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Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study
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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.
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“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]
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“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]
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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
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György, A., Marlow, T., Abrahão, B., & Makovi, K. (2023) · Network Science · doi:10.1017/nws.2023.6 · Cited by 4
Forsyth, J., Wang, L., & Thomas-Bachli, A. L. (2023) · Canadian Journal of Public Health · doi:10.17269/s41997-023-00791-4 · Cited by 4
Murari, A., Gelfusa, M., Craciunescu, T., Gelfusa, C., Gaudio, P. D., Bovesecchi, G., & Rossi, R. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1222389 · Cited by 4
Peng, N., & Liu, X. (2024) · Annals of the American Association of Geographers · doi:10.1080/24694452.2024.2313517 · Cited by 4
Yang, J., Zhang, Y., Zhao, Q., & Zou, M. (2024) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2024.101085 · Cited by 4
Li, X., Li, J., Liu, H., Mínguez‐Alarcón, L., van Loosdrecht, M. C. M., & Wang, Q. (2025) · Water Research · doi:10.1016/j.watres.2025.123114 · Cited by 4
Pourhasanzadeh, M., Ahmadi, G., & Aram, F. (2025) · Regional Science Policy & Practice · doi:10.1016/j.rspp.2025.100177 · Cited by 4
Fujiwara, N., Onaga, T., Wada, T., Takeuchi, S., Seto, J., Nakaya, T., & Aihara, K. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07403-5 · Cited by 4
Fong, D., Mair, M. J., Lanthaler, F., Alber, M., & Mitterer, M. (2021) · International Journal of Cancer · doi:10.1002/ijc.33838 · Cited by 4
Maldonado, P., Peng, A., Ouyang, D., Suckale, J., & Ho, D. E. (2022) · American Journal of Public Health · doi:10.2105/ajph.2021.306576 · Cited by 4
Wong, N. S., Lee, S. S., Mitchell, K. M., Yeoh, E., & Wang, C. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-021-06963-2 · Cited by 4
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Kodera, S., Ueta, H., Unemi, T., Nakata, T., & Hirata, A. (2023) · Vaccines · doi:10.3390/vaccines11091457 · Cited by 4
Mohammad, M. A., Koul, S. M., Gale, C. P., Alfredsson, J., James, S. K., Fröbert, O., Ömerovic, E., & Erlinge, D. (2020) · Journal of Internal Medicine · doi:10.1111/joim.13206 · Cited by 4
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Urbina, J. U. M., González-Farı́as, G., Ramírez-Ramírez, L. L., & González, D. I. R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0261650 · Cited by 4
Luo, Z., Li, L., Ma, J., Tang, Z., Shen, H., Zhu, H., & Wu, B. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11040229 · Cited by 4
Venkatesh, U., P., A. G., Ara, T., Rahman, M. M., & Kishore, J. (2022) · Healthcare Informatics Research · doi:10.4258/hir.2022.28.2.160 · Cited by 4
Yuan, B., Zhao, H., & Li, J. (2022) · The International Journal of Health Planning and Management · doi:10.1002/hpm.3507 · Cited by 4
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Del-Águila-Mejía, J., García-García, D., Rojas‐Benedicto, A., Rosillo, N., Guerrero-Vadillo, M., Peñuelas, M., Ramis, R., Gómez‐Barroso, D., & Donado-Campos, J. D. M. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054356 · Cited by 4
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Lu, L., Xu, J., Wei, J., & Shults, F. L. (2024) · Natural Hazards Review · doi:10.1061/nhrefo.nheng-1994 · Cited by 4
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Diniz‐Filho, J. A. F., Jardim, L., Toscano, C. M., & Rangel, T. F. (2020) · medRxiv · doi:10.1101/2020.07.28.20163493 · Cited by 4
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Sundar, S., Schwab, P., Tan, J. Z. H., Brufau, S. R., Celi, L. A., Wangmo, D., & Penna, N. D. (2021) · medRxiv · doi:10.1101/2021.11.06.21266007 · Cited by 4
Chamie-Quintero, J. J., Hibberd, J. A., & Scheim, D. E. (2021) · doi:10.31219/osf.io/9egh4 · Cited by 4
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Yang, S., Chu, W., & Kevrekidis, P. G. (2024) · Proceedings of the European Academy of Sciences and Arts · doi:10.4081/peasa.42 · Cited by 3
Pandey, A., Zhong, L., & Rennert, L. (2026) · BMC Global and Public Health · doi:10.1186/s44263-026-00293-8 · Cited by 3
