Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Luisa Ferrari; Giuseppe Gerardi; Giancarlo Manzi; Alessandra Micheletti; Federica Nicolussi; Elia Biganzoli; Silvia Salini

doi:10.3390/ijerph18126563

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Int J Environ Res Public Health. 2021 Jun 18;18(12):6563. doi: 10.3390/ijerph18126563.

Authors

Luisa Ferrari¹, Giuseppe Gerardi², Giancarlo Manzi³, Alessandra Micheletti⁴, Federica Nicolussi³, Elia Biganzoli⁵, Silvia Salini³

Affiliations

¹ Department of Statistical Science, University College London, London WC1E 6BT, UK.
² Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy.
³ Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy.
⁴ Department of Environmental Science and Policy and Data Science Research Center, University of Milan, 20122 Milan, Italy.
⁵ Department of Clinical Sciences and Community Health and Data Science Research Center, University of Milan, 20122 Milan, Italy.

Abstract

In this paper, we develop a forecasting 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 local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Keywords: COVID-19; EU NUTS-3 regions; Italy; SIRD-derived models; epidemic data.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Epidemics*
Forecasting
Humans
Italy / epidemiology
SARS-CoV-2