Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models
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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.
In: PLOS Computational Biology, Vol. 20, No. 4 April, e1011575, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models
AU - Bouman, Judith A.
AU - Hauser, Anthony
AU - Grimm, Simon L.
AU - Wohlfender, Martin
AU - Bhatt, Samir
AU - Semenova, Elizaveta
AU - Gelman, Andrew
AU - Althaus, Christian L.
AU - Riou, Julien
N1 - Publisher Copyright: © 2024 Bouman et al.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pcbi.1011575
DO - 10.1371/journal.pcbi.1011575
M3 - Journal article
C2 - 38683878
AN - SCOPUS:85191657919
VL - 20
JO - P L o S Computational Biology (Online)
JF - P L o S Computational Biology (Online)
SN - 1553-734X
IS - 4 April
M1 - e1011575
ER -
ID: 391582921