A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk

Joanne Kim*, Andrew B. Lawson, Brian Neelon, Jeffrey E. Korte, Jan M. Eberth, Gerardo Chowell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Number of pages1
JournalSTATISTICS IN MEDICINE
Early online date10 Oct 2024
DOIs
Publication statusE-pub ahead of print - 10 Oct 2024

Keywords / Materials (for Non-textual outputs)

  • Bayesian
  • infectious disease
  • prediction
  • public health
  • spatio-temporal model

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