TY - JOUR
T1 - Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods
AU - Ospina, Raydonal
AU - Leite, André
AU - Ferraz, Cristiano
AU - Magalhães, André
AU - Leiva, Víctor
N1 - Funding Information:
This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through the grant 305305/2019-0 (RO), and Comissão de Aperfeiçoa-mento de Pessoal do Nível Superior (CAPES), from the Brazilian government; and by FONDECYT, grant number 1200525 (V. Leiva), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation.
Funding Information:
This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through the grant 305305/2019-0 (RO), and Comissão de Aperfeiçoamento de Pessoal do Nível Superior (CAPES), from the Brazilian government; and by FONDECYT, grant number 1200525 (V. Leiva), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation.
Publisher Copyright:
© 2022 The Author(s). Published by MRE Press.
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
KW - Basic and effective reproduction numbers
KW - Data science
KW - Data visualization
KW - Growth model
KW - SARS-CoV-2
KW - Smart analytics
KW - Time-series models
UR - http://www.scopus.com/inward/record.url?scp=85130589722&partnerID=8YFLogxK
U2 - 10.22514/sv.2021.253
DO - 10.22514/sv.2021.253
M3 - Article
AN - SCOPUS:85130589722
VL - 18
SP - 18
EP - 32
JO - Signa Vitae
JF - Signa Vitae
SN - 1334-5605
IS - 3
ER -