Abstract

Objective

to estimate the transmission rate, the epidemiological peak, and the number of deaths by the new coronavirus.

Method

a mathematical and epidemiological model of susceptible, infected, and recovered cases was applied to the nine Brazilian capitals with the highest number of cases of the infection. The number of cases for the 80 days following the first case was estimated by solving the differential equations. The results were logarithmized and compared with the actual values to observe the model fit. In all scenarios, it was considered that no preventive measures had been taken.

Results

the nine metropolises studied showed an upward curve of confirmed cases of COVID-19. The prediction data point to the peak of the infection between late April and early May. Fortaleza and Manaus had the highest transmission rates (≥2·0 and ≥1·8, respectively). Rio de Janeiro may have the largest number of infected people (692,957) and Florianópolis the smallest (24,750).

Conclusion

the estimates of the transmission rate, epidemiological peak, and number of deaths from coronavirus in Brazilian metropolises presented expressive and important numbers the Brazilian Ministry of Health needs to consider. The results confirm the rapid spread of the virus and its high mortality in the country.

Coronavirus Infections; Social Isolation; Forecasting; Epidemiology; Epidemiologic Models; Nursing

Details

Title
Estimation and prediction of COVID-19 cases in Brazilian metropolises
Author
George Jó Bezerra Sousa  VIAFID ORCID Logo  ; Thiago Santos Garces  VIAFID ORCID Logo  ; Virna Ribeiro Feitosa Cestari  VIAFID ORCID Logo  ; Thereza Maria Magalhães Moreira  VIAFID ORCID Logo  ; Raquel Sampaio Florêncio  VIAFID ORCID Logo  ; Duarte Pereira, Maria Lúcia  VIAFID ORCID Logo 
Section
Original Article
Publication year
2020
Publication date
2020
Publisher
Universidade de São Paulo-USP, Escola de Enfermagem de Ribeirão Preto - USP
ISSN
01041169
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2719274529
Copyright
© 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.