2024-07-242024-07-242020-06BARROS, Juliana Ramalho; GIOIA, Thamy Barbara; VASQUES, Hérika Silva. Proposta de índice para avaliação de situação de vulnerabilidade social ao COVID-19. Hygeia, Uberlândia, p. 361–369, 2020. Edição Especial: COVID-19. DOI: 10.14393/Hygeia0054537. Disponível em: https://seer.ufu.br/index.php/hygeia/article/view/54537. Acesso em: 17 jul. 2024.e- 1980-1726http://repositorio.bc.ufg.br//handle/ri/25008The health-disease process encompasses factors beyond genetic and biological susceptibility, but also includes variables linked to socialand economic conditions that can lead to health vulnerability. The expanding situation of COVID-19 in Brazil has demonstrated how social inequalities affect this health-disease process; thus, evaluating such disparities can help the country confront thedisease. The objective of this article was to establish an index to assess the situation of social vulnerability to COVID-19. From the 12 selected variables, the modeling identified those the predicted the occurrence of COVID-19 in the State of Goiás andthe Federal District. For this, two machine learning algorithms were tested: Random Forest and XGBoost. The results indicated the most predictive variables were income status, the total hospitalizations for ailments classified as very vulnerable, and the percentage of the population working informally. Therefore, approximately 23% of the municipalities were classified with high to very high vulnerability.porAcesso Abertohttp://creativecommons.org/licenses/by-nc-nd/4.0/Índice de vulnerabilidade socialAlgoritmos de machine learningCOVID-19GoiásSocial vulnerability indexMachine learning algorithmsArtigo10.14393/Hygeia0054537