A Markov Chain Modeling Approach for Predicting Relative Risks of Spatial Clusters in Public Health
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908728424398848 |
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| author | Iamrache, Lyza Rekab, Kamel Bani-Yagoub, Majid Pluta, Julia Mehailia, Abdelghani |
| author_facet | Iamrache, Lyza Rekab, Kamel Bani-Yagoub, Majid Pluta, Julia Mehailia, Abdelghani |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Markov Chain Modeling Approach for Predicting Relative Risks of Spatial Clusters in Public Health Iamrache, Lyza Rekab, Kamel Bani-Yagoub, Majid Pluta, Julia Mehailia, Abdelghani Methodology Applications 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. |
| title | A Markov Chain Modeling Approach for Predicting Relative Risks of Spatial Clusters in Public Health |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2512.19635 |