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Data Driven Disease Surveillance · Journal article
International Journal of Innovative Science and Research Technology · August 29, 2026
A consensus or society position rather than new primary data.
This is a narrative state-of-the-art review that surveys the methodological evolution of spatiotemporal hotspot prediction across classical spatial epidemiology, Bayesian methods, machine learning, deep learning, GeoAI, and foundation models. The review does not report empirical outcome data or comparative effectiveness, but instead synthesizes conceptual advances and identifies persistent challenges in interpretability, data equity, computational demands, and governance.
State-of-the-art narrative review with structured literature search. Spatiotemporal hotspot prediction methods and approaches in public health surveillance and intervention contexts.
Evolution of analytical approaches spans classical spatial epidemiology through Bayesian modeling, machine learning, deep learning, GeoAI, and foundation models Key advancement patterns include increasing predictive capability, re-integration of spatial reasoning, and growing emphasis on operational scalability and multimodal intelligence Persistent challenges identified: interpretability, data equity, computational demands, and governance
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This review provides conceptual scaffolding for practitioners and researchers evaluating methodological options for predictive public health surveillance. It does not offer direct evidence of which approach performs best for any specific disease or outcome, and decisions should be informed by domain-specific comparative studies.
A state-of-the-art narrative review synthesizing the evolution of methodological approaches to spatiotemporal hotspot prediction in public health, offering conceptual framing and future directions rather than empirical evidence of clinical or public health outcomes.
As stated by the source record.
This review provides conceptual scaffolding for practitioners and researchers evaluating methodological options for predictive public health surveillance. It does not offer direct evidence of which approach performs best for any specific disease or outcome, and decisions should be informed by domain-specific comparative studies.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Spatiotemporal hotspot prediction has become essential for proactive public health surveillance and intervention. Over several decades, analytical approaches in this field have evolved from classical spatial epidemiology through Bayesian modeling, machine learning, deep learning, GeoAI, and more recently, foundation models. This State-of-the-Art Review systematically traces the evolution of this paradigm, comparing methodological characteristics, strengths, and limitations across successive approaches. This State-of-the-Art Review synthesizes evidence from peer-reviewed studies identified through a structured literature search. It identifies key patterns of advancement, including increasing predictive capability, the re-integration of spatial reasoning, and a growing emphasis on operational scalability and multimodal intelligence. It also highlights persistent challenges related to interpretability, data equity, computational demands, and governance. The synthesis reveals that the current state of the art is defined by the convergence of GeoAI and foundation models, which together offer new possibilities for context-aware and generalizable public health intelligence. The review concludes by outlining evidence-supported future trajectories and emphasizing that continued progress will depend on addressing both technical and institutional challenges.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.