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Quantifying the Impact of Human Mobility on Malaria
Open accessAbstract
Mobile Phone “Hot Spots”: An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267 ) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital.
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“Yet, human mobility plays an important role in the transmission of vector‐borne diseases, as population movements lead to continuous pathogen reintroductions (Buckee et al., 2013; Wesolowski et al., 2012).”
From Spatial Optimization Methods for Malaria Risk Mapping in Sub‐Saharan African Cities Using Demographic and Health Surveys (2023) · cites this paper as Wesolowski et al., 2012
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“Pathogen transmission by mosquitoes has been characterized as being highly local and focal, with transmission foci and hotspots.75”
From Recasting the theory of mosquito-borne pathogen transmission dynamics and control (2014) · cites this paper as [75]
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“Recent evidence suggests that human social networks are just as important for transmission within cities as mosquito ecology,108 and that movement networks are a critical element of transmission within and among countries.109,110”
From Recasting the theory of mosquito-borne pathogen transmission dynamics and control (2014) · cites this paper as [110]
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“Apart from the above infectious diseases, mobility has been shown to play a major role in spreading other diseases like malaria [111]—people travelling to different cities carry the parasite and infect the mosquitoes there and in turn the population.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [111]
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Citations
Cites
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Strategies for containing an emerging influenza pandemic in Southeast Asia
Ferguson, N. M., Cummings, D. A. T., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A. C., Iamsirithaworn, S., & Burke, D. S. (2005) · Nature · doi:10.1038/nature04017 · Cited by 1,820
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
Containing Pandemic Influenza at the Source
Longini, I. M., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D. A. T., & Halloran, M. E. (2005) · Science · doi:10.1126/science.1115717 · Cited by 1,177
A new world malaria map: Plasmodium falciparum endemicity in 2010
Gething, P. W., Patil, A. P., Smith, D. L., Guerra, C. A., Elyazar, I., Johnston, G. L., Tatem, A. J., & Hay, S. I. (2011) · Malaria Journal · doi:10.1186/1475-2875-10-378 · Cited by 1,169
Self-citationPopulation Distribution, Settlement Patterns and Accessibility across Africa in 2010
Linard, C., Gilbert, M., Snow, R. W., Noor, A. M., & Tatem, A. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031743 · Cited by 595
Self-citationThe role of human movement in the transmission of vector-borne pathogens
Stoddard, S. T., Morrison, A. C., Vazquez-Prokopec, G. M., Paz Soldan, V., Kochel, T. J., Kitron, U., Elder, J. P., & Scott, T. W. (2009) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0000481 · Cited by 573
Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya
Bejon, P., Williams, T. N., Liljander, A., Noor, A. M., Wambua, J., Ogada, E. A., Olotu, A., Osier, F. H. A., Hay, S. I., Färnert, A., et al. (2010) · PLoS Medicine · doi:10.1371/journal.pmed.1000304 · Cited by 269
Self-citationDye, C., & Hasibeder, G. (1986) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1016/0035-9203(86)90199-9 · Cited by 253
Research works printed but not linked: 1
- B. Rapuoda P. Achola Study on malaria and its vectors in Nairobi: A review of the distribution of the vectors and the prevalence of the disease. Proceedings of the KEMRI/KETRI Fifth Annual Medical Scientific Conference Nairobi Kenya 1984 (Kenya Medical Research Institute and Kenya Trypanosomiasis Research Institute Nairobi 1984) pp. 115–119.
