ArticleScientific reports2021
Public mobility data enables COVID-19 forecasting and management at local and global scales.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission.Nature communications · 2024Pooled it
- Forecasting Thailand's mobility trends using Feature Engineered XGBoost for pandemic crisis movement management.PloS one · 2026Article
- Explaining COVID-19 dynamics through user activity data from digital platforms with Yandex's self-isolation index as a case study.Scientific reports · 2025Article
- Article
- Private commercial companies sharing health-relevant consumer data with health researchers in sub-Saharan Africa: an ethical exploration.Policy studies (Policy Studies Institute) · 2024Article
- Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study.NPJ digital medicine · 2024Article
- The effect of mobility reductions on infection growth is quadratic in many cases.Scientific reports · 2024Article
- The impact of health inequity on spatial variation of COVID-19 transmission in England.PLoS computational biology · 2024Article
- Unravelling habituation for COVID-19-related information: A panel data study in Japan.PloS one · 2024Article
- Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024Article
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- Article
- Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model.Scientific reports · 2023Article
- Places Nigerians visited during COVID-19 government stay-home policy: evidence from secondary analysis of data collected during the lockdown.Journal of public health in Africa · 2023Article
- Trends in recreational walking trail usage in Ireland during the COVID-19 pandemic: Implications for practice.Journal of outdoor recreation and tourism · 2023Article
- Replacing discontinued Big Tech mobility reports: a penetration-based analysis.Scientific reports · 2023Article
- Changes in mobility patterns during the COVID-19 pandemic in Zambia: Implications for the effectiveness of NPIs in Sub-Saharan Africa.PLOS global public health · 2023Article
- Evaluating the effectiveness of lockdowns and restrictions during SARS-CoV-2 variant waves in the Canadian province of Nova Scotia.Frontiers in public health · 2023Article
- Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility-collected by Google, Facebook, and other providers-can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases. An advantage of the approach is that it involves minimal assumptions about disease dynamics, and requires only publicly-available data. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. We find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.