Covid-19 vaccination priorities defined on machine learning

Bibliographic Data

ID16780475
AuthorsRonaldo Costa Couto (0000-0003-4907-6295, Faculdade de Ciências Médicas de Minas Gerais, corresponding author), Tânia Moreira Grillo Pedrosa (0000-0002-0042-8125, Faculdade de Ciências Médicas de Minas Gerais), Luciana Moreira Seara (0000-0001-9822-6056), Carolina Seara Couto (0000-0002-0373-3248, Hospital do Servidor Público Estadual), Vitor Seara Couto (0000-0001-5218-2158), Karla Giacomin (0000-0002-9510-6953, Centro de Direito Internacional), Ana Claudia Couto de Abreu (0000-0002-9929-7435)
Year2022
Volume56
Pages11
Publication date2022-03-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRevista de Saúde Pública (JOURNAL)
Journal identifiersISSN: 0034-8910 • E-ISSN: 0034-8910
PublisherUniversidade de São Paulo. Agência de Bibliotecas e Coleções Digitais (PUBLISHER)
DOI10.11606/s1518-8787.2022056004045
PMID35319671
OpenAlexW4221006489
LanguageEN
Citations received1
References cited8

OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers

2019-20 coronavirus outbreak · Betacoronavirus · Coronavirus disease 2019 (COVID-19) · Coronavirus Infections · Infectious disease (medical specialty) · MEDLINE · Outbreak · Pandemic · Pathology · Political science · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Vaccination · Computer Science · Immune responses and vaccinations · Medicine · SARS-CoV-2 and COVID-19 Research · Sepsis Diagnosis and Treatment · Virology

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