Using Machine Learning to support health system planning during the COVID-19 pandemic: a case study using data from São José dos Campos (Brazil)

Abstract

Many efforts were made by the scientific community during the COVID-19 pandemic to understand the disease and better manage health systems’ resources. Believing that city and population characteristics influence how the disease spreads and develops, we used Machine Learning techniques to provide insights to support decision-making in the city of São José dos Campos (SP), Brazil. Using a dataset with information from people who undergo the COVID-19 test in this city, we generated and evaluated predictive models related to severity, need for hospitalization and period of hospitalization. Additionally, we used SHAP (SHapley Additive exPlanations) values for models’ interpretation of the most decisive attributes influencing the predictions. We can conclude that patient age linked to symptoms such as low saturation and respiratory distress and comorbidities such as cardiovascular disease and diabetes are the most important factors to consider when one wants to predict severity and need for hospitalization in this city. We also stress the need of a greater attention to the proper collection of this information from citizens who undergo the COVID-19 diagnosis test.