Generated Time-series Prediction Data of COVID-19′s Daily Infections in Brazil by Using Recurrent Neural Networks

Hawas, Mohamed

In light of the COVID-19 pandemic that has struck the world since the end of 2019, many endeavors have been carried out to overcome this crisis. Taking into consideration the uncertainty as a feature of forecasting, this data article introduces long-term time-series predictions for the virus's daily infections in Brazil by training forecasting models on limited raw data (30 time-steps and 40 time-steps alternatives). The primary reuse potential of this forecasting data is to enable decision-makers to develop action plans against the pandemic, and to help researchers working in infection prevention and control to: 1) explore limited data usage in predicting infections. 2) develop a reinforcement learning model on top of this data-lake, which can perform an online game between the trained models to generate a new capable model for predicting future true data. The prediction data was generated by training 4200 recurrent neural networks (54 to 84 days validation periods) on raw data from Johns Hopkins University's online repository, to pave the way for generating reliable extended long-term predictions.