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

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

Publication date: Oct 10, 2024

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.

Concepts Keywords
30weeks Bayesian
Carolina infectious disease
Covid prediction
Epidemiology public health
Infectious spatio‐temporal model

Semantics

Type Source Name
disease MESH Emerging Infectious Disease
disease MESH infectious disease
pathway REACTOME Infectious disease
disease IDO intervention
drug DRUGBANK Isoxaflutole
disease IDO role
disease MESH COVID-19

Original Article

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