Modeling the progression of SARS-CoV-2 infection in patients with COVID-19 risk factors through predictive analysis

Leon-Abarca, J. A.

With almost a third of adults being obese, another third hypertense and almost a tenth affected by diabetes, Latin American countries could see an elevated number of severe COVID-19 outcomes. We used the Open Dataset of Mexican patients with COVID-19 suspicion who had a definite RT-PCR result to develop a statistical model that evaluated the progression of SARS-CoV-2 infection in the population. We included patients of all ages with every risk factor provided by the dataset: asthma, chronic obstructive pulmonary disease, smoking, diabetes, obesity, hypertension, immunodeficiencies, chronic kidney disease, cardiovascular diseases, and pregnancy. The dataset also included an unspecified category for other risk factors that were not specified as a single variable. To avoid excluding potential patients at risk, that category was included in our analysis. Due to the nature of the dataset, the calculation of a standardized comorbidity index was not possible. Therefore, we treated risk factors as a categorical variable with two categories: absence of risk factors and the presence of at least one risk factor in accordance with previous epidemiological reports. Multiple logistic regressions were carried out to associate sex, risk factors, and age as a continuous variable (and the interaction that accounted for increasing diseases with older ages); and SARS-CoV-2 infection as the dependent zero-one binomial variable. Post estimation predictive marginal analysis was performed to generate probability trends along 95% confidence bands. This analysis was repeated several times through the course of the pandemic since the first record provided in their repository (April 12, 2020) to one month after the end of the state of sanitary emergency (the last date analyzed: June 27, 2020). After processing, the last measurement included 464,389 patients. The baseline analysis on April 12 revealed that people 35 years and older with at least one risk factor had a lower risk of SARS-CoV-2 infection in comparison to patients without risk factors (Figure 1). One month before the end of the nationwide state of emergency this age threshold was found at 50 years (May 2, 2020) and it shifted to 65 years on May 30. Two weeks after the end of the public emergency (June 13, 2020) the trends converged at 80 years and one week later (June 27, 2020) every male and female patient with at least one risk factor had a higher risk of SARS-CoV-2 infection compared to people without risk factors. Through the course of the COVID-19 pandemic, all four probability curves shifted upwards as a result of progressive disease spread. In conclusion, we found our model could monitor accurately the probability of SARS-CoV-2 infection in relation to age, sex, and the presence of at least one risk factor. Also, because the model can be applied to any particular political region within Mexico, it could help evaluate the contagion spread in specific vulnerable populations. Further studies are needed to determine the underlying nature of the mechanisms behind such observations.