Work

A Markov chain modeling approach for predicting relative risks of spatial clusters in public health

Year: 2026

Type: article

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 longitudinal 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 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.

Source: Discover Public Health

DOI: 10.1186/s12982-026-02009-7

Authors: Lyza Iamrache, Kamel Rekab, Majid Bani Yaghoub, Julia Pluta, Abdelghani Mehailia

Institutions: University of Missouri–Kansas City, Cornell University, Yorkville University

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: National Science Foundation