooled it
–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

267 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Pooled it
  4. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Setting-specific Transmission Rates: A Systematic Review and Meta-analysis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Pooled it
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  15. Ideological Consistency and News Sharing as Predictors of Masking Among College Students.International journal of environmental research and public health · 2024
    Article
  16. Routine immunization againstThe Lancet regional health. Southeast Asia · 2024
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207 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Seyed M MoghadasAgent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada.
Meagan C FitzpatrickCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510.
Pratha SahCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510.
Abhishek PandeyCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510.
Affan ShoukatCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510.
Burton H SingerEmerging Pathogens Institute, University of Florida, Gainesville, FL 32610 bhsinger@epi.ufl.edu alison.galvani@yale.edu.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06510; bhsinger@epi.ufl.edu alison.galvani@yale.edu.

Funding

Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial dataR01AI151176 · NIAID · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2020 to 2024
$3.9M
Mathematical modeling to optimize rotavirus vaccination in sub-Saharan AfricaK01AI141576 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI FITZPATRICK, MEAGAN COLLEEN · 2019 to 2023
$649k
NIAID NIH HHS K01 AI141576NIAID NIH HHS R01 AI151176NIH HHS 1K01AI141576-01NIH HHS 1R01AI151176-01
6 · The paper itself

Abstract

Since the emergence of coronavirus disease 2019 (COVID-19), unprecedented movement restrictions and social distancing measures have been implemented worldwide. The socioeconomic repercussions have fueled calls to lift these measures. In the absence of population-wide restrictions, isolation of infected individuals is key to curtailing transmission. However, the effectiveness of symptom-based isolation in preventing a resurgence depends on the extent of presymptomatic and asymptomatic transmission. We evaluate the contribution of presymptomatic and asymptomatic transmission based on recent individual-level data regarding infectiousness prior to symptom onset and the asymptomatic proportion among all infections. We found that the majority of incidences may be attributable to silent transmission from a combination of the presymptomatic stage and asymptomatic infections. Consequently, even if all symptomatic cases are isolated, a vast outbreak may nonetheless unfold. We further quantified the effect of isolating silent infections in addition to symptomatic cases, finding that over one-third of silent infections must be isolated to suppress a future outbreak below 1% of the population. Our results indicate that symptom-based isolation must be supplemented by rapid contact tracing and testing that identifies asymptomatic and presymptomatic cases, in order to safely lift current restrictions and minimize the risk of resurgence.

Indexed as

AdolescentAdultAgedAsymptomatic InfectionsBetacoronavirusChildChild, PreschoolContact TracingCoronavirus InfectionsCOVID-19FemaleHumansIncidenceInfantInfant, NewbornInfection Controlcase isolationcontact tracingCOVID-19

Identifiers

PMID32632012
PMCPMC7395516

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.