Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence

Laszlo Robert Kolozsvari; Tamas Berczes; Andras Hajdu; Rudolf Gesztelyi; Attila TIba; Imre Varga; Gergo Jozsef Szollosi; Szilvia Harsanyi; Szabolcs Garboczy; Judit Zsuga

The copyright holder for this preprint (which was not peer-reviewed) is . https://doi.org/10.1101/2020.04.17.20069666 doi: medRxiv preprint data set, reflective of the trends in the average time series. It follows that predictions will 223 comply primarily with the Hubei time series, especially in the far future. Therefore Prediction 224 2 shows highest fidelity to the country-specific future scenario if the approach to mitigate the 225 epidemic is similar to that in Hubei. Accordingly, this scenario is also reflective of a country-226 specific future state given the practices of Hubei were followed in said country. On the other 227 hand, Prediction 1 is yielded after the neural network is retrained after any prediction, providing To validate the predictions, we first made the above mentioned two predictions based on data 235 available up to 30/03/2020. The resulting daily new morbidity data are labeled "Old Prediction 236 1" and "Old Prediction 2" on each graph. We then expanded our factual data set with new daily 237 data available until 10/04/2020. These new factual data are labeled "Observed next days" on 238 the graphs. Thus, except for Hungary, we have 11 new daily factual data elements for all 239 countries examined. In the case of Hungary, the data of 10/04/2020 were already available, so 240 in this case 12 new factual data elements are included. Using these data, we validated the two 241 predictions of our model.