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

Judith A Bouman; Anthony Hauser; Simon L Grimm; Martin Wohlfender; Samir Bhatt; Elizaveta Semenova; Andrew Gelman; Christian L Althaus; Julien Riou

doi:10.1371/journal.pcbi.1011575

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

PLoS Comput Biol. 2024 Apr 29;20(4):e1011575. doi: 10.1371/journal.pcbi.1011575. eCollection 2024 Apr.

Authors

Judith A Bouman^{1

2}, Anthony Hauser^{1

3}, Simon L Grimm^{1

4}, Martin Wohlfender^{1

2}, Samir Bhatt^{5

6}, Elizaveta Semenova⁷, Andrew Gelman^{8

9}, Christian L Althaus^{1

2}, Julien Riou¹⁰

Affiliations

¹ Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
² Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland.
³ Institut national de la santé et de la recherche médicale Sorbonne Université (INSERM), Sorbonne Université, Paris, France.
⁴ Center for Space and Habitability, University of Bern, Bern, Switzerland.
⁵ MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom.
⁶ Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
⁷ Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
⁸ Department of Statistics, Columbia University, New York, New York, United States of America.
⁹ Department of Political Science, Columbia University, New York, New York, United States of America.
¹⁰ Department of Epidemiology and Health Systems, Unisanté, Center for Primary Care and Public Health & University of Lausanne, Lausanne, Switzerland.

Abstract

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.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Bayes Theorem*
COVID-19* / epidemiology
COVID-19* / transmission
Computational Biology
Computer Simulation
Humans
SARS-CoV-2*
Switzerland / epidemiology
Workflow*

Grants and funding

This study is funded by the Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland. JR is supported by the Swiss Federal Office of Public Health (142005806) and by the Swiss National Science Foundation (189498). CLA received funding from the European Union’s Horizon 2020 research and innovation program - project EpiPose (No 101003688) and the Swiss National Science Foundation (No 196046). S.B. acknowledges support from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement, and also part of the EDCTP2 programme supported by the European Union. S.B. acknowledges support from the National Institute for Health and Care Research (NIHR) via the Health Protection Research Unit in Modelling and Health Economics, which is a partnership between the UK Health Security Agency (UKHSA), Imperial College London, and the London School of Hygiene &; Tropical Medicine (grant code NIHR200908). (The views expressed are those of the authors and not necessarily those of the UK Department of Health and Social Care, NIHR, or UKHSA.). S.B. acknowledges support from the Novo Nordisk Foundation via The Novo Nordisk Young Investigator Award (NNF20OC0059309). SB acknowledges the Danish National Research Foundation (DNRF160) through the chair grant. S.B. acknowledges support from The Eric and Wendy Schmidt Fund For Strategic Innovation via the Schmidt Polymath Award (G-22-63345). E.S. acknowledges support in part by the AI2050 program at Schmidt Futures (Grant [G-22-64476]). A.G. acknowledges funding from the Office of Naval Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.