Confirmed First Year Expansion of COVID-19 Pandemic in Brazil

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Sergio Celaschi


Background: COVID-19 was first reported in Brazil in February 2020 – EPI week # 9. One semester latter, the country became one of the worst affected globally. After twelve months from the first reported case, the number of confirmed cases and deaths crossed 10 million and 248.5 thousand, respectively. Since the start of the epidemic in Brazil, several types of Non-pharmaceutical Interventions - NPI have been adopted with varied success. This work aims to confirm previous forecasts reported on September, 2020 for the expansion of the COVID-19 pandemic in Brazil, the most populated South American country with over 120 million inhabitants.

Methods: The methodology employed in this work was presented in a previous publication. In such methodology, a first series of published data, determines the epidemiological parameters that govern the dynamics of the compartmental model, and adjust the model parameters aiming to forecast the COVID-19 evolutionary outbreak in a longer period. The deterministic and compartmental model provides predictions of the time series of infected individuals and fatalities in the studied population. A SEIR compartmental model was previously selected to estimate outcomes to the dynamics of the COVID-19 epidemic breakout in Brazil. This model takes into account two dominant lineages of the SARS-CoV-2, and a time-varying reproduction number R(t) to estimate the epidemic behavior. Compartments for individuals vaccinated, and newer prevalent SARS-Cov-2 variants were not included. A time-dependent incidence weight on R(t) accounted for Non Pharmaceutical Interventions (NPI).

Results: The cohort study was set as a city population-based analysis. Population-based sample, 3,862,311 during the first study period, was the number of confirmed cases on infected individuals. A previous analysis, restricted to the city of S. Paulo - Brazil, was applied to predict the consequences of holding for posterior NPI releases, and indicates the appearance of a second wave starting last quarter of 2020.  A second series of official data available from September 1st, 2020 to February 28, 2021, covering the whole Brazilian country, confirms the forecasts previously reported for the evolution of infected people and fatalities. 

Conclusion: This work quantifies the importance of the adopted and enforced Non-pharmaceutical Interventions - NPI to control the spread of the COVID-19 pandemic in Brazil, as predicted by a compartmental model. By February 28, 2021, the number of confirmed cases reached 10,551,259 - 5% bellow predicted average of accumulated cases, and fatalities accounted for 254,942 - 4% above accumulated average of estimated deaths. After March 1st, 2021 new peaks on reported numbers of daily new infected and new fatalities appeared and were label as the second wave. They resulted as a combination to the presence of the prevalent SARS-CoV-2 P1 variant, the progressive NPIs realise, and the increased number of vaccinated individuals. Regarding the original SARS-CoV-2 form and its variant, the only model assumption is their distinct incubation rates.

Keywords: COVID-19, Brazil, confirmed forecast, NPI and mitigation policy, second wave, prevalent variants, vaccination

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How to Cite
CELASCHI, Sergio. Confirmed First Year Expansion of COVID-19 Pandemic in Brazil. Medical Research Archives, [S.l.], v. 10, n. 7, july 2022. ISSN 2375-1924. Available at: <>. Date accessed: 25 sep. 2023. doi:
Research Articles


1. Zegarra-Valdivia JA, Chino-Vilca BN, Ames-Guerrero R. " Knowledge, attitudes, and perception susceptibility towards the COVID-19 pandemic in Latin American region" Medical Research Archives [Online]. Volume 10, Issue 4 April 2022.
2. de Souza WM, Buss LF, Candido DS et al. Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil. Nature Hum. Behav. 4, 856–865, 2020.
3. Felix HC, Fontenele J. Instantaneous R calculation for COVID-19 epidemic in Brazil. medRxiv preprint.
4. Qianqian L, et al., The Impact of mutations in SARS-CoV-2 spike on viral infectivity and antigenicit. Cell, July 2020,
5. Darlan SC.Evolution and epidemic spread of SARS-CoV-2 in Brazil. medRxiv preprint
6. Celaschi S. Quantifying effects, forecasting releases, and herd immunity of the Covid-19 epidemic in S. Paulo – Brazil”. medRxiv preprint
7. Celaschi S. Second wave analysis and confirmed forecasts of the SARS-Cov-2 epidemic outbreak in São Paulo, Brazil. SciMedicine Journal. Vol. 3, Special Issue "COVID-19", 2021, DOI: 10.28991/SciMedJ-2021-03-SI-10.
8. Celaschi S. Modeling COVID-19 as a national dynamics with a SARS-CoV-2 prevalent variant: Brazil - a Study Case. MedRxiv.
9. Zlojutro A, Rey D, Gardner L, A decision-support framework to optimize border control for global outbreak mitigation. Nature Sci Rep. 9, 2216 2019.
10. Kissler SM, Tedijanto C, Goldstein E, et al.Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 14 Apr 2020. DOI: 10.1126/science.abb5793.
11. Thompson RN, et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics. 2019. DOI:
12. Fauver, JR, et al. Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell. 181, 990–996.e5 2020.
13. Volz E, Hill V, et al. Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity. medRxiv preprint DOI:
14. Obadia et al. The Ro package: a toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Medical Informatics and Decision Making. Vol. 12: 147. Published online 2012 Dec 18. DOI: 10.1186/1472-6947-12-147.
15. Ali ST, Lin Wang E, et al. Serial interval of SARS-CoV-2 was shortened over time by NPI interventions, Science 28 Aug 2020: Vol. 369, Issue 6507, pp. 1106-1109 DOI: 10.1126/science.abc9004.
16. Kimihito I, et. al. Relative instantaneous reproduction number of Omicron SARS-CoV-2 variant with respect to the Delta variant in Denmark. J. of Medical Virology. Short Comm., Dec. 2021.
17. Cori, A, Ferguson, NM, Fraser C., Cauchemez S. “A new framework and software to estimate time-varying reproduction numbers during epidemics”. Am. J. Epidemiol., 178, 1505–1512, 2013.
18. Thompson RN, et al. See [11], Site visited on May, 30, 2022.
19. Estimating mortality from COVID-19, WHO reference number: WHO-2019-nCoV-Sci_Brief-Mortality-2020.1 Scientific Brief 4 Aug. 2020.
20. Marr V, and Quartin, M. An estimate of the COVID-19 infection fatality rate in Brazil base on a seroprevalence survey. medRxiv preprint. DOI:
21. Grassly N, Fraser C, Mathematical models of infectious disease transmission. Nature Rev Microbiol 6, 477–487, 2008. DOI: 10.1038/nrmicro1845.
22. . Visited on May 25, 2022.
23. Lamarca AP. et al. Genomic surveillance of SARS-CoV-2 tracks early interstate transmission of P.1 lineage and diversification within P.2 clade in Brazil. medRxiv preprint. DOI:
24. Faria NR. Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil. medRxiv preprint. DOI:
25. 2021. Page visited on April 25, 2020.
26. Page visited on April 25, 2020.
27. Zeiser FA, et al., First and second COVID-19 waves in Brazil: A cross-sectional study of patients' characteristics related to hospitalization and in-hospital mortality. Lancet Reg. Health Am. 2022 Feb; 6:100107. DOI: 10.1016/j.lana.2021.100107. Epub 2021 Nov 1. PMID: 34746913; PMCID: PMC8557995.