Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

ABSTRACT: The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC

SUBMITTER: Baqui P 

PROVIDER: S-EPMC8329284 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature