Abstract
Since December 2019, the novel coronavirus disease (COVID-19) has had a considerable impact on the health and socio-economic fabric of Italy. The effective reproduction number Rt is one of the most representative indicators of the contagion status as it reports the number of new infections caused by an infected subject in a partially immunized population. The task of predicting Rt values forward in time is challenging and, historically, it has been addressed by exploiting compartmental models or statistical frameworks. The present study proposes an Artificial Neural Networks-based approach to predict the Rt temporal trend at a daily resolution. For each Italian region and autonomous province, 21 daily COVID-19 indicators were exploited for the 7-day ahead prediction of the Rt trend by means of different neural network architectures, i.e., Feed Forward, Mono-Dimensional Convolutional, and Long Short-Term Memory. Focusing on Lombardy, which is one of the most affected regions, the predictions proved to be very accurate, with a minimum Root Mean Squared Error (RMSE) ranging from 0.035 at day t + 1 to 0.106 at day t + 7. Overall, the results show that it is possible to obtain accurate forecasts in Italy at a daily temporal resolution instead of the weekly resolution characterizing the official Rt data.
Highlights
Publisher’s Note: MDPI stays neutralSince December 2019, the entire world has been fighting the COrona VIrus Disease2019 (COVID-19) pandemic, whose first case was identified in Wuhan, China [1]
The present study focused on all 21 Italian regions, aiming to provide an approach based on Artificial Neural Networks (ANNs) for predicting the effective reproduction number (Rt )
The present study provides an approach fully based on ANNs, not requiring a-priori knowledge for building and fitting the models, as opposed to traditional compartmental frameworks, which need a preliminary specification of disease-related hyperparameters
Summary
Since December 2019, the entire world has been fighting the COrona VIrus Disease2019 (COVID-19) pandemic, whose first case was identified in Wuhan, China [1]. Accurate estimates of the Rt value are crucial for decision makers to control the spread of the disease and to plan containment measures [6]. The temporal resolution of the parameters to be estimated has a fundamental role in developing predictive models in order to facilitate the timely application of appropriate containment measures. For this reason, the present study focused on all 21 Italian regions, aiming to provide an approach based on Artificial Neural Networks (ANNs) for predicting the effective reproduction number (Rt ). Obtaining daily predictions of the Rt represents a crucial aspect because a finer temporal resolution constitutes a benefit for tracking the disease spread and supporting the corresponding policy makers decisions
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