Next Article in Journal
DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning
Previous Article in Journal
Client Selection in Federated Learning under Imperfections in Environment
 
 
Article

An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy

1
Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Via Marco Biagi, 5, 73100 Lecce, Italy
2
Department of Engineering for Innovation, University of Salento, Via Provinciale Lecce-Monteroni, 73100 Lecce, Italy
3
Department of Biological and Environmental Sciences and Technologies, University of Salento, Via Provinciale Lecce-Monteroni, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Luis Javier Garcia Villalba
AI 2022, 3(1), 146-163; https://doi.org/10.3390/ai3010009
Received: 27 January 2022 / Revised: 14 February 2022 / Accepted: 24 February 2022 / Published: 26 February 2022
(This article belongs to the Section Medical & Healthcare AI)
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. View Full-Text
Keywords: accurate daily forecasts; artificial neural networks; COVID-19; effective reproduction number Rt; epidemiological factors; Italian regions accurate daily forecasts; artificial neural networks; COVID-19; effective reproduction number Rt; epidemiological factors; Italian regions
Show Figures

Figure 1

MDPI and ACS Style

Gatto, A.; Aloisi, V.; Accarino, G.; Immorlano, F.; Chiarelli, M.; Aloisio, G. An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy. AI 2022, 3, 146-163. https://doi.org/10.3390/ai3010009

AMA Style

Gatto A, Aloisi V, Accarino G, Immorlano F, Chiarelli M, Aloisio G. An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy. AI. 2022; 3(1):146-163. https://doi.org/10.3390/ai3010009

Chicago/Turabian Style

Gatto, Andrea, Valeria Aloisi, Gabriele Accarino, Francesco Immorlano, Marco Chiarelli, and Giovanni Aloisio. 2022. "An Artificial Neural Network-Based Approach for Predicting the COVID-19 Daily Effective Reproduction Number Rt in Italy" AI 3, no. 1: 146-163. https://doi.org/10.3390/ai3010009

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop