The Spatial Epidemiology models on Covid-19 cases in India Mathematical models and data used in Spatial Epidemiology

Abstract

Geographical epidemiology has been description of geographical patterns of mortality rates as part of descriptive epidemiological investigations, with the goal of developing theories regarding disease causation. Disease mapping, disease clustering, and ecological analysis are the predominant methods of geographical epidemiology, having close relationships between them. For describing the transmission of an illness within a geographically dispersed population, many models incorporating frameworks based on individuals, networks, stochastic processes, as well as partial derivative equations have been made. However, these models need a large amount of information and even a large amount of computational performance. Keeping this in mind, we have tried to create deterministic models formulated as partial differential equations to model spatial epidemics in spatial domains. This has been by assuming two types of population, the susceptible population, and the infective population, considering the functions of space and time. COVID-19 is a global tragedy, with India likely to be among the most hit. The fluctuation in the dispersion of COVID-19-related well-being results is most likely connected with numerous basic factors, like segment, financial, or natural poisons related factors.