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Ogunyemi, K. O., Shaikh, A. T., Bashir, A., Dixon, B. E., Warmath, D., Toth, M., Salim, J., Lokossou, V. K., Teferi, F., Baer, C., et al. (2026) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2026.101452 · Cited by 3
Michalitsi-Psarrou, A., Παπαγεωργίου, Ι. Λ., Ntanos, C., & Psarras, J. E. (2021) · Applied Sciences · doi:10.3390/app11146530 · Cited by 3
Wei, J., Yang, S., & Jiang, H. (2023) · Administration & Society · doi:10.1177/00953997231216989 · Cited by 3
Yang, Z., Krishnan, R., & Li, B. (2024) · INFORMS Journal on Data Science · doi:10.1287/ijds.2023.0013 · Cited by 3
Topîrceanu, A. (2024) · Mathematics · doi:10.3390/math12172765 · Cited by 3
Naranjo, J. M. V., González-Escobar, R., Jiménez-Espada, M., & Vivas, J. G. (2024) · Land · doi:10.3390/land13111778 · Cited by 4
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Cockerham, W. C. (2025) · Routledge eBooks · doi:10.4324/9781003569824 · Cited by 3
Zhu, X., Ahmad, M. I., Rehman, R. U., Naseem, M. A., & Ahmad, M. (2021) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s308741 · Cited by 3
Robust optimal parameter estimation for the susceptible-unidentified infected-confirmed model
Lee, C., Kwak, S., Kim, S., Hwang, Y., Choi, Y., & Kim, J. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111556 · Cited by 3
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Stewart, B. P., Hou, X., Tariverdi, M., Pambudi, E. S., Harimurti, P., Jia, W., Nagpal, S., Vicencio, J. M., Görgens, M., & Garrett, K. P. (2022) · World Bank, Washington, DC eBooks · doi:10.1596/37027 · Cited by 3
d’Almeida, S. (2022) · Vaccines · doi:10.3390/vaccines10060852 · Cited by 3
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Lu, X., Zhao, X., & Zhao, X. (2022) · Journal of Computer Information Systems · doi:10.1080/08874417.2022.2098879 · Cited by 3
Weichelt, R. D., Archer, J., Watrel, R. H., Watrel, R., & Archer, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-94350-9_29 · Cited by 3
Sargsyan, A., Galstyan, N., Nahatakyan, H., & Morales‐Suárez‐Varela, M. (2022) · Atmosphere · doi:10.3390/atmos13101563 · Cited by 3
Albassam, D., Nouh, M., & Hosoi, A. E. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20075343 · Cited by 3
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Marlow, T., Makovi, K., & Abrahão, B. (2023) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231231198857 · Cited by 3
Jiang, W., Wen, T., Huang, Y.-C., Chiou, H., Chen, W. J., Hsiung, C. A., Sytwu, H., & Tsou, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-44474-z · Cited by 3
Liu, H., Zong, H., Yang, Y., Schwebel, D. C., Xie, B., Ning, P., Rao, Z., Li, L., & Hu, G. (2024) · JMIR Public Health and Surveillance · doi:10.2196/65439 · Cited by 3
Laudares, H., Batista, C., Gagliardi, P. H., Rocha, R., & Ray, N. (2022) · PLOS Global Public Health · doi:10.1371/journal.pgph.0004527 · Cited by 3
Behaviour strategies of digital natives during the COVID-19 pandemic
Sivrikova, N. V., & Kharlanova, E. M. (2020) · Gercenovskie čteniâ: psihologičeskie issledovaniâ v obrazovanii · doi:10.33910/herzenpsyconf-2020-3-59 · Cited by 3
Hepp, T., Marquart, P., Jauck, C., & Gefeller, O. (2021) · Das Gesundheitswesen · doi:10.1055/a-1341-1575 · Cited by 3
The COVID-19 pandemic and the transformation of health policy: a syndemic perspective
Chen, X., & Fan, A. (2021) · Journal of Chinese Economic and Business Studies · doi:10.1080/14765284.2021.1940453 · Cited by 3
Liu, V. X., Thai, K. K., Galin, J., Gerstley, L. D., Myers, L. C., Parodi, S. M., Chen, Y.-F. I., Goler, N. C., Escobar, G. J., & Kipnis, P. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048211 · Cited by 3
Determining travel fluxes in epidemic areas
Chen, D., Xue, Y., & Xiao, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009473 · Cited by 3
Striking lineage diversity of severe acute respiratory syndrome coronavirus 2 from non-human sources
Muñoz, M., Patiño, L. H., Ballesteros, N., Castañeda, S., Luna, N., Noguera, L. A. D., Hernández‐Pereira, C. E., Shaban, M. V., Muñoz, S. A., Paniz‐Mondolfi, A., et al. (2021) · One Health · doi:10.1016/j.onehlt.2021.100363 · Cited by 3
Lestari, F., Kadir, A. R., Idham, M., Azwar, F., Ramadhany, G., Sembiring, F., Ghazmahadi, G., Hakim, A., Modjo, R., Widanarko, B., et al. (2021) · Sustainability · doi:10.3390/su14010169 · Cited by 3
Urban mobility patterns and the spatial distribution of infections in Santiago de Chile
