The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
June 04, 2020 Β· Declared Dead Β· π arXiv.org
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Authors
Paolo Cintia, Luca Pappalardo, Salvatore Rinzivillo, Daniele Fadda, Tobia Boschi, Fosca Giannotti, Francesca Chiaromonte, Pietro Bonato, Francesco Fabbri, Francesco Penone, Marcello Savarese, Francesco Calabrese, Giorgio Guzzetta, Flavia Riccardo, Valentina Marziano, Piero Poletti, Filippo Trentini, Antonino Bella, Xanthi Andrianou, Martina Del Manso, Massimo Fabiani, Stefania Bellino, Stefano Boros, Alberto Mateo Urdiales, Maria Fenicia Vescio, Silvio Brusaferro, Giovanni Rezza, Patrizio Pezzotti, Marco Ajelli, Stefano Merler, Paolo Vineis, Dino Pedreschi
arXiv ID
2006.03141
Category
cs.SI: Social & Info Networks
Cross-listed
physics.soc-ph,
stat.AP
Citations
52
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare systems and economies. Using Italian data at different geographic scales, we investigate the relationship between human mobility, which subsumes many facets of the population's response to the changing situation, and the spread of COVID-19. Leveraging mobile phone data from February through September 2020, we find a striking relationship between the decrease in mobility flows and the net reproduction number. We find that the time needed to switch off mobility and bring the net reproduction number below the critical threshold of 1 is about one week. Moreover, we observe a strong relationship between the number of days spent above such threshold before the lockdown-induced drop in mobility flows and the total number of infections per 100k inhabitants. Estimating the statistical effect of mobility flows on the net reproduction number over time, we document a 2-week lag positive association, strong in March and April, and weaker but still significant in June. Our study demonstrates the value of big mobility data to monitor the epidemic and inform control interventions during its unfolding.
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