Leveraging cellphone-derived mobility networks to assess Covid-19 travel risk
Since the beginning of the Covid-19 pandemic, public health authorities across the globe have implemented policies, such as lockdowns, in an attempt to reduce population mobility and, consequently, person-to-person contacts. It is well known that lockdowns reduce mobility, but to what extent does this reduction in mobility lead to lower infection rates? In this paper we extend the endemic-epidemic modeling framework in a principled manner, incorporating temporally changing mobility network data and quantifying the risk associated with travelling throughout the first year of the pandemic in two Spanish communities.
Authors
- Justin Slater (ORCID: https://orcid.org/0000-0002-6648-5990)
- Patrick Brown (ORCID: https://orcid.org/0000-0003-2541-3744)
- Jeffrey S. Rosenthal (ORCID: https://orcid.org/0000-0002-5118-6808)
- Jorge Mateu (ORCID: https://orcid.org/0000-0002-2868-7604)
Institutions
- Universitat Jaume I (ES)
- University of Toronto (CA)
- University of Guelph (CA)
Publication Details
- Journal
- The Annals of Applied Statistics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1214/24-aoas1914
- Citations
- 1
- Primary Topic
- Human Mobility and Location-Based Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00