Unsupervised analysis of COVID-19 pandemic evolution in brazilian states

Abstract

Extracting information and discovering patterns from a massive dataset is a hard task. In an epidemic scenario, this data has to be integrated providing organization, agility, transparency and, above all, it has to be free of any type of censorship or bias. The aim of this paper is to analyze how coronavirus contamination has evolved in Brazil applying unsupervised analysis algorithms to extract information and find characteristics between them. To achieve this goal we describe an implementation that uses data about Covid-19 spread in Brazilian states (26 states and the federal district), applying a Time Series Clustering technique based on a K-Means variation, using Dynamic Time Warping as a similarity metric. We used data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants, during 452 days from the first reported death in each state. Two analyzes were performed, one considering 3 clusters and the other with 6 clusters. Through these analysis, 3 patterns of responses to the pandemic can be observed, ranging from one of greater to lesser control of the pandemic, although in recent months all clusters showed a highly increase in the number of deaths. The identification of these patterns is important to highlight possible actions and events, as well as other characteristics that determine the correct or incorrect public decision-making in combating the Covid-19 pandemic.

Performance indicators of Primary Care of the Previne Brasil Program

“…Still in this sense, the Brazilian states responded differently to the COVID-19 epidemic, in relation to the number of deaths. A study shows that the states of Amazonas, Mato Grosso and Rondônia had the highest number of deaths per 100,000 inhabitants, which may be related to the difficulties faced, in this period, in health care processes (31) .…”

Time series clustering of COVID-19 pandemic-related data

“…The Pearson correlation was performed ( Mahmoudi et al, 2020 ) to demonstrate that cumulative confirmed cases and deaths were significantly positively correlated with population size, and they rescale the data based on the US population's size to eliminate the effect. Others ( Cassão et al, 2022 ; Landmesser, 2021 ; Mahmoudi et al, 2020 ; Zarikas et al, 2020 ) used daily new cases per 100,000 or 1 million population as raw data to mitigate the impact of population size. Here to reduce the influence of non-relevant factors and better identify the pattern structure, we standardized the raw data for every country.…”

Preventable COVID-19 cases and deaths by alternative vaccination and non-pharmacological intervention policies in Brazil

“…This work uses daily historical data from each state for the number of cases (infections), deaths (absolute number of deaths), and number of vaccinated individuals available at https://github.com/wcota/covid19br 31 which aggregates data from at least two main sources 2 : the Ministry of Health 1 and Brasil.IO 32 . Previous works use the same data source in their analysis (see, for example, Araújo et al 33 , Badr et al 34 , Cassão et al 35 , Aragão et al 36 , and Almeida et al 37 ).…”