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 (ie, weight) functions …

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 (ie, weight) functions …