Towards a predictive model for prevention of the risk of COVID-19 infection.

Toudert, Djamel

INTRODUCTION The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. OBJECTIVE To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. METHODS Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. RESULTS A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. CONCLUSION The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.