The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data
Open accessAbstract
Background: Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures. Methods: We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis. Results: Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted. Conclusions: The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.
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“While in the above we reviewed COVID-19 related studies for their timely relevance, mobility restrictions had been used and studied in the past, for example to stop the spread of Ebola in Sierra Leone [108], H1N1 influenza [109], etc.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [108]
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Citations
Cites
Automatic Time Series Forecasting: The forecast Package for R
Hyndman, R. J., & Khandakar, Y. (2008) · Journal of Statistical Software · doi:10.18637/jss.v027.i03 · Cited by 3,548 · cited 1×
“We used the auto.arima function in the forecast package in R (Hyndman and Khandakar 30 ) to identify the best-fitting parameters for the periodicity of the time series, and the linear mixed effects lme function in the nlme package in R (Pinheeiro et al. 31 ) to fit the time series model in the…”
MethodIntervention Analysis with Applications to Economic and Environmental Problems
Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10480264 · Cited by 2,399
Applied Spatial Data Analysis with R - Second Edition
Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2008) · Use R! · doi:10.1007/978-1-4614-7618-4 · Cited by 2,065
Multiscale mobility networks and the spatial spreading of infectious diseases
Balcan, D., Vittoria Colizza, A., Gonçalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906910106 · Cited by 1,347 · cited 1×
“8 Specifically for travel bans and border closures during the 2014–15 Ebola epidemic, simulations assuming travel reductions exceeding 80% predicted limited impact on international disease diffusion; 9 joining the call for quantification of previously unmeasured changes in mobility during the…”
BackgroundPercentage Points for a Generalized ESD Many-Outlier Procedure
ROSNER, B. A. (1983) · Technometrics · doi:10.1080/00401706.1983.10487848 · Cited by 1,287 · cited 1×
“The method uses a generalized extreme studentized deviate many-outlier procedure 27 to detect positive and negative deviants of statistically significant magnitude in the presence of periodic (in this application, weekly) and long-term trends.”
MethodTime Series Analysis: With Applications in R
Cryer, J. D., & Chan, K. (2010) · Springer texts in statistics · doi:10.1007/978-0-387-75959-3 · Cited by 682
Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & von Schreeb, J. (2011) · PLoS Medicine · doi:10.1371/journal.pmed.1001083 · Cited by 634
Self-citationPredictability of population displacement after the 2010 Haiti earthquake
Lu, X. B., Bengtsson, L., & Holme, P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203882109 · Cited by 607
Self-citationHuman Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic
Bajardi, P., Poletto, C., Ramasco, J. J., Tizzoni, M., Colizza, V., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016591 · Cited by 495 · cited 1×
“3 The ultimate goals of travel restrictions (e.g. geographical containment and transmission interruption) depend first and fundamentally on the impact of travel restrictions on changes to human travel behaviour.”
BackgroundLessons from the History of Quarantine, from Plague to Influenza A
Tognotti, E. (2013) · Emerging infectious diseases · doi:10.3201/eid1902.120312 · Cited by 460 · cited 1×
“This class of intervention ranges from ancient restrictions of the free association of people with leprosy 1 to maritime quaran-tines in 13th century Venice 2 and international air travel restrictions in the highly interconnected world of the 21st century.”
Background
Flagged first, then by citations
Research works printed but not linked: 4
- Médecins Sans Frontières (MSF), 2014, Ebola: Quarantine Can Undermine Efforts to Curb Epidemic
- National Ebola Reponse Centre (NERC), 2015, Zero Ebola Campaign 3 Days Stay At Home—Operational Overview
- National Ebola Reponse Centre (NERC), 2015, FAQs—National Zero Ebola Campaign
- National Ebola Response Centre (NERC), 2015, Surge Operation Order
Cited by
Sorted by citations
Items 101–140 of 140
Goh, B. H., Yuen, C. W., & Onn, C. C. (2025) · Systems · doi:10.3390/systems13121045 · Cited by 2
Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior
Gökaşar, I., Ozcan, E., & Deveci, M. (2024) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2024.101305 · Cited by 3
Marx, A. J., Poynor, M., Kim, Y.-K., & Oberreiter, L. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9110643 · Cited by 2
“CDR data has also been utilized in applications of epidemics and disease control. investigate Peak et al. (2018) used CDRs to algorithmically investigate the decrease of travel during the Ebola epidemic intervention in Sierra Leone [21]. the decrease of travel during the Ebola epidemic intervention…”
BackgroundMethodSelection and Exploration of Cultural and Creative Tourist Attractions Based on BP Network
Xing, N. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4386357 · Cited by 1
“” Literature [14] compared several major cultural and creative industry development models.”
