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Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

ABSTRACT: SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

SUBMITTER: Seymour RG

PROVIDER: S-EPMC8915959 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

Seymour Rowland G RG Kypraios Theodore T O'Neill Philip D PD

Proceedings of the National Academy of Sciences of the United States of America 20220301 10

SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predict ...[more]

PMID: 35238628

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