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

TL;DR

This paper introduces a flexible semiparametric Bayesian framework for modeling infectious disease spread, capable of handling incomplete data, analyzing contact networks, and identifying superspreaders, demonstrated through simulations and MERS data.

Contribution

It presents a novel semiparametric Bayesian approach that addresses incomplete data and network structure in epidemic modeling, with practical application to MERS in South Korea.

Findings

Effective modeling of contact networks and superspreaders.

Ability to handle incomplete epidemic data.

Successful application to MERS outbreak data.

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 modeling 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 modeling 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.