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Althoff, T., Sosič, R., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017) · Nature · doi:10.1038/nature23018 · Cited by 1,000
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Wang, Z., Bauch, C. T., Bhattacharyya, S., d’Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 973
Dynamic population mapping using mobile phone data
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., Blondel, V. D., & Tatem, A. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408439111 · Cited by 893
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Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020) · Nature · doi:10.1038/s41586-020-2284-y · Cited by 835
Mobile Crowd Sensing and Computing
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015) · ACM Computing Surveys · doi:10.1145/2794400 · Cited by 723
Modeling infectious disease dynamics in the complex landscape of global health
Heesterbeek, H., Anderson, R. M., Andreasen, V., Bansal, S., Angelis, D. D., Dye, C., Eames, K. T. D., Edmunds, W. J., Frost, S. D. W., Funk, S., et al. (2015) · Science · doi:10.1126/science.aaa4339 · Cited by 721
A survey of results on mobile phone datasets analysis
Blondel, V. D., Decuyper, A., & Krings, G. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0046-0 · Cited by 679
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle
Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., De Nadai, M., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc0764 · Cited by 631
Unique in the shopping mall: On the reidentifiability of credit card metadata
de Montjoye, Y.-A., Radaelli, L., Singh, V. K., & Pentland, A. “. (2015) · Science · doi:10.1126/science.1256297 · Cited by 570
A multi-source dataset of urban life in the city of Milan and the Province of Trentino
Barlacchi, G., Nadai, M. D., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., & Lepri, B. (2015) · Scientific Data · doi:10.1038/sdata.2015.55 · Cited by 583
Understanding Human Mobility from Twitter
Jurdak, R., Zhao, K., Liu, J., Jaoude, M. A., Cameron, M. A., & Newth, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131469 · Cited by 554
Emergence and potential for spread of Chikungunya virus in Brazil
Nunes, M. R. T., Faria, N. R., de Vasconcelos, J. M., Golding, N., Kraemer, M. U. G., de Oliveira, L. F., Azevedo, R. D. S. D. S., da Silva, D. E. A., da Silva, E. V. P., da Silva, S. P., et al. (2015) · BMC Medicine · doi:10.1186/s12916-015-0348-x · Cited by 533
Impact of human mobility on the emergence of dengue epidemics in Pakistan
Wesolowski, A., Qureshi, T., Boni, M. F., Sundsøy, P., Johansson, M. A., Rasheed, S. B., Engø‐Monsen, K., & Buckee, C. O. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1504964112 · Cited by 497
Self-citationKwan, M. (2013) · Annals of the Association of American Geographers · doi:10.1080/00045608.2013.792177 · Cited by 474
Mapping the zoonotic niche of Ebola virus disease in Africa
Pigott, D. M., Golding, N., Mylne, A., Huang, Z., Henry, A. J., Weiss, D. J., Brady, O. J., Kraemer, M. U. G., Smith, D. L., Moyes, C. L., et al. (2014) · eLife · doi:10.7554/elife.04395 · Cited by 475
Self-citationThe universal visitation law of human mobility
Schläpfer, M., Dong, L., O’Keeffe, K. P., Santi, P., Szell, M., Salat, H., Anklesaria, S., Vazifeh, M. M., Ratti, C., & West, G. B. (2021) · Nature · doi:10.1038/s41586-021-03480-9 · Cited by 449
On the Use of Human Mobility Proxies for Modeling Epidemics
Tizzoni, M., Bajardi, P., Decuyper, A., King, G. K. K., Schneider, C., Blondel, V. D., Smoreda, Z., González, M. C., & Vittoria Colizza, A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003716 · Cited by 404
The path most traveled: Travel demand estimation using big data resources
Toole, J. L., Çolak, S., Sturt, B., Alexander, L. P., Evsukoff, A. G., & González, M. C. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.04.022 · Cited by 418
The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology
