Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

ABSTRACT: Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE)" that employs hybrid kernel (i.e., weight) functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. M

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC3057986 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature