Dataset Information

Estimation and prediction of COVID-19 cases in Brazilian metropolises.

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.

SUBMITTER: Sousa GJB

PROVIDER: S-EPMC7319758 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Estimation and prediction of COVID-19 cases in Brazilian metropolises.

Sousa George Jó Bezerra GJB Garces Thiago Santos TS Cestari Virna Ribeiro Feitosa VRF Moreira Thereza Maria Magalhães TMM Florêncio Raquel Sampaio RS Pereira Maria Lúcia Duarte MLD

Revista latino-americana de enfermagem 20200626

<h4>Objective</h4>to estimate the transmission rate, the epidemiological peak, and the number of deaths by the new coronavirus.<h4>Method</h4>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 val ...[more]

PMID: 32609282

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Project description:BackgroundThe COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.MethodsWe developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.ResultsThe model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.ConclusionsWe successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

Dataset Information

Estimation and prediction of COVID-19 cases in Brazilian metropolises.

Publications

Estimation and prediction of COVID-19 cases in Brazilian metropolises.

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