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

Author

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

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.Author summary: Mathematical models are a central tool for understanding the spread of infectious diseases. These models can frequently be fitted to surveillance data such as the number of laboratory-confirmed cases and seroprevalence over time. We identified that in these situations, four crucial features are required for a model to provide insightful information for managing an epidemic. These features relate to the adjustment for incomplete case ascertainment, to the choice of sampling distribution, to the variation of transmission over time and to the stratification by age. For each feature, we identify and compare several implementation options on simulated data. This structural comparison of methods results in a Bayesian workflow that is optimized for modeling the transmission of SARS-CoV-2 over a short period. We highlight the advantages and limitations of our approach in a real situation, using real-world SARS-CoV-2 data from the canton of Geneva. In addition to providing validated solutions to important technical points, such a comprehensive workflow helps to improve the reliability and the transparency of epidemic models.

Suggested Citation

  • Judith A Bouman & Anthony Hauser & Simon L Grimm & Martin Wohlfender & Samir Bhatt & Elizaveta Semenova & Andrew Gelman & Christian L Althaus & Julien Riou, 2024. "Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models," PLOS Computational Biology, Public Library of Science, vol. 20(4), pages 1-19, April.
    • Handle: RePEc:plo:pcbi00:1011575
    DOI: 10.1371/journal.pcbi.1011575
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    1. repec:plo:pcbi00:1006211 is not listed on IDEAS
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Lorenzo Pellis & Simon Cauchemez & Neil M. Ferguson & Christophe Fraser, 2020. "Systematic selection between age and household structure for models aimed at emerging epidemic predictions," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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