Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods
1Department of Statistics, CASTLab, Universidade Federal de Pernambuco, 51280-000 Recife, Brazil
2Department of Economics, Universidade Federal de Pernambuco, 51280-000 Recife, Brazil
3School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, 2340000 Valparaíso, Chile
DOI: 10.22514/sv.2021.253 Vol.18,Issue 3,May 2022 pp.18-32
Submitted: 18 August 2021 Accepted: 03 November 2021
Published: 08 May 2022
*Corresponding Author(s): Víctor Leiva E-mail: firstname.lastname@example.org; email@example.com
The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.
Basic and effective reproduction numbers; Data science; Data visualization; Growth model; SARS-CoV-2; Smart analytics; Time-series models
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