NONPARAMETRIC INFERENCE FOR THE REPRODUCTIVE RATE IN GENERALIZED COMPARTMENTAL MODELS*

被引：0
|
作者：
Trejo, Imelda [1 ]
Lin, Yen Ting [2 ]
Patrick, Amanda [3 ]
Hengartner, Nicolas [1 ,4 ]
机构：
[1] Theoretical Biology and Biophysics Group (T-6), Theoretical Division, Los Alamos National Laboratory, Los Alamos,NM,87545, United States
[2] Information Sciences Group (CCS-3), Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos,NM,87545, United States
[3] Department of Mathematics, The University of Texas at Arlington, Arlington,TX,76019, United States
[4] Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos,NM,87545, United States
来源：
SIAM Journal on Applied Mathematics | 2024年 / 84卷 / 03期
关键词：
COVID-19;
D O I：
10.1137/22M1505499
中图分类号：
学科分类号：
摘要：
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. © 2024 SIAM. Published by SIAM under the terms of the Creative Commons 4.0 license.
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