The impact of public transport on the diffusion of COVID-19 pandemic in Lombardy during 2020

Main Article Content

Francesca Ieva Greta Galliani Piercesare Secchi

Abstract

In 2020, the COVID-19 pandemic has impacted the world, affecting health, economy, education, and social behavior. Much concern was raised about the role of mobility in the diffusion of the disease, with particular attention to public transport. Indeed, understanding the relationship between mobility and the pandemic is key for developing effective public health interventions and policy decisions.


 


In this work, we aim to understand how mobility, and more specifically mobility by public transport, has affected the diffusion of the pandemic at the regional scale. We focus our attention on Lombardy, the most populated Italian region severely hit by the pandemic in 2020. We explore static mobility data provided by Regione Lombardia, the regional service district, and dynamic mobility data provided by Trenord, a railway operator which serves Lombardy and neighboring areas. We develop an inventive pipeline for the dynamic estimation of Origin-Destination matrices obtained from tickets and passenger counts. This allows us to spot potential triggers in pandemic diffusion enhanced by the concept of proximity induced by mobility. We also develop a novel perspective for assessing the relationship between mobility and overall mortality based upon a functional approach combined with a spatial correlation analysis aimed at identifying the diversified effects on mortality in small geographical areas as a result of the restrictions on mobility introduced to contrast the pandemic.

Keywords: COVID-19, mobility, OD matrix, spatial autocorrelation

Article Details

How to Cite
IEVA, Francesca; GALLIANI, Greta; SECCHI, Piercesare. The impact of public transport on the diffusion of COVID-19 pandemic in Lombardy during 2020. Medical Research Archives, [S.l.], v. 11, n. 9, sep. 2023. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4356>. Date accessed: 21 nov. 2024. doi: https://doi.org/10.18103/mra.v11i9.4356.
Section
Research Articles

References

1. Hu T, Wang S, She B, et al. Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges. Int J Digit Earth. 2021; 14(9):1126-1147. doi:10.1080/17538947.2021.1952324

2. Kraemer MUG, Golding N, Bisanzio D, et al. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource-poor settings. Sci Rep. 2019; 9(1):5151. doi:10.1038/s41598-019-41192-3.

3. Pieroni V, Facchini A, Riccaboni M. COVID-19 vaccination and unemployment risk: lessons from the Italian crisis. Sci Rep. 2021; 11(1):18538.doi:https://doi.org/10.1038/s41598-021-97462-6

4. Kartal MT, Depren Ö, Kiliç Depren S. The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis. Transp Res Interdiscip Perspect. 2021; 10:100366. doi:https://doi.org/10.1016/j.trip.2021.100366

5. Kishore N, Kahn R, Martinez PP, Salazar PM De, Mahmud AS, Buckee CO. Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2. Sci Rep. 2021; 11(1):6995. doi:10.1038/s41598-021-86297-w

6. Hu S, Xiong C, Yang M, Younes H, Luo W, Zhang L. A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. Transp Res Part C Emerg Technol. 2021; 124:102955. doi:https://doi.org/10.1016/j.trc.2020.102955

7. Vahedi B, Karimzadeh M, Zoraghein H. Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions. Nat Commun. 2021; 12(1):6440. doi:10.1038/s41467-021-22108-2

8. Ilin C, Annan-Phan S, Tai XH, Mehra S, Hsiang S, Blumenstock JE. Public mobility data enables COVID-19 forecasting and management at local and global scales. Sci Rep. 2021; 11(1):13531. doi:10.1038/s41598-021-92824-3

9. Iacus SM, Santamaria C, Sermi F, Spyratos S, Tarchi D, Vespe M. Human mobility and COVID-19 initial dynamics. Nonlinear Dyn. 2020; 101(3):1901-1919. doi:10.1007/s11071-020-05854-6

10. Mazzola V, Bonaccorsi G, Ieva F, Secchi P. The effects of mobility restrictions on public health: a functional data analysis for Italy over the years 2020 and 2021. Poster presented at: SEAS Conference; June 21-23th, 2023; Ancona.

