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DOE OSTI · 2467367

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

Trejo, Imelda·Lin, Yen Ting·Patrick, Amanda·Hengartner, Nicolas W.
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Abstract

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

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97 MATHEMATICS AND COMPUTINGBayesian inferenceBiological scienceCOVID-19 diseaseDisease transmissionEpidemic wavesMathematicsNonhomogeneous mixing modelsReproduction number

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Trejo, Imelda, Lin, Yen Ting, Patrick, Amanda, Hengartner, Nicolas W.. 2023-07-25. Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models. https://doi.org/10.1137/22m1505499

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