Simulated Identification of Silent COVID-19 Infections Among Children and Estimated Future Infection Rates With Vaccination
JournalJAMA Network Open
PublisherAmerican Medical Association
MetadataShow full item record
AbstractOBJECTIVE: 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.
Keywordsilent COVID-19 infection
simulation modeling study
Disease Transmission, Infectious
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/15515
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