Ensemble learning methods of inference for spatially stratified infectious disease systems

Peitsch Jeffrey, Pokharel Gyanendra () and Hossain Shakhawat
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Peitsch Jeffrey: Department of Mathematics and Statistics, 2129 University of Calgary , Calgary, AB, Canada
Pokharel Gyanendra: Department of Mathematics and Statistics, 8665 University of Winnipeg , Winnipeg, MB, Canada
Hossain Shakhawat: Department of Mathematics and Statistics, 8665 University of Winnipeg , Winnipeg, MB, Canada

The International Journal of Biostatistics, 2024, vol. 20, issue 2, 507-529

Abstract: Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

Keywords: infectious disease; ensemble learning; epidemic; spatial stratification; foot-and-mouth disease (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2023-0102

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