Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence

Objectives The current form of severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19) caused by a coronavirus (SARS-CoV-2) is a major global health problem. The aim of our study was to use the official epidemiological data and predict the possible outcomes of the COVID-19 pandemic using artificial intelligence (AI)-based RNNs (Recurrent Neural Networks), then compare and validate the predicted and observed data. Materials and Methods We used the publicly available datasets of World Health Organization and Johns Hopkins University to create the training dataset, then have used recurrent neural networks (RNNs) with gated recurring units (Long Short-Term Memory – LSTM units) to create 2 Prediction Models. Information collected in the first t time-steps were aggregated with a fully connected (dense) neural network layer and a consequent regression output layer to determine the next predicted value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and observed data, then recalculated the predictions again. Results The result of our study underscores that the COVID-19 pandemic is probably a propagated source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of the newly diagnosed infections) are to be anticipated. The errors between the predicted and validated data and trends seems to be low. Conclusions The influence of this pandemic is great worldwide, impact our everyday lifes. Especially decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based predictions might be useful tools for predictions and the models can be recalculated according to the new observed data, to get more precise forecast of the pandemic.