In this talk we will present a predictive model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. An adjusted time-dependent SIRD model is used to predict the behaviour of the epidemic, specifically the number of susceptible, infected, deceased and recovered people. Predictive model performance is evaluated using comparison with real data. Furthermore a special attention to Lombardy region, the one most affected by the epidemics in Italy, will be payed, and a wavelet and cross-correlation study of the predictive power of Twitter messages and of emergency calls to the rescue service will be presented, as possible tools to anticipate the peaks of new infections or to evaluate the effects of national policies for restriction measures.

References

1. Luisa Ferrari, Giuseppe Gerardi, Giancarlo Manzi, Alessandra Micheletti, Federica Nicolussi, Elia Biganzoli, Silvia Salini, Modelling provincial Covid-19 epidemic data in Italy using an adjusted time-dependent SIRD model, preprint, 2020 , arXiv:2005.12170

2. Luisa Ferrari, Giuseppe Gerardi, Giancarlo Manzi, Alessandra Micheletti, Federica Nicolussi, Silvia Salini, COVID-Pro in Italy: a dashboard for a province-based analysis, preprint, 2020, arXiv:2004.12779

3. B.A.Rivieccio, A. Micheletti, M. Maffeo, M. Zignani, A.Comunian, F.Nicolussi, S.Salini, G.Manzi, F.Auxilia, M. Giudici, G.Naldi, S. Gaito, S. Castaldi, E. Biganzoli, CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region, medRxiv preprint, 2020. doi: https://doi.org/10.1101/2020.10.14.20212415

Biography: Alessandra Micheletti is Associate Professor of Probability and Mathematical Statistics at the Dept. of Environmental Science and Policy, Università degli Studi di Milano. She was awarded her PhD in computational mathematics and is a member of the council of the European Consortium for Mathematics in Industry, of the Data Science Research Center of University of Milan and coordinator of the H2020 MSCA project BIGMATH- “Big data challenges for Mathematics”. She is then managing editor of the Journal of Mathematics in Industry and guest editor of a special issue entitled “Mathematical models of the spread and consequences of the SARS-CoV-2 pandemics. Effects on health, society, industry, economics and technology.” Her main research interests are in the field of Statistics and Topological Data Analysis applied to industrial and life sciences problems.