Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events
- Fred Hutchinson Cancer Research Center, United States
Abstract
SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2 infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than one day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 towards super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.
Data availability
The original data and code is shared at: https://github.com/ashish2goyal/SARS_CoV_2_Super_Spreader_Event
Article and author information
Author details
Funding
National Institute of Allergy and Infectious Diseases (R01 AI121129-05S1)
- Joshua T Schiffer
Council of State and Territorial Epidemiologists (Inform Public Health Decision Making Funding Opportunity)
- Joshua T Schiffer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Goyal et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events
eLife 10:e63537.
https://doi.org/10.7554/eLife.63537
Further reading
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- Medicine
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