A Mechanism-Based Analysis of COVID-19 Transmission Using the SEIR Model: A Comparative Study of Wuhan and Italy

Abstract

The COVID-19 pandemic has made it more important to be able to know the underlying mechanisms of the spread of infectious diseases beyond simple predictive modeling. The following study uses the SEIR (Susceptible exposed infectious recovered) model, which is a system of ordinary differential equations, to study the dynamics of an epidemic on a mechanism based approach. Based on a comparative case study of the Wuhan (China) and Italy at the initial phases of the outbreak, the study connects the main model pa rameters with real-world intervention actions and how the variables of the transmission rate, incubation period, and recovery rate affect the development of epidemics. The results indicate that changes in policy time and intervention intensity have a pronounced impact on transmission processes by changing key model parameters, especially the transmission rate and recovery rate. Wuhan had very early and strict measures, which led to a quick decrease in transmission and successful control of the epidemic, and Italy was characterized by delayed action, which increased the peak of the number of infected people and the duration of the outbreak. The findings reveal that the mathematical modeling framework of SEIR can reflect the dynamics of interaction between epidemiological mechanisms and the interventions of the population health. The study adds to the body of literature because it highlights the explanatory value of mathematical models in the study of epidemics and suggests that the linkage between theoretical modeling and the interpretation of policy is missing. The results are relevant to the practical use of enhancing the effectiveness of the interventions in the public health and increasing the preparedness to the occurrence of new epidemic events.