Zero-state Coupled Markov Switching Count Models for Spatio-temporal Infectious Disease Spread

TL;DR

This paper introduces a novel zero-state coupled Markov switching negative binomial model to better analyze and predict spatio-temporal infectious disease spread, accounting for excess zeros and separate dynamics of disease reemergence.

Contribution

It develops a new coupled Markov switching model that captures the presence and absence of disease in areas, improving analysis of heterogeneity and reemergence dynamics.

Findings

Model effectively captures disease presence and absence states.

Bayesian inference applied to dengue fever data in Rio de Janeiro.

Improved quantification of space-time heterogeneity in disease spread.

Abstract

Spatio-temporal counts of infectious disease cases often contain an excess of zeros. With existing zero inflated count models applied to such data it is difficult to quantify space-time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous Markov chains coupled between neighboring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible 0 representing the disease being undetected. Bayesian inference and prediction is illustrated using spatio-temporal counts of dengue fever cases in Rio de Janeiro, Brazil.