An Application of Neural Networks to Predict COVID-19 Cases in Italy †

: COVID-19 pandemic has become the greatest worldwide threat, as it has spread rapidly among individuals in most countries around the world. This study concerns the problem of weekly prediction of new COVID-19 cases in Italy, aiming to find the best predictive model for daily infection number in countries with a large number of confirmed cases. We compare the forecasting performance of linear and nonlinear forecasting models using weekly COVID-19 data for the period between 24 February 2020 until 16 May 2022. We discuss various forecasting approaches, including a Nonlinear Autoregressive Neural Network (NARNN) model, an Autoregressive Integrated Moving Average (ARIMA) model, a TBATS model, and Exponential Smoothing on the collected data and compared their accuracy using the data collected from 23 March 2020 to 20 April 2020, choosing the model with the lowest Mean Absolute Percentage Error (MAPE) value. Since the linear models seem to not easily follow the nonlinear patterns of daily confirmed COVID-19 cases, Artificial Neural Network (ANN) have been successfully applied to solve problems of forecasting nonlinear models. The model has been used for weekly prediction of COVID-19 cases for the next 4 weeks without any additional intervention. The prediction model can be applied to other countries struggling with the COVID-19 pandemic, to any possible future pandemics, and also help make better decisions in future.