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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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Tominga, A., Silm, S., Orru, K., Vent, K., Klaos, M., Võik, E.-J., & Saluveer, E. (2023) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2023.103887 · Cited by 9
Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax
Liu, Y., Rong, Y., Guo, Z., Chen, N., Xu, T., Tsung, F., & Li, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26678 · Cited by 9
Assessing the impact of human mobility to predict regional excess death in Ecuador
Cuéllar, L., Torres, I., Romero-Severson, E. O., Mahesh, R., Ortega, N., Pungitore, S. A., Ke, R., & Hengartner, N. (2022) · Scientific Reports · doi:10.1038/s41598-021-03926-0 · Cited by 9
“Such data has emerged as a useful tool to measure human mobility and its relationship to the spread of diseases8,9,10 including SARS-COV-28,11,12, malaria13,14 cholera15, measles16, dengue17,18, and Ebola19,20.”
BackgroundAI-Assisted Diplomatic Decision-Making During Crises: Challenges and Opportunities
Pokhriyal, N., & Koebe, T. (2023) · Springer eBooks · doi:10.1007/978-3-032-12201-8_36 · Cited by 15
Key opportunities and challenges for the use of big data in migration research and policy
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) · UCL Open Environment · doi:10.14324/111.444/ucloe.000027 · Cited by 11
“Previous studies have used big data to predict patterns of human movement during natural disasters [8] and track movement in near real-time [9], quantify migration at national scales [10–14], guide and evaluate humanitarian interventions [15] and examine the effects of human movement on disease transmission [16].”
BackgroundSelf-citationExploring the role of information technology in tourism informatics: state of the art
Wei, W., Ding, S., Chai, Y., Sun, J., & Wang, F. (2024) · Asia Pacific Journal of Tourism Research · doi:10.1080/10941665.2024.2358311 · Cited by 7
“In addition to social media, cell phone data can be used to control the fl ow of tourists (Peak et al., 2018), and climate data and the RayMan model can be used to quantify the potential of climate tourism in tourist destinations (Brosy et al., 2014).”
Kajiita, R. M., & Kang’ethe, S. M. (2021) · Perspectives on Global Development and Technology · doi:10.1163/15691497-12341596 · Cited by 7
“Like other previous epidemics, such as HIV and AIDS in Africa and Ebola in West and central Africa (Peak et al. 2018; Mo Ibrahim Foundation 2020; WHO 2020b), the outbreak of COVID-19 exposed the incapacity and unpreparedness of many countries to respond to health disasters (Gilbert et al. 2020;…”
BackgroundArjona, J. O., & Sampériz, J. D. L. O.-L. (2023) · Scientific Reports · doi:10.1038/s41598-023-36108-1 · Cited by 7
“In contrast to previous research that has dealt with other infectious diseases in developing countries, a significant number of COVID-19-related studies have focused on the United States or European countries as study areas, with the main result being the decrease in population movements after the…”
ResultHigh-resolution home location prediction from tweets using deep learning with dynamic structure
Ghaffari, M., Srinivasan, A., & Liu, X. (2019) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3341161.3342956 · Cited by 7
“information, have been considered as alternative sources [1], [2].”
BackgroundYang, L., Wei, C.-Y., Jiang, X., Ye, Q., & Tatano, H. (2022) · International Journal of Disaster Risk Science · doi:10.1007/s13753-022-00393-7 · Cited by 7
“Mobile phone data are usually used to capture population flow (Peak et al. 2018; Yabe et al. 2020).”
