ArticleHealth science reports2025
Predicting Infectious Disease Incidence After Flooding Using Artificial Intelligence Models: A Retrospective Pre-Post Cohort Study Using Routinely Collected EHR Data.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Natural disasters, particularly floods, significantly increase infectious disease risk through environmental contamination and healthcare system disruption. Despite well-documented flood-disease associations, predictive models for post-disaster epidemiological surveillance remain limited. We aimed to develop and validate machine learning algorithms to predict infectious disease incidence following flood events. Methods: We conducted a retrospective pre-post cohort study using routinely collected electronic health records from Firuzkuh County health centers, comparing a 30-day pre-flood cohort (July-August 2021; Results: Post-flood infectious disease prevalence increased significantly from 39.5% to 47.3% ( Conclusion: Machine learning models demonstrated moderate predictive performance for post-flood infectious disease occurrence. While results show feasibility for AI-based disaster epidemiology, the modest performance indicates that incorporating additional environmental and socioeconomic variables is essential for developing clinically actionable prediction systems for public health emergency response.
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