COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown

  1. Emanuele Pepe
  2. Paolo Bajardi
  3. Laetitia Gauvin
  4. Filippo Privitera
  5. Brennan Lake
  6. Ciro Cattuto
  7. Michele Tizzoni

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

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.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.22.20039933: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study comes with limitations. Although the sample of users under study well matches the population distribution of the Italian provinces (see Fig. S5 in the Supplementary Information file), it is not representative of the general Italian population in terms of age and socio-economic status although we verified that it does represent fairly well population distribution at the provincial level. The population under study is not constant over time because users can opt-out sharing their geo-location at any time. Also, the internal logic of the data gathering system might fail in sensing users who stop moving for a prolonged period of time. This suggests that our estimates may underestimate the true effects of mobility restrictions. Finally, we do not try to characterize the trips made by our users, and we have no information as to whether a given trip is among those with an authorized purposed. Therefore, our results should not be interpreted as an assessment of the adherence by the Italian population to the restrictions. Because of the exceptional nature of the unfolding events, there is little available evidence to compare our results to. Recent studies, based on the analysis of digital traces and contact surveys, have demonstrated that NPIs were effective in containing the COVID-19 outbreak in China (8, 19, 24). The estimated maximumum reduction in mobility and social contacts in China during the lockdown was about 80%, with respect to a baseline set on January 1, 2020 (1...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1038/s41597-020-00575-2
  3. Version published to 10.1101/2020.03.22.20039933 on medRxiv