Research Progress of Spatio-Temporal Interpolation in the Field of Public Health

Abstract

The spatio-temporal interpolation method aims to use the existing spatio-temporal data to estimate missing values and to finely express the spatiotemporal distribution of the research content. The application of the spatiotemporal interpolation method in the field of public health is of great significance to the study of the spatiotemporal distribution and prevention of diseases. This article first introduces the principle of the current main spatiotemporal interpolation methods (spatiotemporal kriging, Bayesian maximum entropy, and regression-based methods) and their applications in the field of public health. Then analyze the advantages and disadvantages of different spatio-temporal interpolation methods. Finally, in view of the shortcomings of the existing spatio-temporal interpolation methods, the future development direction is proposed in order to enrich disease risk prediction methods.