A COMPUTATIONAL MODEL OF EPIDEMICS USING SEIRX MODEL

Abstract

Epidemiology studies the spread and impact of infectious diseases within defined populations, focusing on factors such as transmission rate, infectious agents, infectious periods, and susceptibility. Computational epidemiology simulates these factors using basic compartmental models like Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected (SEI), and Susceptible-Exposed-Infected-Recovered (SEIR). However, these models inadequately address mortality and fatality rates. To enhance the accuracy of epidemic transmission models, we propose an expanded SEIR model by introducing a new compartment, denoted as X, representing the deceased population. This new model, Susceptible-Exposed-Infected-Recovered-Deceased (SEIRX), incorporates fatality and mortality rates, providing a more comprehensive understanding of epidemic dynamics.  The SEIRX model demonstrates superior accuracy in inferring and forecasting epidemic transmission compared to existing models, offering a complete and detailed approach to studying infectious disease outbreaks.