BackgroundBuilding a Policy-Relevant Research Agenda on Environmental Migration in Africa
Mueller, V. (2022) · International handbooks of population · doi:10.1007/978-3-030-76433-3_8 · Cited by 2
Ascione, C., & Valdano, E. (2025) · Physical Review Research · doi:10.1103/32b8-x9z5 · Cited by 1
Assessing the Resilience of the Current and Future Transportation System
Mocanu, T., Nägele, S. I., & Bergfeld, M. (2026) · Ethics of science and technology assessment/Wissenschaftsethik und Technikfolgenbeurteilung · doi:10.1007/978-3-032-03187-7_15 · Cited by 1
Pullano, G., Bansal, S., Rubrichi, S., & Colizza, V. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014427 · Cited by 3
“…have demonstrated their usefulness for quantifying mobility networks and connectivity patterns [24–27], and they have been applied to a variety of infectious diseases including malaria [25,28], cholera [29,30], schistosomiasis [31], dengue [32], HIV [33,34], 2013–2016 Ebola outbreak [35,36].”
Enhancing Surveillance: New Data, New Technologies, and New Actors
Francis, J. G., & Francis, L. P. (2021) · Public health ethics analysis · doi:10.1007/978-3-030-63928-0_5 · Cited by 1
Classroom teaching of tourism management using multimedia big data analysis
Li, Y., & Zhang, J. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00696-7 · Cited by 1
“Therefore, this quadrant is the core of the image monitoring problem (Peak et al., 2018).”
Shen, B., Tan, W., Cai, H., Guo, J., Jiang, L., & Qin, P. (2021) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe52470.2021.00034 · Cited by 1
“Home quarantines or travel restrictions are frequently exerted in the scale of community, city, or even country [2], reproducing the situations of SARS in 2003 [3] and Ebola in 2015 [4].”
BackgroundThe recovery of long-distance mobility after COVID-19: What can we expect?
Brunel, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99770-6.00025-9 · Cited by 1
A big data szerepe a városi mobilitás kutatásában és fejlesztésében
Váczi, S., Fabula, S., & Nagy, G. (2022) · Modern Geográfia · doi:10.15170/mg.2022.17.04.04 · Cited by 1
Big Data in Migration Research: The Ethical Considerations
Turper, S. (2023) · Istanbul Bilgi University · doi:10.47613/reflektif.2023.139 · Cited by 1
Lee, J., Goh, S., Kim, J. W., Lee, K., & Choi, M. Y. (2021) · Journal of the Korean Physical Society · doi:10.1007/s40042-021-00303-y · Cited by 1
Integrating Social Sciences to Mitigate Against Covid
Paul, R. E. L., Telle, O., & Benkimoun, S. (2021) · Economics, law, and institutions in Asia Pacific · doi:10.1007/978-981-16-5727-6_3 · Cited by 1
“More recently, thousands of individuals were put into quarantine during the 2003 SARS epidemic and home-based quarantine was carried out during the Ebola epidemic.”
BackgroundKey opportunities and challenges for the use of big data in migration research and policyPreprint
Franklinos, L. H. V., Parrish, R., Burns, R., Caflisch, A. C., Mallick, B., Rahman, T., Routsis, V., López, A. S., Tatem, A. J., & Trigwell, R. (2021) · doi:10.14324/111.444/000042.v2 · Cited by 1
Self-citationIs Lockdown Effective Against Fatality of COVID-19?
Matsuda, S. (2020) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2020.28.004729
“Social lockdown and effective vaccine could have drastic impacts on severe viral infections even in the filovirus such as Ebola fever virus and Lassa fever virus, which are important global health issues resulting in significant morbidity and mortality [3,4].”
BackgroundHerrera, M., & Godoy‐Faúndez, Á. (2021) · Future Internet · doi:10.3390/fi13050112 · Cited by 3
de Andrade, L. G. M., Ferreira, G. F., & Junior, H. T. S. (2021) · medRxiv · doi:10.1101/2021.04.13.21255382
“Published data used anonymized public mobile phone data to measure the spread of malaria [3] and Ebola [4].”
BackgroundMethodInfluentialHigh-resolution home location prediction from Twitter activities using consensus deep learning
Ghaffari, M., Srinivasan, A., Liu, X., & Chakraborty, S. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00808-1
“As mentioned above, several applications need high-resolution home location (Jones et al. 2018; Peak et al. 2018; Wesolowski et al. 2015).”
BackgroundInfluentialBorderline microscopic organism and lockdown impacted across the borders—global shakers
Suleman, S., Farooqui, A., Sharma, P., Malhotra, N., Yadav, N., Narang, J., Hasnain, M. S., & Nayak, A. K. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-17641-w
“Travel bans could impede the distribution of medical supplies and the deployment of specialist personnel to handle the epidemic (Peak et al. 2018).”
BackgroundCreative reuses of data for greater value
Bank, W. (2021) · World Bank, Washington, DC eBooks · doi:10.1596/978-1-4648-1600-0_ch4
Innovations in Disease Surveillance and Monitoring
Espey, J., & Dahmm, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-45009-0_16
Prevention Strategy for Covid-19 by Healthy Mobility
Sihotang, W. Y., Siregar, S. D., Siagian, M., Ginting, R., Hartono, H., Wau, H., Sinurat, B., Lolita, E., & Pakpahan, E. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1083/1/012032
“With the same transmission as Covid-19, maintaining social distance is also a strategy echoed by goverment to supress the transmission by reducing the frequency of close contact between individuals [13].”
