A time-series ecological study protocol to analyze trends of incidence, mortality, lethality of COVID-19 in Brazil
Keywords:COVID-19, protocol, time-series, epidemiology, indicators
Introduction: Since the first case of COVID-19 was confirmed in February 2020, Brazil has reported more than 20 million cases and more than 600,000 deaths on October 31, 2021. The behavior of the pandemic was also different in the various regions of the country, from those with less economic development to those with greater economic development, such as the state of São Paulo.
Objective: to describe step-by-step time series for analyzing trends in mortality, lethality and incidence of COVID-19 in Brazil.
Methods: a protocol for an ecological study of time series, covering the 26 states and the federal district (Brasilia).
Results: The descriptions have the potential to provide information for the government and society in decision-making, about knowledge and conduct, clinical, epidemiological and research investments in health care for the Brazilian people. It is focused on fully understanding the spread of SARS-COV-2 infection in the Brazilian territory, and developing a database for public and universal access for comparative studies between countries and continents.
Conclusion: database built from ecological studies are essential for a full understanding of the virus behavior, its transmissibility, lethality and mortality, and a repository for data that’s been collected and integrated from multiple sources. It is a relevant tool for the search of information and decision-making in global health.
Fischhoff B. Making decisions in a covid-19 world. JAMA. 14 de julho de 2020; 324(2): 139–40.
Coronavírus Brasil [Internet]. [citado 01 de agosto de 2021]. Disponível em: https://covid.saude.gov.br/
Áreas territoriais | IBGE [Internet]. [citado 26 de agosto de 2021]. Disponível em: https://www.ibge.gov.br/geociencias/organizacao-do-territorio/estrutura-territorial/15761-areas-dos-municipios.html?=&t=o-que-e
Coelho FC, Lana RM, Cruz OG, Villela DAM, Bastos LS, Piontti AP y, et al. Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability. PLOS ONE. 18 de setembro de 2020; 15(9): e0238214.
University of Miami. COVID-19 observatory. (2021) ‘Public polity adoption index in Brazil’, available: http://observcovid.miami.edu/brazil/. [citado em 22 Jun. 2021].
Bernal HM, Siqueira CE, Adami F, Santos EFS. Trends in case-fatality rates of COVID-19 in the world, between december, 2019 and august, 2020.J Hum Growth Dev. 2020; 30(3): 344-354. DOI: http:doi.org/10.7322/jhgd.v30.11063
WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020 [Internet]. [citado 26 de outubro de 2021]. Disponível em: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-11-march-2020
Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology. 1o de novembro de 2013; 178(9): 1505–12.
Thompson RN, Stockwin JE, van Gaalen RD, Polonsky JA, Kamvar ZN, Demarsh PA, et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics. dezembro de 2019; 29: 100356.
Prete CA Jr, Buss L, Dighe A, Porto VB, da Silva Candido D, Ghilardi F, et al. Serial interval distribution of SARS-CoV-2 infection in Brazil. Journal of Travel Medicine [Internet]. 1º de março de 2021 [citado 26 de agosto de 2021]; 28(2). Disponível em: https://doi.org/10.1093/jtm/taaa115
Ali ST, Yeung A, Shan S, Wang L, Gao H, Du Z, et al. Serial intervals and case isolation delays for coronavirus disease 2019: a systematic review and meta-analysis. Clinical Infectious Diseases [Internet]. 25 de maio de 2021 [citado 26 de agosto de 2021]; (ciab491). Disponível em: https://doi.org/10.1093/cid/ciab491
DATASUS – Ministério da Saúde [Internet]. [citado 20 de junho de 2021]. Disponível em: https://datasus.saude.gov.br/
Antunes JLF, Cardoso MRA. Uso da análise de séries temporais em estudos epidemiológicos. Epidemiol Serv Saúde. setembro de 2015; 24(3): 565–76.
Editors TPM. Pandemic responses: Planning to neutralize SARS-CoV-2 and prepare for future outbreaks. PLOS Medicine. 28 de abril de 2020; 17(4): e1003123.
Ghayda RA, Lee KH, Han YJ, Ryu S, Hong SH, Yoon S, et al. Estimation of global case fatality rate of coronavirus disease 2019 (COVID-19) using meta-analyses: Comparison between calendar date and days since the outbreak of the first confirmed case. Int J Infect Dis. novembro de 2020; 100: 302–8.
Copyright (c) 2021 Abreu LC, Elmusharaf K, Siqueira CEG
This work is licensed under a Creative Commons Attribution 4.0 International License.