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arXiv · 2512.19635

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

Lyza Iamrache·Kamel Rekab·Majid Bani-Yagoub·Julia Pluta·Abdelghani Mehailia
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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.

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BibTeXRIS

Lyza Iamrache, Kamel Rekab, Majid Bani-Yagoub, Julia Pluta, Abdelghani Mehailia. 2025-12-22. A Markov Chain Modeling Approach for Predicting Relative Risks of Spatial Clusters in Public Health. https://arxiv.org/abs/2512.19635

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