At the end of 2019, the latest novel coronavirus Sars-CoV-2 emerged as a significant acute respiratory
\ndisease that has become a global pandemic. Countries like Brazil have had difficulty in dealing
\nwith the virus due to the high socioeconomic difference of states and municipalities. Therefore, this
\nstudy presents a new approach using different machine learning and deep learning algorithms applied
\nto Brazilian COVID-19 data. First, a clustering algorithm is used to identify counties with similar
\nsociodemographic behavior, while Benford\u2019s law is used to check for data manipulation. Based
\non these results we are able to correctly model SARIMA models based on the clusters to predict
\nnew daily cases. The unsupervised machine learning techniques optimized the process of defining
\nthe parameters of the SARIMA model. This framework can also be useful to propose confinement
\nscenarios during the so-called second wave. We have used the 645 counties from S\u00e3o Paulo state, the
\nmost populous state in Brazil. However, this methodology can be used in other states or countries.
\nThis paper demonstrates how different techniques of machine learning, deep learning, data mining
\nand statistics can be used together to produce important results when dealing with pandemic data.
\nAlthough the findings cannot be used exclusively to assess and influence policy decisions, they offer
\nan alternative to the ineffective measures that have been used.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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