11. Bonaccorsi G, Pierri F, Cinelli M, et al. Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci U S A. 2020; 117(27):15530-15535. doi:10.1073/pnas.2007658117

12. Alsger AA, Mesbah M, Ferreira L, Safi H. Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination Matrix. Transp Res Rec. 2015; 2535(1):88-96. doi:10.3141/2535-10

13. Willumsen LG. Estimation of an OD Matrix from Traffic Counts–A Review. Institute of Transport Studies, University of Leeds; 1978.

14. Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L. Time-evolving O-D matrix estimation using high-speed GPS data streams. Expert Syst Appl. 2016; 44:275-288. doi:https://doi.org/10.1016/j.eswa.2015.08.048

15. Manfredini F, Pucci P, Secchi P, Tagliolato P, Vantini S, Vitelli V. Treelet Decomposition of Mobile Phone Data for Deriving City Usage and Mobility Pattern in the Milan Urban Region. In: Paganoni AM, Secchi P, editors. Advances in Complex Data Modeling and Computational Methods in Statistics. Springer International Publishing; 2015. p. 133-147. doi:10.1007/978-3-319-11149-0_9.

16. Moran PAP. The Interpretation of Statistical Maps. J R Stat Soc Ser. 1948; 10(2):243-251. doi:https://doi.org/10.1111/j.2517-6161.1948.tb00012.x

17. Anselin L. Local Indicators of Spatial Association - LISA. Geogr Anal. 1995; 27(2):93-115. doi:https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

18. McCord MR, Mishalani RG, Goel P, Strohl B. Iterative Proportional Fitting Procedure to Determine Bus Route Passenger Origin–Destination Flows. Transp Res Rec. 2010; 2145(1):59-65. doi:10.3141/2145-07

19. Torti A, Galvani M, Urbano V, et al. Analysing transportation system reliability: the case study of the metro system of Milan. Technical Report 84, MOX-Report No. 84/2021. 2021. https://www.mate.polimi.it/biblioteca/add/qmox/84-2021.pdf. Accessed April 13, 2023.

20. Regione Lombardia. Matrice OD2020 - Passeggeri. Published 2019. Accessed July 17, 2023. https://www.dati.lombardia.it/Mobilit-e-trasporti/Matrice-OD2020-Passeggeri/hyqr-mpe2

21. Evans AW. Some properties of trip distribution methods. Transportation Res. 1970; 4:19-36. doi:https://doi.org/10.1016/0041- 1647(70)90072-9

22. Anselin L, Syabri I, Kho Y. GeoDa: An Introduction to Spatial Data Analysis. Geogr Anal. 2006; 38(1):5-22.

23. Bivand RS, Pebesma E, Gòmez-Rubio V. Applied Spatial Data Analysis with R. Springer; 2013.

24. Ermagun A, Levinson D. An Introduction to the Network Weight Matrix: Introduction to the Network Weight Matrix. Geogr Anal. 2017; 50. doi:10.1111/gean.12134

25. Zhu P, Li J, Hou Y. Applying a Population Flow-Based Spatial Weight Matrix in Spatial Econometric Models: Conceptual Framework and Application to COVID-19 Transmission Analysis. Ann Am Assoc Geogr. 2022; 112(8):2266-2286. doi:10.1080/24694452.2022.2060791

26. Cliff AD, Ord JK. Spatial Processes: Models and Applications. Pion Limited; 1981.

27. Scimone R, Menafoglio A, Sangalli LM, Secchi P. A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities. Spat Stat. 2022; 49:100541. doi:https://doi.org/10.1016/j.spasta.2021.100541

28. Smolyak A, Bonaccorsi G, Flori A, Pammolli F, Havlin S. Effects of mobility restrictions during COVID19 in Italy. Sci Rep. 2021; 11(1):21783. doi:10.1038/s41598-021-01076-x

29. Bayir MA, Demirbas M, Eagle N. Discovering spatiotemporal mobility profiles of cellphone users. In: 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops. 15-19 June 2009:1-9. doi:10.1109/WOWMOM.2009.5282489

30. Wilson AG. The Use of Entropy Maximising Models, in the Theory of Trip Distribution, Mode Split and Route Split. J Transp Econ Policy. 1969; 3(1):108-126.

31. Toch E, Lerner B, Ben-Zion E, Ben-Gal I. Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl Inf Syst. 2019; 58(3):501-523. doi:10.1007/s10115-018-1186-x