Open AccessBayesian nonparametric inference for heterogeneously mixing infectious disease models
Author(s) -
Rowland G. Seymour,
Theodore Kypraios,
Philip D. O’Neill
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2118425119
Subject(s) - inference , nonparametric statistics , infectious disease (medical specialty) , computer science , bayesian probability , bayesian inference , transmission (telecommunications) , econometrics , outbreak , population , data science , disease , parametric statistics , risk analysis (engineering) , machine learning , artificial intelligence , biology , statistics , medicine , mathematics , environmental health , virology , telecommunications , pathology
Significance Mathematical 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.