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Activity Number: 462 - Novel Spatial and Spatio-Temporal Models in Public Health
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309584
Title: Detecting Disease Clusters Across Space and Time Using Model Averaging
Author(s): Maria Kamenetsky* and Ronald Gangnon
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
Keywords: disease clusters; spatial scan statistic; model averaging

Patterns in the grouping of people and disease across space and time are important to epidemiologists and health professionals, because they may be indicative of underlying elevated disease risk. In some cases, elevated risk may be driven by environmental exposures, infectious diseases or other factors where timely public health intervention could save lives. The spatial and spatio-temporal scan statistic identify a single most likely cluster or equivalently select a single correct model. In this work, we consider the set of all potential clusters across a given study area as an ensemble of (single-cluster) models. We use model averaging using likelihood-based weights to combine predictions from all of the single-cluster models into a sequence of meta-models indexed by the effective number of parameters/space-time clusters. The optimal number of parameters/space-time clusters is identified using information criteria (AIC/BIC). The method is illustrated using data on female breast cancer incidence data at the municipality level in Japan.

Authors who are presenting talks have a * after their name.

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