A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015

Michael Schweinberger, Rashmi P. Bomiriya, Sergii Babkin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modelling framework with at least three advantages. First, it enables researchers to study the structure of a population contact network and its impact on the spread of infectious diseases. Second, it can accommodate short- and long-tailed degree distributions and detect potential superspreaders, who represent an important public health concern. Third, it addresses the important issue of incomplete data. Starting from first principles, we show when the incomplete-data generating process is ignorable for the purpose of Bayesian inference for the parameters of the population model. We demonstrate the semiparametric modelling framework by simulations and an application to the partially observed MERS epidemic in South Korea in 2015. We conclude with an extended discussion of open questions and directions for future research.

Original languageEnglish (US)
Pages (from-to)628-662
Number of pages35
JournalJournal of Nonparametric Statistics
Volume34
Issue number3
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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