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.
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Research ethics: Ethics approval is not required as this research do not include any studies on human and animals.
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Author contributions: All authors involved in drafting the manuscript or riving it critically for intellectual content, and all authors approved the final version to be published. Study concept and design: Pokharel. Modelling and data analysis: Peitch and Pokharel. Interpretation of the results: Pokharel, Peitch and Hossain.
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Competing interests: Authors have nothing to declare.
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Research funding: This work was funded by Natural Sciences and Engineering Research Council of Canada (NSERC), and NSERC Undergraduate Student Research Award (NSERC-USRA).
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Data availability: The data we utilized was obtained from the United Kingdom Government, Department of Environment. Food and Rural Affairs (DEFRA). Wo do not have permission to redistribute the data. If readers wish to obtain the data, they may contact Dr. Pokharel or DEFRA directly at defra.helpline@defra.gsi.gov.uk.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijb-2023-0102).
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