Bedoya-Maya, F., Calatayud, A., Giraldez, F., & González, S. S. (2022) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2022.06.011 · Cited by 3
Effects of the Government Response and Community Mobility on the COVID-19 Pandemic in Southeast Asia
Widyasari, V., Lee, C. B., Lin, K., Husnayain, A., Su, E. C., & Wang, J. (2022) · Healthcare · doi:10.3390/healthcare10102003 · Cited by 3
Lentz, T. S., Konkel, R. H., Gallagher, H., & Ratkowski, D. (2022) · Crime Science · doi:10.1186/s40163-022-00172-1 · Cited by 3
The influence of video learning media in offline learning in pandemic Covid-19
Sumilat, J. M., Tumurang, H. J., & Pulisir, V. L. S. (2022) · AIP conference proceedings · doi:10.1063/5.0102482 · Cited by 3
The impact of public transportation on the transmission of COVID-19 in Rwanda
Umutoni, B., Tumushime, J. C., Hewins, B., Udahemuka, J. C., Ndishimye, P., Kelvin, D. J., & Martinez, G. S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1345433 · Cited by 3
Sarim, M., Zhao, Q., & Bailey, N. (2021) · Human dynamics in smart cities · doi:10.1007/978-3-030-72808-3_14 · Cited by 3
Hosseini, M., & Gittler, A. M. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377932 · Cited by 3
Datta, S., & Saratchand, C. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06712-9 · Cited by 3
Degano, I., & Lotito, P. A. (2021) · Transactions in GIS · doi:10.1111/tgis.12799 · Cited by 3
The allometric propagation of COVID-19 is explained by human travel
Tuladhar, R., Grigolini, P., & Santamarı́a, F. (2021) · Infectious Disease Modelling · doi:10.1016/j.idm.2021.12.003 · Cited by 3
Rahnama, A., Asl, A. S., Kazemi, Z., Nobakht, M., Abbaszadeh, M., Dabiri, F., & Roozbeh, N. (2022) · Disease and Diagnosis · doi:10.34172/ddj.2022.06 · Cited by 3
Oliveira, S., Ribeiro, A. I., Nogueira, P., & Rocha, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274286 · Cited by 3
Sim, H.-Y., Kim, M.-H., & Shon, H. (2022) · 국토계획 · doi:10.17208/jkpa.2022.08.57.4.94 · Cited by 3
Ramadhan, M. S., & Syakurah, R. A. (2021) · International Journal of Public Health Science (IJPHS) · doi:10.11591/ijphs.v10i2.20635 · Cited by 3
Through the looking-glass of “Go To Travel Campaign” in Japan, and what Alice found there
Takagi, H. (2021) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2021.102048 · Cited by 3
Changes in mobility and socioeconomic conditions in Bogotá city during the COVID-19 outbreakPreprint
E., M. A. D., Campi, M., & Olmos, L. E. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2008.11850 · Cited by 3
Yamamoto, N., & Wang, H. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3682092 · Cited by 3
High resolution proximity statistics as early warning for US universities reopening during COVID-19Preprint
Mehrab, Z., Ranga, A. G., Sarkar, D., Venkatramanan, S., Baek, Y. C., Swarup, S., & MARATHE, M. V. (2020) · medRxiv · doi:10.1101/2020.11.21.20236042 · Cited by 3
Temporal considerations in the 2021 COVID-19 lockdown of Ho Chi Minh CityPreprint
Plan, E. L. C. V. M., Thi, H. L., Le, D. M., & Phan, H. (2021) · medRxiv · doi:10.1101/2021.08.04.21261332 · Cited by 3
Herrera, M., & Godoy‐Faúndez, Á. (2021) · Future Internet · doi:10.3390/fi13050112 · Cited by 3
Kreslake, J. M., Aarvig, K., Muller-Tabanera, H., Vallone, D., & Hair, E. C. (2023) · JMIR Public Health and Surveillance · doi:10.2196/44950 · Cited by 2
The Impact of COVID-19 on Mortality in 34 Countries and Economies
Dang, O., Kamiya, S., & Zhou, L. (2025) · North American Actuarial Journal · doi:10.1080/10920277.2025.2496726 · Cited by 2
Baccega, D., Aguilar, J., Baquero, C., Anta, A. F., & Ramírez, J. M. (2025) · IEEE Access · doi:10.1109/access.2025.3586508 · Cited by 2
Hu, Z., Chang, W., Jang, Y.-O., Jo, Y., & Jung, J.-H. (2025) · Scientific Reports · doi:10.1038/s41598-025-05583-z · Cited by 2
Mobile phone data analyses for public health research: a scoping review
Cheng, X., Jiang, W., Liu, S., Lou, X., Li, Y., Liu, L., Li, M., Wang, X., Cen, Y., Chong, A. Y., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1728985 · Cited by 2
Xu, S., Lin, L., Ke, W., Xiang, X., & Huang, T. (2025) · Population Space and Place · doi:10.1002/psp.70152 · Cited by 2
Smail, M. M. A., Singh, J., Ismail, A. M. A., Cummings, E., Hanoman, C., Rupee, S., Rupee, K., & Adeghate, E. A. (2021) · Advances in biochemistry in health and disease · doi:10.1007/978-3-030-84763-0_5 · Cited by 2
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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
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