ABSTRACT
In this article's Data Availability Statement, the URL to the replication code was missing. Find the full Data Availability Statement below along with the link to the openly available code on GitHub. Data Availability Statement. If possible, results of computed indicators or aggregated statistics will be made available through the website of the Gambia Bureau of Statistics (GBoS) or the Public Utilities Regulatory Authority (PURA). Details of methodologies employed for computing indicators can be found on the World Bank COVID19 Mobility Task Force Github repository. Code adjusted for running a system under PURA is maintained on the University of Tokyo's Spatial Data Commons Github repository and can be found here: https://github.com/SpatialDataCommons/CDR-wb-indicators-package. © The Author(s), 2023. Published by Cambridge University Press on behalf of Applied Probability Trust.
ABSTRACT
Aggregated data from mobile network operators (MNOs) can provide snapshots of population mobility patterns in real time, generating valuable insights when other more traditional data sources are unavailable or out-of-date. The COVID-19 pandemic has highlighted the value of remotely-collected, high-frequency, localized data in inferring the economic impact of shocks to inform decision-making. However, proper protocols must be put in place to ensure end-to-end user-confidentiality and compliance with international best practice. We demonstrate how to build such a data pipeline, channeling data from MNOs through the national regulator to the analytical users, who in turn produce policy-relevant insights. The aggregated indicators analyzed offer a detailed snapshot of the decrease in mobility and increased out-migration from urban to rural areas during the COVID-19 lockdown. Recommendations based on lessons learned from this process can inform engagements with other regulators in creating data pipelines to inform policy-making.