Grantz, K. H., Meredith, H. R., Cummings, D. A. T., Metcalf, C. J. E., Grenfell, B. T., Giles, J. R., Mehta, S. H., Solomon, S. S., Labrique, A. B., Kishore, N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18190-5 · Cited by 425
Self-citationUncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data
Liu, Y., Sui, Z., Kang, C., & Gao, Y. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086026 · Cited by 393
Building a tuberculosis-free world: The Lancet Commission on tuberculosis
Reid, M. J. A., Arinaminpathy, N., Bloom, A., Bloom, B. R., Boehme, C. C., Chaisson, R. E., Chin, D. P., Churchyard, G., Cox, H. S., Diţiu, L., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30024-8 · Cited by 382
Social Media– and Internet-Based Disease Surveillance for Public Health
Aiello, A. E., Renson, A., & Zivich, P. N. (2020) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094402 · Cited by 363
Targeting Asymptomatic Malaria Infections: Active Surveillance in Control and Elimination
Sturrock, H. J. W., Hsiang, M. S., Cohen, J. M., Smith, D. L., Greenhouse, B., Bousema, T., & Gosling, R. D. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001467 · Cited by 341
Self-citationData ex Machina: Introduction to Big Data
Lazer, D. M. J., & Radford, J. P. (2017) · Annual Review of Sociology · doi:10.1146/annurev-soc-060116-053457 · Cited by 323
Zhou, Y., Xu, R., Hu, D., Yue, Y., Li, Q., & Xia, J. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30165-5 · Cited by 285
Using Mobile Phone Data to Predict the Spatial Spread of Cholera
Bengtsson, L., Gaudart, J., Lu, X., Moore, S., Wetter, E., Sallah, K., Rebaudet, S., & Piarroux, R. (2015) · Scientific Reports · doi:10.1038/srep08923 · Cited by 281
Kazdin, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993647.010 · Cited by 279
A Survey on Trajectory Data Management, Analytics, and Learning
Wang, S., Bao, Z., Culpepper, J. S., & Cong, G. (2021) · ACM Computing Surveys · doi:10.1145/3440207 · Cited by 295
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de Montjoye, Y.-A., Shmueli, E., Wang, S. S., & Pentland, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098790 · Cited by 282
Banks, N., Paini, D. R., Bayliss, K. L., & Hodda, M. (2014) · Ecology Letters · doi:10.1111/ele.12397 · Cited by 263
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Naboulsi, D., Fiore, M., Ribot, S., & Stanica, R. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2491361 · Cited by 277
Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2663254 · Cited by 259
Universal model of individual and population mobility on diverse spatial scales
Yan, X.-Y., Wang, W.-X., Gao, Z.-Y., & Lai, Y. (2017) · Nature Communications · doi:10.1038/s41467-017-01892-8 · Cited by 247
The impact of biases in mobile phone ownership on estimates of human mobility
Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., & Buckee, C. O. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0986 · Cited by 239
Self-citationVazquez‐Prokopec, G. M., Bisanzio, D., Stoddard, S. T., Paz‐Soldán, V. A., Morrison, A. C., Elder, J. P., Ramirez-Paredes, J., Halsey, E. S., Kochel, T. J., Scott, T. W., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058802 · Cited by 252
Twitter reveals human mobility dynamics during the COVID-19 pandemic
Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0241957 · Cited by 242
Research Design in Clinical Psychology
Kazdin, A. E. (2021) · doi:10.1017/9781108993647 · Cited by 240
Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic
Yabe, T., Tsubouchi, K., Fujiwara, N., Wada, T., Sekimoto, Y., & Ukkusuri, S. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-75033-5 · Cited by 236
Spatial Turn in Health Research
Richardson, D. B., Volkow, N. D., Kwan, M., Kaplan, R. M., Goodchild, M. F., & Croyle, R. T. (2013) · Science · doi:10.1126/science.1232257 · Cited by 231
Emerging Network-Based Tools in Movement Ecology
Jacoby, D. M. P., & Freeman, R. (2016) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2016.01.011 · Cited by 219
Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.10.004 · Cited by 229
Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data
