An Extended SI-Based SEIQRD Dynamical Model of Infectious Diseases

Abstract

Infectious disease models are a core tool in mathematical epidemiology, designed to describe, through mathematical equations, the patterns of pathogen transmission within host populations. This paper systematically outlines the development of infectious disease models over the years. After exploring several major factors influencing disease transmission, we introduce new variables into the model and establish the SEIQRD dynamical model based on the SI framework. Using this SEIQRD model, we analyze the transmission of COVID-19 in Wuhan (2022) and New York City (2023). Through the study of the epidemic spread in these two cities, we find that, due to the complexity of the pandemic, the SEIQRD model aligns more closely with actual observations and provides a more accurate description of COVID-19 compared to the earlier SIR model. Furthermore, the experience of managing COVID-19 reveals that, with the continuous advancement of data science, infectious disease modeling is shifting from a macroscopic description of infected populations toward precise predictions of different infection levels and individual infected cases.