Kernel Density Estimation (KDE) is a non-parametric method used to estimate the probability density function of a random variable. In the context of epidemiology, KDE is particularly valuable for visualizing and analyzing the spatial distribution of disease cases or health-related events. Unlike parametric approaches, KDE does not assume a specific distribution, making it flexible for various types of data.
