A machine learning explanation of the pathogen-immune relationship of SARS-CoV-2 (COVID-19), model to predict immunity, and therapeutic opportunity

Abstract

Asymptomatic people infected during the SARS-CoV-2 pandemic have outnumbered symptomatic people by an approximate ratio of 4:1 with little understanding to date as to why; therefore, they have been difficult to identify. Moreover, studies indicate that most asymptomatic virus-positive patients are infectious, thereby creating a new public health danger via a plethora of "silent spreaders." This data science study identified four novel discoveries that may significantly impact our understanding of the pathogen-immune relationship: (1) Spearman rho correlation coefficients and associated P-values identified 33 of 55 common immune factors have statistically significant associations with SARS-CoV-2 morbidity, their direction (+/-) and strength to inform research and therapies; (2) five machine learning algorithms were applied to 74 observations of these 33 immunological variables and identified three models of prognostic biomarkers that can classify and predict who will be asymptomatic or symptomatic if infected with 94.8% to 100% accuracy; (3) a random forest of 200 decision trees ordinally ranked the 33 statistically significant independent predictor variables by their relative importance in predicting SARS-CoV-2 symptoms; and, (4) three different decision-tree algorithms separately identified and validated three immunological biomarkers and levels that nearly always differentiate asymptomatic patients: SCGF-Beta; (> 127637), IL-16 (> 45), and M-CSF (> 57). The implications of these findings are they indicate a tool that can identify in advance the 20% of people who are at higher risk of morbidity from infection and suggests a specific stem-cell factor for therapeutics.