Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models.

Autor: Bouman JA; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland., Hauser A; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Institut national de la santé et de la recherche médicale Sorbonne Université (INSERM), Sorbonne Université, Paris, France., Grimm SL; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Center for Space and Habitability, University of Bern, Bern, Switzerland., Wohlfender M; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland., Bhatt S; MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom.; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark., Semenova E; Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom., Gelman A; Department of Statistics, Columbia University, New York, New York, United States of America.; Department of Political Science, Columbia University, New York, New York, United States of America., Althaus CL; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland., Riou J; Department of Epidemiology and Health Systems, Unisanté, Center for Primary Care and Public Health & University of Lausanne, Lausanne, Switzerland.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Apr 29; Vol. 20 (4), pp. e1011575. Date of Electronic Publication: 2024 Apr 29 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1011575
Abstrakt: Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Bouman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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