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Simulated Identification of Silent COVID-19 Infections Among Children and Estimated Future Infection Rates With Vaccination

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Key Points

Question  Is a targeted strategy for identification of silent COVID-19 infections among children in the absence of their vaccination associated with reduced infection rates in the general population?

Findings  In this simulation modeling study, identifying 10% to 20% of silent infections among children within 3 days after infection would bring attack rates below 5% if only adults were vaccinated. If silent infections among children remained undetected, achieving the same attack rate would require an unrealistically high vaccination coverage (≥81%) of this age group, in addition to vaccination of adults.

Meaning  These findings suggest that rapid identification of silent infections among children may achieve comparable effects as would their vaccination.

Abstract

Importance  A significant proportion of COVID-19 transmission occurs silently during the presymptomatic and asymptomatic stages of infection. Children, although important drivers of silent transmission, are not included in the current COVID-19 vaccination campaigns.

Objective  To estimate the benefits of identifying silent infections among children as a proxy for their vaccination.

Design, Setting, and Participants  This study used an age-structured disease transmission model, parameterized with census data and estimates from published literature, to simulate the estimated synergistic effect of interventions in reducing attack rates during the course of 1 year among a synthetic population representative of the US demographic composition. The population included 6 age groups of 0 to 4, 5 to 10, 11 to 18, 19 to 49, 50 to 64, and 65 years or older based on US census data. Data were analyzed from December 12, 2020, to February 26, 2021.

Exposures  In addition to the isolation of symptomatic cases within 24 hours of symptom onset, vaccination of adults was implemented to reach a 40% to 60% coverage during 1 year with an efficacy of 95% against symptomatic and severe COVID-19.

Main Outcomes and Measures  The combinations of proportion and speed for detecting silent infections among children that would suppress future attack rates to less than 5%.

Results  In the base-case scenarios with an effective reproduction number Re = 1.2, a targeted approach that identifies 11% of silent infections among children within 2 days and 14% within 3 days after infection would bring attack rates to less than 5% with 40% vaccination coverage of adults. If silent infections among children remained undetected, achieving the same attack rates would require an unrealistically high vaccination coverage (≥81%) of this age group, in addition to 40% vaccination coverage of adults. The estimated effect of identifying silent infections was robust in sensitivity analyses with respect to vaccine efficacy against infection and reduced susceptibility of children to infection.

Conclusions and Relevance  In this simulation modeling study of a synthetic US population, in the absence of vaccine availability for children, a targeted approach to rapidly identify silent COVID-19 infections in this age group was estimated to significantly mitigate disease burden. These findings suggest that without measures to interrupt transmission chains from silent infections, vaccination of adults is unlikely to contain the outbreaks in the near term.

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Article Information

Accepted for Publication: February 24, 2021.

Published: April 23, 2021. doi:10.1001/jamanetworkopen.2021.7097

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Moghadas SM et al. JAMA Network Open.

Corresponding Author: Alison P. Galvani, PhD, Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, 135 College, Ste 200, New Haven, CT 06520 (alison.galvani@yale.edu).

Author Contributions: Drs Moghadas, Fitzpatrick, and Shoukat contributed equally to this study. Dr Moghadas had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Moghadas, Fitzpatrick, Shoukat, Galvani.

Acquisition, analysis, or interpretation of data: Moghadas, Shoukat, Zhang, Galvani.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: Moghadas, Fitzpatrick, Zhang.

Statistical analysis: Moghadas, Shoukat, Zhang.

Obtained funding: Moghadas, Galvani.

Administrative, technical, or material support: Moghadas, Zhang.

Supervision: Moghadas, Galvani.

Conflict of Interest Disclosures: Dr Fitzpatrick reported receiving grants from National Institutes of Health (NIH) during the conduct of the study and personal fees from Sanofi Pasteur outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by grant OV4-170643 COVID-19 Rapid Research from the Canadian Institutes of Health Research, grants 1RO1AI151176-01 and 1K01AI141576-01 from the NIH, and grants RAPID 2027755 and CCF-1918784 from the National Science Foundation.

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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