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. 2020 Jun:135:109853.
doi: 10.1016/j.chaos.2020.109853. Epub 2020 May 1.

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Matheus Henrique Dal Molin Ribeiro  1   2 , Ramon Gomes da Silva  1 , Viviana Cocco Mariani  3   4 , Leandro Dos Santos Coelho  1   4
Affiliations

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Matheus Henrique Dal Molin Ribeiro et al. Chaos Solitons Fractals. 2020 Jun.

Abstract

The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.

Keywords: ARIMA; COVID-19; Decision-making; Forecasting; Machine learning; Time-series.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Heatmap of the cumulative confirmed cases of the analyzed states.
Fig. 2
Fig. 2
Proposed forecasting framework.
Fig. 3
Fig. 3
Predicted versus observed cumulative confirmed cases of COVID-19 for AM, BA, CE, and MG states.
Fig. 4
Fig. 4
Predicted versus observed cumulative confirmed cases of COVID-19 for PR, RJ, RN, RS, SC, and SP states.
Fig. 5
Fig. 5
Box-plot for absolute error according to model and state for COVID-19 forecasting up to SDA.
See this image and copyright information in PMC

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