Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model

Abstract

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNab; which contribute to the cytokine storm and fever, parameters of inflammation d-dimer and ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level and to develop strategies for allocating appropriate resources to mitigate severe cases at a population level.

Nutritional management and clinical outcome of critically ill patients with COVID-19: A retrospective study in a tertiary hospital

“…(2021) mention that the d-dimer levels presented one of the highest values during the first week of hospitalization in the intensive care unit (ICU). Evidence suggests that critically ill COVID-19 patients who have high viral loads may also have indicators of coagulation activation (D-dimer) and elevated pro-inflammatory cytokines [ 3 , 4 ]. Thus, the viral load associated with d-dimer levels can be another item used to identify the severity of these individuals [ 3 , 4 ].…”