Computational Modeling of Viral Infection and Immune Response in COVID-19 Patients

In December 2019, a novel coronavirus known as SARS-CoV-2 emerged in China and rapidly spread worldwide, resulting in a global pandemic with a mortality rate ranging from 3% to 5%. Mathematical models have proven to be valuable tools for gaining insights into the transmission patterns of the virus and identifying potential targets for drug development. However, the intricate dynamics of the immune response leading to lung damage and fatal outcomes are still not fully comprehended. In this study, we present a reaction-diffusion mathematical model that captures the progression of COVID-19 from the initial seeding of the virus in the human respiratory tract, while taking into account the coupled dynamic of healthy cells, infected cells, SARS-CoV-2 molecules, and the innate immune system. The numerical simulations have been performed using a list of values simulating the baseline scenario for viral infection at a cellular scale. The numerical results align well with the experimental findings reported in existing literature.