The datasets are freely available to copy, use, and redistribute for non-commercial purposes only, provided that the authors are appropriately credited.

Download full dataset for COVID-19 in African countries (.csv):

Download full dataset for COVID-19 in Canadian provinces (.csv):

In this project, a multivariate deep-learning based Early Warning System (EWS) is implemented for respiratory infection outbreaks. Data is gathered from multiple online sources including Google trends, Google news, Wiki trends, Reddit, Google Earth Enginge (GEE), Open-Meteo, and underground weather. The datasets are combined and used for training, validating, and testing the models. The models are composed of four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a stacked linear Neural Network (NN). The CNN layer combines the data sources into one vector. The GNN and GRU layers analyze the data on spatial and temporal deminsions, respectively. Finally, the stacked NN layer provides a T step-ahead prediction. The machine learning pipeline is automated to collect the datasets, train and test the models, forecast the coming waves, and visualize the results in this dashboard. The models are capable of forecasting the outbreaks with an outstanding accuracy by up to 56 day(s) (8 week(s)) in advance.

For detailed information on the datasets and methodology, please refer to our manuscript.

Number of COVID-19 Cases