Epidemiological data often suffer from gaps due to various reasons such as limited testing, reporting delays, or logistical constraints. Missing data can hinder the ability to understand disease dynamics, identify risk factors, and implement effective intervention strategies. Interpolation helps fill these gaps, allowing for a more continuous and coherent representation of data, which is crucial for [disease modeling](#), [trend analysis](#), and [spatial analysis](#).
