A simple model for how the risk of pandemics from different virus families depends on viral and human traits
Name
2021.03.17.21253813v1.full.pdf
Description
Submitted version
Size
757.81 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Doelger, Julia
Chakraborty, Arup K
Kardar, Mehran
Date Issued
2022
Journal
Mathematical Biosciences
Publisher
Elsevier BV
Citation
Doelger, Julia, Chakraborty, Arup K and Kardar, Mehran. 2022. "A simple model for how the risk of pandemics from different virus families depends on viral and human traits." Mathematical Biosciences, 343.
Version
Original manuscript
Abstract
Different virus families, like influenza or corona viruses, exhibit characteristic traits such as typical modes of transmission and replication as well as specific animal reservoirs in which each family of viruses circulate. These traits of genetically related groups of viruses influence how easily an animal virus can adapt to infect humans, how well novel human variants can spread in the population, and the risk of causing a global pandemic. Relating the traits of virus families to their risk of causing future pandemics, and identification of the key time scales within which public health interventions can control the spread of a new virus that could cause a pandemic, are obviously significant. We address these issues using a minimal model whose parameters are related to characteristic traits of different virus families. A key trait of viruses that "spillover" from animal reservoirs to infect humans is their ability to propagate infection through the human population (fitness). We find that the risk of pandemics emerging from virus families characterized by a wide distribution of the fitness of spillover strains is much higher than if such strains were characterized by narrow fitness distributions around the same mean. The dependences of the risk of a pandemic on various model parameters exhibit inflection points. We find that these inflection points define informative thresholds. For example, the inflection point in variation of pandemic risk with time after the spillover represents a threshold time beyond which global interventions would likely be too late to prevent a pandemic.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Ragon Institute of MGH, MIT and Harvard
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.MBS.2021.108732