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Self-citationA Survey of Mobile Crowdsensing Techniques
Liu, J., Shen, H., Narman, H. S., Chung, W., & Lin, Z. (2018) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3185504 · Cited by 209
Wang, Q., & Taylor, J. E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147299 · Cited by 192
Lu, X. B., Wrathall, D. J., Sundsøy, P., Nadiruzzaman, M., Wetter, E. W., Iqbal, A., Qureshi, T., Tatem, A. J., Canright, G., Engø‐Monsen, K., et al. (2016) · Global Environmental Change · doi:10.1016/j.gloenvcha.2016.02.002 · Cited by 194
Self-citationChallenges and opportunities in controlling mosquito-borne infections
Ferguson, N. M. (2018) · Nature · doi:10.1038/s41586-018-0318-5 · Cited by 188
Kuchler, T., Russel, D., & Stroebel, J. (2021) · Journal of Urban Economics · doi:10.1016/j.jue.2020.103314 · Cited by 182
Wilson, R., zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., Guthrie, S., Chamberlain, H. R., Brooks, C., Hughes, C., et al. (2016) · PLoS Currents · doi:10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c · Cited by 180
Self-citationMobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks
Finger, F., Genolet, T., Mari, L., de Magny, G. C., Manga, N. M., Rinaldo, A., & Bertuzzo, E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1522305113 · Cited by 177
Tatem, A. J., Huang, Z., Narib, C., Kumar, U., Kandula, D., Pindolia, D. K., Smith, D. L., Cohen, J. M., Graupe, B., Uusiku, P., et al. (2014) · Malaria Journal · doi:10.1186/1475-2875-13-52 · Cited by 169
Self-citationUrban Mobility and Activity Space
Cagney, K. A., Cornwell, E. Y., Goldman, A. W., & Cai, L. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054848 · Cited by 184
Commentary: Containing the Ebola Outbreak - the Potential and Challenge of Mobile Network Data
Wesolowski, A., Buckee, C. O., Bengtsson, L., Wetter, E. W., Lu, X. B., & Tatem, A. J. (2014) · PLoS Currents · doi:10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e · Cited by 169
Self-citationAnalyzing large-scale human mobility data: a survey of machine learning methods and applications
Toch, E., Lerner, B., Ben‐Zion, E., & Ben‐Gal, I. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1186-x · Cited by 163
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Pindolia, D. K., Garcia, A. J., Wesolowski, A., Smith, D. L., Buckee, C. O., Noor, A. M., Snow, R. W., & Tatem, A. J. (2012) · Malaria Journal · doi:10.1186/1475-2875-11-205 · Cited by 165
Self-citationBartlow, A. W., Manore, C. A., Xu, C., Kaufeld, K., Valle, S. Y. D., Ziemann, A., Fairchild, G. C., & Fair, J. M. (2019) · Veterinary Sciences · doi:10.3390/vetsci6020040 · Cited by 169
Mobile phone location data for disasters: A review from natural hazards and epidemics
Yabe, T., Jones, N. K. W., Rao, P. S. C., González, M. C., & Ukkusuri, S. V. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101777 · Cited by 164
City-Scale Dark Fiber DAS Measurements of Infrastructure Use During the COVID-19 Pandemic
Lindsey, N. J., Yuan, S., Lellouch, A., Gualtieri, L., Lecocq, T., & Biondi, B. (2020) · Geophysical Research Letters · doi:10.1029/2020gl089931 · Cited by 162
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Wang, Q., & Taylor, J. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112608 · Cited by 161
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Rosenstock, T. S., Dawson, I. K., Aynekulu, E., Chomba, S., Degrande, A., Fornace, K., Jamnadass, R., Kimaro, A. A., Kindt, R., Lamanna, C. A., et al. (2019) · One Earth · doi:10.1016/j.oneear.2019.10.017 · Cited by 185
Toward data-driven, dynamical complex systems approaches to disaster resilience
Yabe, T., Rao, P. S. C., Ukkusuri, S. V., & Cutter, S. L. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111997119 · Cited by 158
Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data
Ruktanonchai, N., DeLeenheer, P., Tatem, A. J., Alegana, V. A., Caughlin, T. T., zu Erbach-Schoenberg, E., Lourenço, C., Ruktanonchai, C. W., & Smith, D. L. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004846 · Cited by 152
