A0720
Title: A novel Bayesian spatiotemporal surveillance metric to predict emerging infectious disease high-risk clusters
Authors: Joanne Kim - The Ohio State University (United States) [presenting]
Andrew Lawson - Medical University of South Carolina (United States)
Abstract: Identification of high-risk disease clusters has been one of the top goals of infectious disease public health surveillance. However, previous spatial disease mapping research focused on identifying the current hotspot of the elevated risk area. Still, it did not provide information about where the next high-risk cluster is likely to occur, given the existing hotspot. A novel Bayesian metric is introduced to predict the occurrence of new clusters of the elevated risk areas for the infectious disease outbreak. The proposed metric utilizes the areas' own risk profile, temporal risk trend, and spatial neighborhood influence. A weighting scheme is also introduced to balance these three components, which accommodates the characteristics of the infectious disease outbreak, and spatial disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed cluster prediction surveillance metric. Results indicate that the areas' 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 the accuracy of prediction.
