Generated time-series prediction data of COVID-19's daily infections in Brazil by using recurrent neural networks.

ABSTRACT: 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

SUBMITTER: Hawas M 

PROVIDER: S-EPMC7437445 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature