Journal article
A Novel Bayesian Spatio‐Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk
Statistics in medicine, v 43(28), pp 5300-5315
10 Oct 2024
PMID: 39385731
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
ABSTRACT Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio‐temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID‐19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data.
Metrics
Details
- Title
- A Novel Bayesian Spatio‐Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk
- Creators
- Joanne Kim - The Ohio State UniversityAndrew B. Lawson - Medical University of South CarolinaBrian Neelon - Medical University of South CarolinaJeffrey E. Korte - Medical University of South CarolinaJan M. Eberth - Drexel UniversityGerardo Chowell - Georgia State University
- Publication Details
- Statistics in medicine, v 43(28), pp 5300-5315
- Publisher
- Wiley
- Number of pages
- 16
- Grant note
- National Center on Minority Health and Health Disparities: R01-CA237318, R21-MD016947
This work was supported by the National Institutes of Health (grant number R01-CA237318 to A.L. and J.K., R21-MD016947 to B.N.) The funding body had no role in the study design, data analysis, and interpretation, or writing the manuscript.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Health Management and Policy
- Web of Science ID
- WOS:001329530300001
- Scopus ID
- 2-s2.0-85205838682
- Other Identifier
- 991021929331904721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability