Redesigning COVID 19 care with network medicine and machine learning: A review

Abstract

Emerging evidence regarding COVID 19 highlights the role of individual resistance and immune function in both susceptibility to infection as well as severity of disease. Multiple factors influence the response of the human host when exposed to viral pathogens. Influencing an individual’s susceptibility to infection include such factors as nutritional status, physical and psychosocial stressors, obesity, protein calorie malnutrition, emotional resilience, single nucleotide polymorphisms (SNPs), environmental toxins—including air pollution and first- and second-hand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, availability of nutrient dense food and empty calories. This review examines the network of interacting co-factors that influence the host-pathogen relationship, which in turn determine one’s susceptibility to viral infections like COVID 19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients’ risk of developing active infection and devise a comprehensive approach to prevention and treatment.