Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence

TL;DR

This paper introduces a spatial variable selection method using adaptive elastic net penalties to identify environmental and economic factors associated with Lyme disease emergence in Virginia, accounting for spatial correlations.

Contribution

The paper develops a novel spatial variable selection procedure with adaptive elastic net, improving covariate identification in spatial disease models.

Findings

Identified key environmental and economic variables linked to Lyme disease in Virginia.

Demonstrated the effectiveness of the method through simulation studies.

Applied the method to real data, uncovering new important factors.

Abstract

Lyme disease is an infectious disease that is caused by a bacterium called Borrelia burgdorferi sensu stricto. In the United States, Lyme disease is one of the most common infectious diseases. The major endemic areas of the disease are New England, Mid-Atlantic, East-North Central, South Atlantic, and West North-Central. Virginia is on the front-line of the disease's diffusion from the northeast to the south. One of the research objectives for the infectious disease community is to identify environmental and economic variables that are associated with the emergence of Lyme disease. In this paper, we use a spatial Poisson regression model to link the spatial disease counts and environmental and economic variables, and develop a spatial variable selection procedure to effectively identify important factors by using an adaptive elastic net penalty. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations of disease counts. The performance of the proposed method is studied and compared with existing methods via a comprehensive simulation study. We apply the developed variable selection methods to the Virginia Lyme disease data and identify important variables that are new to the literature. Supplementary materials for this paper are available online.