Generated Prediction Data of COVID-19's Daily Infections in Brazil

Dataset general description: • This dataset reports 4200 recurrent neural network models, their settings, and their relevant generated files (including prediction csv files, graphs, and metadata files, as applicable), for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 and 40 time-steps). The used code is developed by the author and located in the following online data repository link: http://dx.doi.org/10.17632/yp4d95pk7n.3 Dataset content: • Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1197 generated models' files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated models' files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated models' files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated models' files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 349 models (out of a 358 sample but 9 models didn't achieve the accuracy threshold), which are a sample of 1001 models. Also, all data of the control group - India (1 model). 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11. 5. The evaluation of performance for 10, 20, 30, 40, and 50 time-steps alternatives (5 models). • Settings and metadata for the above 3 categories: 1. Used settings during the training session in json files. 2. Metadata: training / prediction setup and accuracy in csv files. Raw data source used to train the models: • The used raw data [1] for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University) : https://github.com/CSSEGISandData/COVID-19 (accessed 2020-07-20) • The following raw data links were used (both accessed 2020-07-08): 1. till 2020-06-29: https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. till 2020-06-13: https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv References: 1- Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1