In silico dynamics of COVID-19 phenotypes for optimizing clinical management
Chrysovalantis Voutouri, Mohammad R. Nikmaneshi, C. Corey Hardin, Ankit Patel, Ashish Verma, Melin J. Khandekar, Sayon Dutta, Triantafyllos Stylianopoulos, Lance L. Munn, Rakesh K. Jain
Abstract
Significance A distinctive feature of COVID-19 is its extreme heterogeneity—illness ranges from minimally symptomatic to life threatening. Heterogeneity results from a poorly understood combination of patient factors, viral dynamics, antiviral and immune modulating therapies, and dynamics of the innate and adaptive immune responses. In order to better understand clinical heterogeneity and optimal treatment, we developed a comprehensive mathematical model incorporating elements of the innate and adaptive immune responses, the renin−angiotensin system (which the virus exploits for cellular entry), rates of viral replication, inflammatory cytokines, and the coagulation cascade. Our model reveals divergent treatment responses and clinical outcomes as a function of comorbidities, age, and details of the innate and adaptive immune responses which can provide a framework for understanding individual patients’ trajectories.