Forecasting COVID-19 pandemic using an echo state neural network-based framework

Forecasts can help in the decision-making process. Epidemiological forecasts are no different, they can help to evaluate the scenario and possible direction of disease spread, for guiding possible interventions. In this work, Echo State Networks (ESNs) are evaluated for COVID-19 (Coronavirus Disease 2019) cases and deaths forecasting ten days ahead. The chosen locations for the experiment are five states in Brazil, namely Sao Paulo (SP), Bahia (BA), Minas Gerais (MG), Rio de Janeiro (RJ), and Ceara (CE), the states with the most COVID-19 cases as of December 31, 2020. The results are evaluated using performance indexes RMSE (Root-mean-square error), MAE (Mean absolute error), and MAPE (Mean absolute percentage error). Results are compared with a common forecasting technique called ARIMA (Autoregressive Integrated Moving Average). The error signals are compared using Wilcoxon Signed-Rank Test, to evaluate the difference statistically. ESNs presented overall good results for a ten day horizon forecast regarding used performance metrics, but for the number of cases, ARIMA outperformed ESNs regarding RMSE, MAE, and MAPE in all but one state. For the number of deaths however, ESNs outperformed ARIMA in most states when the MAE is taken into account. ESNs are shown to be a solid forecasting model when compared with ARIMA, presenting comparable results and in some cases outperforming it.

Ensemble Learning Models Coupled with Urban Mobility Information Applied to Predict COVID-19 Incidence Cases

The coronavirus disease (COVID-19), according to the World Health Organization, by July 15th, 2021, has infected more than 188 million people, and more than 4 millions have died from it in the worldwide. It is important to forecast the incidence of cases in a short-term horizon to help the public health system develop strategic planning to deal with the COVID-19. In this chapter, several artificial intelligence (AI) models including extreme gradient boosting, extreme learning machine, long short-term memory, and support vector regression are used stand-alone, and coupled with the ensemble empirical mode decomposition (EEMD) employed to decompose the time-series into intrinsic mode functions and residual signals. All AI techniques are evaluated in the task of forecasting daily incidence COVID-19 cases in ten Brazilian states, with a high number of cases by September 4th, 2020, with seven and fourteen-days-ahead. Previous COVID-19 incidence cases and urban mobility information were employed as systems input for all forecasting models. The models’ effectiveness are evaluated based on the performance criteria. In general, the EEMD approach outperformed the compared models regarding the accuracy in 65% of the cases. Regarding the exogenous variables, urban mobility information indeed plays a key role in the forecasting task. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to fourteen-days-ahead, the adopted models can be recommended as promising for forecasting and can be used to assist in development of public policies to mitigate the effects of COVID-19 outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.