A Markov Chain Modeling Approach for Predicting Relative Risks of Spatial Clusters in Public Health

TL;DR

This paper introduces a Markov chain-based method for predicting the relative risks of spatial clusters in public health, especially effective with limited longitudinal data, demonstrated on COVID-19 morbidity data.

Contribution

The study develops and tests a new Markov chain modeling approach for sequential risk prediction using limited data, improving accuracy over previous methods.

Findings

Better performance on COVID-19 morbidity data

Increasing time intervals improves prediction accuracy

Outperforms previous mortality-based models

Abstract

Predicting relative risk (RR) of spatial clusters is a complex task in public health that can be achieved through various statistical and machine-learning methods for different time intervals. However, high-resolution longitudinal data is often unavailable to successfully apply such methods. The goal of the present study is to further develop and test a new methodology proposed in our previous work for accurate sequential RR predictions in the case of limited lon gitudinal data. In particular, we first use a well-known likelihood ratio test to identify significant spatial clusters over user-defined time intervals. Then we apply a Markov chain modeling ap approach to predict RR values for each time interval. Our findings demonstrate that the proposed approach yields better performance with COVID-19 morbidity data compared to the previous study on mortality data. Additionally, increasing the number of time intervals enhances the accuracy of the proposed Markov chain modeling method.