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

represented on the columns of the matrix D with rows representing each day since the first case report on the region. The Italian regional time series used in this paper for the forecast, has been taken from the Itali [16] models the input/output data by creati a hidden probabilistic representation of the data in its middle layer, also called latent space. The MA model modifies the traditional auto-encoders to employ an extra output branch derived from the late space. While the traditional output of auto-encoder architectures is designed to be trained to match t input, in our case, the extra output is modified to predict also the next sample of the sequencetime seri given to the input. Figure 2 depicts the MAE model architecture. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.