Models for individualized COVID-19 diagnostic prediction

Abstract

Since the COVID-19 pandemic, the world has experienced a large incidence of infections in short periods of time, giving rise to waves of contagion caused by the different variations of SARS-COV-2. Health services, as well as personnel, have been overwhelmed, especially in the poorest countries. Currently and after two years, the pandemic continues and according to experts it is here to stay, which highlights the importance of vaccines and methods of detecting the disease, to curb the number of infections and avoid that the pandemic continues to spread and thus the virus continues to mutate. Detection tests have been scarce and expensive for most of the population, so alternative methods to laboratory ones could be a decisive factor so that people can self-isolate before continuing to infect more people. One of the most effective methods have been statistical predictions of the diagnosis of COVID-19 in a patient, based on certain variables. In this article, it was identified that the most common prediction models were developed from logistic regression and machine-learning, which have shown high percentages of predicting test results for COVID-19. The most important predictor variables in the different models developed in various regions of the world were identified and the opportunities, limitations and perspectives of this prediction method are discussed.