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

Bouman, Judith A; Hauser, Anthony; Grimm, Simon L; Wohlfender, Martin; Bhatt, Samir; Semenova, Elizaveta; Gelman, Andrew; Althaus, Christian L; Riou, Julien (2024). Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models. PLoS computational biology, 20(4), e1011575. Public Library of Science 10.1371/journal.pcbi.1011575

[img]
Preview
Text
Bouman_PLoSComputBiol_2024.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
08 Faculty of Science > Physics Institute

UniBE Contributor:

Bouman, Judith Aveline, Hauser, Anthony Willy, Grimm, Simon Lukas, Wohlfender, Martin Samuel, Althaus, Christian

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 530 Physics

ISSN:

1553-734X

Publisher:

Public Library of Science

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Pubmed Import

Date Deposited:

30 Apr 2024 10:05

Last Modified:

05 Jun 2024 17:53

Publisher DOI:

10.1371/journal.pcbi.1011575

PubMed ID:

38683878

BORIS DOI:

10.48350/196381

URI:

https://boris.unibe.ch/id/eprint/196381

Actions (login required)

Edit item Edit item
Provide Feedback