MethodEbola and slum dwellers: Community engagement and epidemic response strategies in urban Sierra Leone
Hrdličková, Z., Macarthy, J. M., Conteh, A., Ali, S. H., Blango, V. N., & Sesay, A. B. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17425 · Cited by 6
Čaušević, A. (2023) · Sustainability · doi:10.3390/su151310701 · Cited by 6
Chen, Q., Li, M., Wang, Y., Zhang, L., & Tan, X. (2021) · Disaster Medicine and Public Health Preparedness · doi:10.1017/dmp.2021.20 · Cited by 6
Lockdown and psychological stress in Wuhan, China
Li, M., Wang, W., Zhu, B., Chen, Q., Zhang, Y., Peng, S., Zhang, L., & Tan, X. (2023) · PLoS ONE · doi:10.1371/journal.pone.0274696 · Cited by 5
Ebola incidence and mortality before and during a lockdown: The 2022 epidemic in Uganda
Izudi, J., Komakech, A., Morukileng, J., & Bajunirwe, F. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002702 · Cited by 4
Papageorgiou, G., Skamnia, E., & Εconomou, P. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70016 · Cited by 5
Innovations in Disease Surveillance and Monitoring
Espey, J., & Dahmm, H. (2020) · Springer eBooks · doi:10.1007/978-3-030-05325-3_16-1 · Cited by 4
Construction of Tourism Management Engineering Based on Data Mining Technology
Ma, Z. (2022) · Journal of Electrical and Computer Engineering · doi:10.1155/2022/1982462 · Cited by 4
“Literature [13] believes that the developing tourism e-commerce website will become the real core of the tourism supply chain.”
BackgroundRetractedThe Prospects of Domestic Tourism in Post-Covid Nepal: Connecting the Missing Dots
Silwal, A., Garcia, G. N. G., & Silwal, A. (2022) · Shanti Journal · doi:10.3126/shantij.v1i1.47794 · Cited by 4
“The study further amplifies that travel restrictions are implemented during pandemics (Peak et al., 2018; Muley et al., 2020; Abdullah et al., 2020).”
BackgroundYuan, B., Lee, H., & Nishiura, H. (2021) · Theoretical Biology and Medical Modelling · doi:10.1186/s12976-021-00149-8 · Cited by 4
“Published applications include modeling of malaria [3], rubella [12], dengue [13], and Ebola transmission [14].”
Angelis, M. D., Durastanti, C., Giovannoni, M., & Moretti, L. (2022) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2022.100646 · Cited by 4
Socio-Life Science and the COVID-19 Outbreak
Yano, M., Matsuda, F., Sakuntabhai, A., & Hirota, S. (2021) · Economics, law, and institutions in Asia Pacific · doi:10.1007/978-981-16-5727-6 · Cited by 4
On the predictability of infectious disease outbreaksPreprint
Scarpino, S. V., & Petri, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07317 · Cited by 4
Literature Review and Data Analysis on the Impacts of Prolonged Events on Transit System Ridership
Liu, Y., Osorio, J., & Ouyang, Y. (2021) · doi:10.36501/0197-9191/21-026 · Cited by 4
Kamis, A. A., Ding, Y., Qu, Z., & Zhang, C. (2021) · Applied Sciences · doi:10.3390/app112311227 · Cited by 3
Intelligent Planning of Tourist Routes Based on Cloud Computing and Marching Algorithm
Lu, J. (2022) · Security and Communication Networks · doi:10.1155/2022/8793392 · Cited by 3
“Literature [15] uses a multisource social media fusion method to integrate fragmented tourism information from multiple aspects to recommend routes to users.”
BackgroundRetractedAnalysis of the Covid-19 pandemic on preferences of transport modes
Murat, Y. Ş., & Çakıcı, Z. (2024) · Proceedings of the Institution of Civil Engineers - Transport · doi:10.1680/jtran.23.00105 · Cited by 3
Positive Impacts of Covid-19 Pandemic on Various Aspects
Sharma, A. (2021) · Psychology and Education Journal · doi:10.17762/pae.v58i2.1868 · Cited by 3
“We know from past examinations that movement limitations have exhibited a beneficial outcome in past SARS, Ebola and bubonic plague episodes [13,14,15].”
ResultCovid-19 Outbreak: Current Scenario of Pakistan
Fatima, I. (2023) · Annals of Human and Social Sciences · doi:10.35484/ahss.2023(4-ii)16 · Cited by 3
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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