Self-citationKuchler, T., Russel, D., & Stroebel, J. (2020) · National Bureau of Economic Research · doi:10.3386/w26990 · Cited by 155
Kwan, M. (2014) · Springer eBooks · doi:10.1007/978-94-017-9205-9_4 · Cited by 151
Ethical machines: The human-centric use of artificial intelligence
Lepri, B., Oliver, N., & Pentland, A. (2021) · iScience · doi:10.1016/j.isci.2021.102249 · Cited by 149
Kass‐Hout, T., & Alhinnawi, H. (2013) · British Medical Bulletin · doi:10.1093/bmb/ldt028 · Cited by 148
Wesolowski, A., Stresman, G. H., Eagle, N., Stevenson, J. C., Owaga, C., Marube, E., Bousema, T., DRAKELEY, C. J., Cox, J., & Buckee, C. O. (2014) · Scientific Reports · doi:10.1038/srep05678 · Cited by 140
Self-citationMobile phones and malaria: Modeling human and parasite travel
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Self-citationHuang, B., Wang, J., Jixuan Cai, 蔡., Yao, S., Chan, P. K. S., Tam, T., Hong, Y., Ruktanonchai, C. W., Carioli, A., Floyd, J., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01063-2 · Cited by 141
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Self-citationXu, F., Tu, Z., Li, Y., Zhang, P., Fu, X., & Jin, D. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052620 · Cited by 144
Guyant, P., Canavati, S. E., Nguon, C., Ly, P., Whittaker, M. A., Roca‐Feltrer, A., & Yeung, S. (2015) · Malaria Journal · doi:10.1186/s12936-015-0773-5 · Cited by 120
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Pybus, O. G., Tatem, A. J., & Lemey, P. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2014.2878 · Cited by 128
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Self-citationUnderstanding post-disaster population recovery patterns
Yabe, T., Tsubouchi, K., Fujiwara, N., Sekimoto, Y., & Ukkusuri, S. V. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0532 · Cited by 130
Taylor, A. R., Schaffner, S. F., Cerqueira, G. C., Nkhoma, S. C., Anderson, T. J. C., Sriprawat, K., Phyo, A. P., Nosten, F. H., Neafsey, D. E., & Buckee, C. O. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007065 · Cited by 116
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“mobile phone data across Kenya highlighted the role of human mobility in malaria transmission; these movement patterns are largely driven by trade and connectivity of different land use types [48].”
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Gao, J. G., Zhang, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.05.002 · Cited by 122
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Theron, G., Jenkins, H. E., Cobelens, F. G. J., Abubakar, I., Khan, A. J., Cohen, T., & Dowdy, D. W. (2015) · The Lancet · doi:10.1016/s0140-6736(15)00321-9 · Cited by 115
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Metadata
- Authors
- Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O.
- Affiliations
- Carnegie Mellon University (US)
- Northeastern University (US)
- National Institutes of Health (US)
- Kenya Medical Research Institute (KE)
- Harvard University (US)
- Corresponding author
- Buckee, Caroline O.
- ORCID
- Venue
- Science
- Published
- 2012-10-12
- Volume
- 338
- Issue
- 6104
- Pages
- 267-270
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3675794
- OpenAlex
- W1994682066
- Cited by
- 992
- References
- 26
- Citations by year
- 2026: 36 · 2025: 55 · 2024: 43 · 2023: 50 · 2022: 79 · 2021: 144 · 2020: 129 · 2019: 81 · 2018: 77 · 2017: 58 · 2016: 74 · 2015: 69 · 2014: 58 · 2013: 36 · 2012: 3
- FWCI
- 50.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Funders
- Wellcome Trust (095127, 092654, 079080)
- National Institute of General Medical Sciences (U54GM088558)
- National Institute of Allergy and Infectious Diseases (U19AI089674)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Travel-related health issues
- Keywords
- malaria, human mobility, mobile phone data, malaria transmission, malaria risk mapping, Nairobi, local transmission, malaria control
- MeSH terms
- Animals, Communicable Disease Control, Humans, Kenya, Culicidae, Plasmodium falciparum, Travel, Prevalence, Malaria, Falciparum, Cell Phone
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