Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data

Cosimo Distante; Igor Gadelha Pereira; Luiz Marcos Garcia Goncalves; Prisco Piscitelli; Alessandro Miani

The correct prediction of new daily cases at this time of Italian COVID-19 outbreak requires the correct estimation of the peak including the unknown remaining part of the epidemiological curve, where this later can be predicted ahead by using a deep convolutional auto-encoder. Therefore, we applied a Modified Auto-Encoder (MAE) for a time-series forecast in order to predict the evolution of daily cases for each of the 21 regions of Italy. [15] The model was trained with the data from the Chinese regions, which provides complete data in the sense that they already went through the peak number of daily cases and managed to suppress the epidemic by social distancing measures. Although such measures implemented by the Chinese government may be impossible to implement in other countries or may not be as effective as it was in China, the data generated by their experience going through the epidemic can be used to derive data-oriented models to predict the epidemic dynamic behavior in other countries. The forecast of new daily cases has been used to correctly estimate the single peaks but also to obtain better spreading predictions from SEIR model. We modeled spreading of Covid-19 using Chinese data and used the model