Please use this identifier to cite or link to this item: http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7558
Machine learning approaches for localized lockdown during COVID-19: a case study analysis
Sara Malvar
Julio R Meneghini
Acceso Abierto
Atribución-NoComercial-CompartirIgual
arXiv:2201.00715v1
https://arxiv.org/abs/2201.00715
At the end of 2019, the latest novel coronavirus Sars-CoV-2 emerged as a significant acute respiratory disease that has become a global pandemic. Countries like Brazil have had difficulty in dealing with the virus due to the high socioeconomic difference of states and municipalities. Therefore, this study presents a new approach using different machine learning and deep learning algorithms applied to Brazilian COVID-19 data. First, a clustering algorithm is used to identify counties with similar sociodemographic behavior, while Benford's law is used to check for data manipulation. Based on these results we are able to correctly model SARIMA models based on the clusters to predict new daily cases. The unsupervised machine learning techniques optimized the process of defining the parameters of the SARIMA model. This framework can also be useful to propose confinement scenarios during the so-called second wave. We have used the 645 counties from São Paulo state, the most populous state in Brazil. However, this methodology can be used in other states or countries. This paper demonstrates how different techniques of machine learning, deep learning, data mining and statistics can be used together to produce important results when dealing with pandemic data. Although the findings cannot be used exclusively to assess and influence policy decisions, they offer an alternative to the ineffective measures that have been used.
Cornell University
03-01-2022
Preimpreso
arxiv
Inglés
Epidemia COVID-19
Público en general
OTRAS
Versión publicada
publishedVersion - Versión publicada
Appears in Collections:Artículos científicos

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