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Finding the infectious dose for COVID-19 by applying an airborne-transmission model to superspreader events

Mara Prentiss, Arthur Chu, Karl K. Berggren

2022PLoS ONE52 citationsDOIOpen Access PDF

Abstract

We probed the transmission of COVID-19 by applying an airborne transmission model to five well-documented case studies-a Washington state church choir, a Korean call center, a Korean exercise class, and two different Chinese bus trips. For all events the likely index patients were pre-symptomatic or mildly symptomatic, which is when infective patients are most likely to interact with large groups of people. Applying the model to those events yields results that suggest the following: (1) transmission was airborne; (2) superspreading events do not require an index patient with an unusually high viral load; (3) the viral loads for all of the index patients were of the same order of magnitude and consistent with experimentally measured values for patients at the onset of symptoms, even though viral loads across the population vary by a factor of >108. In particular we used a Wells-Riley exposure model to calculate q, the total average number of infectious quanta inhaled by a person at the event. Given the q value for each event, the simple airborne transmission model was used to determined Sq, the rate at which the index patient exhaled infectious quanta and N0, the characteristic number of COVID-19 virions needed to induce infection. Despite the uncertainties in the values of some parameters of the superspreading events, all five events yielded (N0∼300-2,000 virions), which is similar to published values for influenza. Finally, this work describes the conditions under which similar methods can provide actionable information on the transmission of other viruses.

Topics & Concepts

Transmission (telecommunications)Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Viral loadAirborne transmissionPopulationVirologyTransmission rateMedicineIndex caseSars virusDemographyStatisticsInfectious disease (medical specialty)Internal medicineMathematicsVirusEnvironmental healthComputer scienceDiseaseOutbreakTelecommunicationsSociologyInfection Control and VentilationCOVID-19 epidemiological studiesSARS-CoV-2 detection and testing
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