Abstract
Asymptomatic people infected during the SARS-CoV-2 (COVID-19) pandemic have outnumbered symptomatic people by an approximate ratio of 4:1 with little understanding to date as to why; therefore, they have been impossible to identify in advance. 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 34 of 53 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-β (> 127637), IL-16 (> 45), and M-CSF (> 57). The first potentially important implication of these findings is they suggest that SCGF-β could be a viable biomarker for prognoses, screenings, and triaging people exposed to SARS-CoV-2, which could be a valuable tool at the point-of-care for managing and preventing outbreaks. It may be able to predict who will get sick or not, and who has a probability of living or dying. A second potentially important implication is the results suggest SCGF-β may be a viable therapeutic for SARS-Cov-2.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
No external funding was received for this study.
Author Declarations
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Footnotes
This version includes the statistical significance of immunological factors at P-values of .01 in addition to .05. It updates the number of factors that met this threshold level. The second version also revised the introduction to review what is known about asymptomatic COVID-19 patients and clarifies the text in response to peer feedback. However, the crux of the findings remains unchanged.
Data Availability
The data is in the public domain.