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

https://doi.org/10.1016/j.chaos.2020.109853Get rights and content

Highlights

  • Regression models are employed to forecasting COVID-19 cases in the Brazilian context.

  • Data from ten states with a high incidence of COVID-19 are adopted.

  • Models for multi-step-ahead forecasting are evaluated.

  • Out-of-sample forecasting errors lower than 6.9% are achieved by best models.

  • SVR and stacking ensemble are the most suitable tools to forecasting COVID-19 cases in the evaluated scenarios.

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
Forecasting
Decision-making
Machine learning
Time-series

Cited by (0)

View Abstract