The new PMC design is here! Learn more about navigating our updated article layout. The PMC legacy view will also be available for a limited time. Federal government websites often end in. The site is secure. The outbreak of the new COVID disease is a serious health problem that has affected a large part of the world population, especially older adults and people who suffer from a previous comorbidity.
In this work, we proposed a classifier model that allows for deciding whether or not a patient might suffer from the COVID disease, considering spatio-temporal variables, physical characteristics of the patients and the presence of previous diseases.
We used XGBoost to maximize the likelihood function of the multivariate logistic regression model. The main results revealed that patients without comorbidities are less likely to be COVID positive, unlike people with diabetes, obesity and pneumonia.
In the case of the third and fourth wave, there was an increased risk for young individuals under 20 years , while older adults over 40 years decreased their chances of infection. Estimates of positive COVID cases with both the XGBoost-LR model and the multivariate logistic regression model were used to create maps to visualize the spatial distribution of positive cases across the country.
Spatial analysis was carried out to determine, through the data, the main geographical areas where a greater number of positive cases occurred. Among these, the United States, India, Brazil, the United Kingdom and Russia lead the list of countries with the highest number of infected [ 1 ]. The outbreak of the SARS-CoV-2 virus was declared a pandemic in March , due to its rapid dissemination and its negative effects across various countries [ 2 ].