BackgroundImpacts of COVID-19 on Travel Mode Preferences in Klang Valley
Yunin, N. A. M., Sim, H. J., & Yussof, I. (2022) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2022-34(6)-11
Rashidi, M., Seyedhosseini, S. M., & Naderan, A. (2023) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-023-00347-3
“Each country, according to its economic, social and cultural conditions, imposes certain restrictions, such as travel restrictions, to control the spread of pollution[3, 4].”
BackgroundLockdowns and Selection Pressure: A Modelling StudyPreprint
Thompson, J., & Wattam, S. (2023) · medRxiv · doi:10.1101/2023.06.13.23291336
“While a lockdown was implemented in Mexico in 2009, in response to the swine flu outbreak [4], and another in Sierra Leone in 2015, in response to the Ebola outbreak [5, 17], these lockdowns lasted only 5 and 3 days, respectively.”
MethodAcker, V. V., Choo, S., & Mokhtarian, P. L. (2023) · Advances in transport policy and planning · doi:10.1016/s2543-0009(23)00014-8
Pullano, G., Bansal, S., Rubrichi, S., & Vittoria Colizza, A. (2024) · medRxiv · doi:10.1101/2024.10.11.24315335
“We then used this model to simulate the spread of influenza-like and Ebola-like diseases and evaluated the outcomes of a stochastic SEIR dynamics.”
BackgroundInfluentialTsekeni, D. E., & Vanli, O. A. (2025) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2025.2569354
“This has been studied in the past, including, for example, the 2009 H1N1 pandemic (Bajardi et al., 2011) and the West African Ebola epidemic (Peak et al., 2018).”
Targeted County-Level Interventions Achieve Epidemic Control Without Statewide LockdownsPreprint
Das, H. K., Hu, T., Santillana, M., & Stolerman, L. M. (2025) · medRxiv · doi:10.1101/2025.10.15.25338091
“. grounded movement patterns [26, 37, 38, 39].”
Keating, P., Murray, J., Schenkel, K., Merson, L., & Seale, A. C. (2021) · Figshare · doi:10.6084/m9.figshare.16680235
Keating, P., Murray, J., Schenkel, K., Merson, L., & Seale, A. C. (2021) · Figshare · doi:10.6084/m9.figshare.16680241
Keating, P., Murray, J., Schenkel, K., Merson, L., & Seale, A. C. (2021) · Figshare · doi:10.6084/m9.figshare.16680232
Keating, P., Murray, J., Schenkel, K., Merson, L., & Seale, A. C. (2021) · Figshare · doi:10.6084/m9.figshare.16680229
Keating, P., Murray, J., Schenkel, K., Merson, L., & Seale, A. C. (2021) · Figshare · doi:10.6084/m9.figshare.16680253
Trade Openness and Health Financing in Sub-Saharan Africa
Amakoue, Y. B. (2024) · theses.fr (ABES) · doi:10.70675/0fe780b1za188z4ae4zae0bze3def4b50c66
SAIMSARA (2026) · SAIMSARA Journal · doi:10.62487/saimsara976931fc
Governing AI for Health Emergencies: Ethical Imperatives, Data Justice, and Collaborative Models
Jammal, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-30647-0_15
Metadata
- Authors
- Peak, C. M., Wesolowski, A., zu Erbach-Schoenberg, E., Tatem, A. J., Wetter, E., Lu, X., Power, D., Weidman-Grunewald, E., Ramos, S., Moritz, S., Buckee, C. O., & Bengtsson, L.
- Affiliations
- Harvard University (US)
- Johns Hopkins University (US)
- Flowminder Foundation, Stockholm, Sweden
- Flowminder Foundation (SE)
- Asian Institute of Management (PH)
- Central South University (CN)
- Ericsson (Sweden) (SE)
- Karolinska Institutet (SE)
- Corresponding author
- Buckee, Caroline O
- ORCID
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-06-26
- Volume
- 47
- Issue
- 5
- Pages
- 1562-1570
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC6208277
- OpenAlex
- W2811210701
- Cited by
- 141
- References
- 29
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 21 · 2022: 22 · 2021: 47 · 2020: 24 · 2019: 10
- FWCI
- 25.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (U54GM088558)
- National Research Service Award (T32AI007535–16A)
- Wellcome Trust Sustaining Health (106866/Z/15/Z)
- Swedish Research Council (D0313701)
- Burroughs Wellcome Fund
- Natural Science Foundation of China (71522014)
- National Institutes of Health
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- Ebola virus disease, travel restrictions, human mobility, mobile phone data, call detail records, epidemic containment, anomaly detection, chiefdoms, spatial heterogeneity, disease surveillance, Sierra Leone
- MeSH terms
- Humans, Population Dynamics, Retrospective Studies, Sierra Leone, Travel, Incidence, Infection Control, Hemorrhagic Fever, Ebola, Cell Phone, Epidemics
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