R0 may not be the best way to keep track of COVID-19
Embargoed until:
Publicly released:
2020-11-04 11:01
International scientists say R0 - the basic reproductive number used to indicate the spread of infectious diseases such as COVID-19 - is too simple to reflect the complex process of a virus spreading through the population. They point out that Spanish flu in 1918 and ebola in 2013 had the same R0, but one caused a pandemic while the other wreaked havoc in just three countries. They say that without data gathered through contact-tracing to clarify how viruses or bacteria are spreading, scientists cannot accurately forecast outbreak sizes.
Journal/conference: Journal of the Royal Society Interface
Link to research (DOI): 10.1098/rsif.2020.0393
Organisation/s: University of Vermont, USA
Funder: L.H.-D. acknowledges support from the National Institutes of Health 1P20 GM125498-01 Centers of Biomedical Research Excellence Award. B.M.A. is supported by Bill and Melinda Gates through the Global Good Fund. S.V.S. is supported by startup funds provided by Northeastern University. A.A. acknowledges financial support
from the Sentinelle Nord initiative of the Canada First Research Excellence Fund and from the Natural Sciences and Engineering Research Council of Canada (project 2019-05183). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Media release
From: The Royal Society
Beyond R0: Heterogeneity in secondary infections and probabilistic epidemic forecasting
The basic reproductive number, R0, is commonly used to describe how fast and far a pathogen will spread in a population. But why use only a single number to describe the tremendously complex process that dictates how a bug spreads? Influenza in 1918 and ebola in 2013 had the same R0, but one caused a pandemic and one wreaked havoc in just three countries. We reformulate a classic result in network theory to show how outbreaks driven by super-spreading events are poorly described by commonly-used models. We demonstrate that without data gathered through contact-tracing on the heterogeneity in secondary infections for diseases like COVID-19, we cannot accurately forecast outbreak sizes.
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