Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19 release_cra6kj3e3rdlbaigqfcqepo4cu

by Mathias Peirlinck, Kevin Linka, Francisco Sahli Costabal, Eran Bendavid, Jayanta Bhattacharya, John Ioannidis, Ellen Kuhl

Published by Cold Spring Harbor Laboratory.



Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model learns, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study reveals that the outbreak dynamics of COVID-19 are sensitive to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For three distinct locations, Santa Clara County (CA, USA), New York City (NY, USA), and Heinsberg (NRW, Germany), our model estimates the fraction of the population that has been infected and recovered by May 13, 2020 to 6.2% (95% CI: 3.3%-9.0%), 22.7% (95% CI: 15.7%-29.8%), and 20.5% (95% CI: 17.0%-24.3%). Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: January 16, 2020 - January 24, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, if needed, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.
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