The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown
Open accessPublished version of a preprint
Abstract
Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020) · Science · doi:10.1126/science.aba9757 · Cited by 3,694
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050074 · Cited by 2,900
Cited by
Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures
Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004978117 · Cited by 1,051
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle
Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., De Nadai, M., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc0764 · Cited by 631
Self-citationTwitter reveals human mobility dynamics during the COVID-19 pandemic
Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0241957 · Cited by 242
Lee, S., Ko, E., Jang, K., & Kim, S. (2023) · Cities · doi:10.1016/j.cities.2023.104223 · Cited by 37
Metadata
- Authors
- Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M.
- Venue
- Scientific Data
- Published
- 2020-07-08
- Volume
- 7
- Issue
- 1
- Pages
- 230-230
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W3041250660
- Cited by
- 318
- References
- 3
- Type
- data paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. https://app.cochat.ai/papers/10.1038/s41597-020-00575-2
- The paper
- Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data. https://doi.org/10.1038/s41597-020-00575-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
