The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study
Open accessPublished version of a preprint
Abstract
Background Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.Methods We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.Findings Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.Interpretation This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.Funding None.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“CDR data are also useful to draw similar conclusions—analysing the data for the most affected counties in the USA [102] found that the decreased mobility strongly correlates (Pearson correlation coefficients above 0.7) with decreased COVID-19 case growth rates.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [102]
Experimental — accuracy not yet measuredIn the citing paper
“Several studies across different countries have shown their effectiveness [101,102].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [102]
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
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
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
Self-citationFirst case of 2019 novel coronavirus in the United States
Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., Spitters, C., Ericson, K., Wilkerson, S., Tural, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001191 · Cited by 6,030
Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-0504 · Cited by 5,970
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
The effect of human mobility and control measures on the COVID-19 epidemic in China
Kraemer, M. U. G., Yang, C.-H., Gutiérrez, B., Wu, C., Klein, B., Pigott, D. M., open COVID-19 data working group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,987
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
Kucharski, A. J., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30144-4 · Cited by 2,699
Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period
Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020) · Science · doi:10.1126/science.abb5793 · Cited by 2,611
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010
Cited by
Sorted by citations
Items 801–822 of 822
Impact of COVID-19 mild lockdowns on residential water consumption: evidence from Brazil
de Morais, L. S. R., Campos, M. A. S., Baumann, L., & Cobacho, R. (2026) · H2Open Journal · doi:10.2166/h2oj.2026.052
Empirical Evidence on Factors Affecting Mobility During the COVID-19 Pandemic
Liu, Z. (2026) · Springer eBooks · doi:10.1007/978-981-95-6821-5_8
Zou, L., Lin, B., Ramakrishnan, T., & Yang, M. (2026) · Transactions in Urban Data Science and Technology · doi:10.1177/27541231261418712
Zhu, W., Liang, X., Wang, X., Zhou, W., Ge, Q., Yang, M., Zheng, B., Wen, Z., Liu, Z., Yao, Y., et al. (2026) · Infectious Disease Modelling · doi:10.1016/j.idm.2026.02.001
Fiandrino, S., Kulkarni, S., Cornale, P., Ghivarello, S., Birello, P., Parazzoli, S. M., Moss, F., Gaetano, A. D., Liberatore, D., D'Ignazi, J., et al. (2026) · medRxiv · doi:10.64898/2026.02.26.26347164
The persistence of behavioral disparities post-pandemic: Insights from activity time series data
Du, H., Xu, S., Rankin, N., (328526), H. Y., & Gardner, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-41553-9
Self-citationMining Mobile Point-of-Interest Visit Data for Socioeconomic Insights
Ma, A., Foutz, N. Z., & Breeden, R. W. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00395
WITHDRAWN: Impact of COVID-19 mild lockdowns on residential water consumption: evidence from Brazil
Morais, L. S. R., Campos, M. A. S., Baumann, L. R. F., & Cobacho, R. (2026) · H2Open Journal · doi:10.1016/j.htopen.2026.100008
RetractedKanekar, S., & Banerjee, T. (2026) · Research Square · doi:10.21203/rs.3.rs-9131274/v1
Forecasting of dengue importation risk in Brazil using deep learning and mobility networks
Chen, X., & Moraga, P. (2026) · npj Digital Public Health · doi:10.1038/s44482-026-00015-9
Mobility, demographics, and pandemic spread: a framework for targeted policy interventions
Sayin, M., Eryarsoy, E., & Zaim, S. (2026) · Health Policy and Technology · doi:10.1016/j.hlpt.2026.101241
Sabri, I., Mohammed, H. S., Kausar, N., Gani, N., Waqar, Z., Pathath, A. W., & Jeelani, A. (2026) · International Journal of Drug Delivery Technology · doi:10.25258/ijddt.16.33s.96
(2024) · Institutional Repositories DataBase (IRDB)
Ndiaye, N. A., Sankhe, S., Jallow, M. M., Kane, M., Ndour, K., Diallo, A. B., Mbanne, M., Top, N. M., Diop, S. M. B. S., Dieng, M., et al. (2026) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.70273
Han, C., & Guhathakurta, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7037104
Niu, C., He, S., Fang, C., Niu, L., Xiao, Y., & Zhang, W. (2026) · Systems · doi:10.3390/systems14070772
Inferring mobility reductions from COVID-19 disease spread along the urban-rural gradient
Paltra, S., Dehning, J., Priesemann, V., & Nagel, K. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1840916
Socioeconomic transitions and the spatial dynamics of syphilis transmission in urbanizing China
Li, C., Zhang, H., Li, J., Ye, X., Zhou, S., Lao, X., & Chen, M. Y. (2026) · International Journal for Equity in Health · doi:10.1186/s12939-026-02949-1
A Fractional-Order SEIHRP Model for COVID-19: Dynamics, Stability and Pathogens as Shedding Effects
Malik, P., Yadav, M., Chahar, P., Rani, R., & Attri, A. K. (2026) · Boletim da Sociedade Paranaense de Matemática · doi:10.5269/bspm.81662
Mujtabe, A. A., Ibnat, A., Akther, M. S., Adri, N., & Shome, P. (2026) · Learning Environments Research · doi:10.1007/s10984-026-09612-0
Metadata
- Authors
- Badr, H. S., Du, H., Marshall, M., Dong, E., Squire, M. M., & Gardner, L. M.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Gardner, Lauren M
- Venue
- The Lancet Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 20
- Issue
- 11
- Pages
- 1247-1254
- ISSN
- 1473-3099 (ISSN-L)
- PMC
- PMC7329287
- OpenAlex
- W3039225830
- Cited by
- 851
- References
- 18
- Citations by year
- 2026: 30 · 2025: 51 · 2024: 75 · 2023: 104 · 2022: 185 · 2021: 325 · 2020: 79
- FWCI
- 27.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet Infectious Diseases (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- SARS-CoV-2 transmission, human mobility, social distancing, stay-at-home orders, mobile phone data, movement patterns, behavior change, incubation period, non-pharmaceutical interventions, United States counties, SARS-CoV-2
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Humans, Pneumonia, Viral, Public Health, Quarantine, United States, Models, Statistical, Coronavirus Infections, Government Regulation, Pandemics
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
