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Issues in Bayesian prospective surveillance of spatial health data
1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29466, USA.
Spatial and Spatio-Temporal Epidemiology
|June 12, 2022
Summary
This paper reviews challenges in prospective surveillance of geo-referenced health data. It highlights Bayesian Hierarchical Modeling (BHM) as a valuable tool for early warning of adverse risk scenarios using surveillance functionals.
Area of Science:
- Epidemiology
- Biostatistics
- Geographic Information Systems (GIS)
Background:
- Geo-referenced health data presents unique challenges for prospective surveillance.
- Traditional surveillance methods may not adequately address the spatial and temporal complexities of health data.
- Model-based approaches are increasingly important for effective health surveillance.
Purpose of the Study:
- To review major issues in prospective surveillance of geo-referenced health data.
- To propose and discuss model-based approaches, particularly the Bayesian paradigm.
- To emphasize the utility of Bayesian Hierarchical Modeling (BHM) for early warning systems.
Main Methods:
- Review of existing literature on geo-referenced health data surveillance.
- Focus on model-based approaches, specifically Bayesian modeling.
- Discussion of posterior functional measures like SCPO and SKL and their extensions.
Main Results:
- Identified key challenges in prospective surveillance of spatial health data.
- Demonstrated the suitability of the Bayesian paradigm for modeling such data.
- Highlighted the effectiveness of surveillance functionals within BHM for risk assessment.
Conclusions:
- Bayesian Hierarchical Modeling (BHM) offers a robust framework for prospective health surveillance.
- Surveillance functionals are crucial for early detection of adverse risk scenarios.
- The proposed model-based approach enhances the early warning capabilities for public health.
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