A machine learning explanation of the pathogen-immune relationship of SARS-CoV-2 (COVID-19), model to predict immunity, and therapeutic opportunity
Authors: Luellen Eric 
Published: July 2020

Structured AbstractImportanceThe clinical impacts of this study are it: (1) identified three immunological factors that differentiate asymptomatic, or resistant, COVID-19 patients; (2) identified the levels of those factors that can be used by clinicians to predict who is likely to be asymptomatic or symptomatic; (3) identified a novel COVID-19 therapeutic for further testing; and, (4) ordinally ranked 34 common immunological factors by their importance in predicting disease severity.ObjectivesThe primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary objective was to learn about the relevance of other immune factors.DesignThis was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly-grouped Chinese COVID-19-positive patients, 37 of whom were symptomatic and 37 asymptomatic.SettingA single-center primary-care hospital in the Wanzhou District of China.ParticipantsImmunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before the recordation of the observations. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years), 22 were female, 15 were male. For comparison, 37 RT-PCR test-positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex.Main OutcomeThe primary study outcome was that asymptomatic COVID-19 patients could be identified by three distinct immunological factors and level: stem-cell growth factor-beta (SCGF-β) (> 127637), interleukin-16 (IL-16) (> 45), and macrophage colony-stimulating factor (M-CSF) (> 57). The secondary study outcome was the novel suggestion that stem-cell therapy with SCGF-β may be a new valuable therapeutic for COVID-19.ResultsWhen SCGF-β was included in the machine-learning analysis, a decision-tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptoms immunity with 100% accuracy. When SCGF-β was excluded, a random-forest algorithm classified and predicted COVID-19 asymptomatic and symptomatic cases with 94.8% area under the ROC curve accuracy (95% CI 90.17% to 100%). Thirty-four (34) common immune factors have statistically significant (P-value < .05) associations with COVID-19 symptoms and 19 immune factors appear to have no statistically significant association.ConclusionPeople with an SCGF-β level > 127637, or an IL-16 level > 45 and M-CSF level > 57, appear to be predictively immune to COVID-19, 100% and 94.8% (ROC AUC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point-of-care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-β and/or M-CSF appear to be promising novel therapeutics for COVID-19.