I dati delle piattaforme durante la crisi da COVID-19 Antonello Romano University of Siena, Italy This is a section of La geografia delle piattaforme digitali(DOI: 10.36253/978-88-5518-602-5) by Antonello Romano Firenze University Press Firenze 2022 https://doi.org/10.36253/978-88-5518-602-5.06

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The chapter critically explores the role of 'platform data' during the COVID-19 pandemic.

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It is available online at https://doi.org/10.36253/978-88-5518-602-5.06

References Wesolowski, A., Buckee, C. O., Bengtsson, L., Wetter, E., Lu, X., & Tatem, A. J. (2014). Commentary: containing the Ebola outbreakthe potential and challenge of mobile network data. PLoS currents, 6. 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e Buckee, C. O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., ... & Schroeder, A. (2020). Aggregated mobility data could help fight COVID-19. Science (New York, NY), 368(6487), 145-146. 10.1126/science.abb8021 Wellenius, G. A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T. C., Hennessy, J., ... & Gabrilovich, E. (2020). Impacts of state-level policies on social distancing in the United States using aggregated mobility data during the COVID-19 pandemic. arXiv preprint arXiv:2004.10172. 10.1038/s41467-021-23404-5 Nouvellet, P., Bhatia, S., Cori, A. et al. Reduction in mobility and COVID-19 transmission. Nat Commun 12, 1090 (2021). 10.1038/s41467-021-21358-2 10.1038/s41467-021-21358-2 Hao, Q., Chen, L., Xu, F., & Li, Y. (2020, August). Understandingthe Urban Pandemic Spreading of COVID-19 with Real World Mobility Data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3485-3492). 10.1145/3394486.3412860 Huang, J., Wang, H., Fan, M., Zhuo, A., Sun, Y., & Li, Y. (2020, August). Understanding the impact of the COVID-19 pandemic on transportationrelated behaviors with human mobility data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3443-3450). 10.1145/3394486.3412856 Campos-Vazquez, R. M., & Esquivel, G. (2021). Consumption and geographic mobility in pandemic times. Evidence from Mexico. Review of Economics of the Household, 1-19. 10.1007/s11150-020-09539-2 Micheli, D., Muratore, G., Vannelli, A., & Sola, G. (2020). Un modello dinamico su un approccio Big- Data alla mobilità per lo studio della diffusione del COVID-19 nel nord Italia, Notiziario tecnico n.1-2020. Buckee, C. O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., ... & Schroeder, A. (2020). Aggregated mobility data could help fight COVID-19. Science (New York, NY), 368(6487), 145-146. 10.1126/science.abb8021 Oliver, N, Lepri, B, Sterly, H, et al.(2020). Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances 6(23): eabc0764 10.1126/sciadv.abc0764 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big data & society, 1(1). 10.1177/2053951714528481