- ABSTRACT.
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- Geography was an important factor across all mobility metrics.
- Seasonal differences were only observed in a single site.
- Demographic factors differences were pronounced but largely driven by differences at a single site.
- Composite mobility was shaped by the relationship between site and demographic factors.
- DISCUSSION
- Supplemental Materials
- ACKNOWLEDGMENTS
- REFERENCES
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
  Kramer AM , Pulliam JT , Alexander LW , Park AW , Rohani P , Drake JM , 2016. Spatial spread of the West Africa Ebola epidemic. R Soc Open Sci 3: 160294.
-
2.
  Molodecky NA et al.2021. Quantifying movement patterns and vaccination status of high risk mobile populations in Pakistan and Afghanistan to inform poliovirus risk and vaccination strategy. Vaccine 39: 2124–2132.
-
3.
  Charu V , Zeger S , Gog J , Bjørnstad ON , Kissler S , Simonsen L , Grenfell BT , Viboud C , 2017. Human mobility and the spatial transmission of influenza in the United States. PLOS Comput Biol 13: e1005382.
-
4.
  Lemey P et al.2021. Untangling introductions and persistence in COVID-19 resurgence in Europe. Nature 595: 713–717.
-
5.
  Bharti N , Tatem AJ , Ferrari MJ , Grais RF , Djibo A , Grenfell BT , 2011. Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science 334: 1424–1427.
-
6.
  Wesolowski A , Eagle N , Tatem AJ , Smith DL , Noor AM , Snow RW , Buckee CO , 2012. Quantifying the impact of human mobility on malaria. Science 338: 267–270.
-
7.
  Truscott J , Ferguson NM , 2012. Evaluating the adequacy of gravity models as a description of human mobility for epidemic modelling. PLOS Comput Biol 8: e1002699.
-
8.
  Wesolowski A et al.2014. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Sci Rep 4: 5678.
-
9.
  Kishore N , Taylor AR , Jacob PE , Vembar N , Cohen T , Buckee CO , Menzies NA , 2022. Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. Lancet Digit Health 4: e27–e36.
-
10.
  Meredith HR et al.2021. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 10: https://doi.org/10.7554/eLife.68441.
-
11.
  Wesolowski A , Buckee CO , Pindolia DK , Eagle N , Smith DL , Garcia AJ , Tatem AJ , 2013. The use of census migration data to approximate human movement patterns across temporal scales. PLoS One 8: e52971.
-
12.
  Huang Z , Das A , Qiu Y , Tatem AJ , 2012. Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool. Int J Health Geogr 11: 33.
-
13.
  Buckee CO et al.2020. Aggregated mobility data could help fight COVID-19. Science 368: 145–146.
-
14.
  Wesolowski A , Buckee CO , Engo-Monsen K , Metcalf CJE , 2016. Connecting mobility to infectious diseases: the promise and limits of mobile phone data. J Infect Dis 214: S414–S420.
-
15.
  Grantz KH et al.2020. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 11: 4961.
-
16.
Anderson RM , Anderson B , May RM , 1992. Infectious Diseases of Humans: Dynamics and Control. Oxford, UK: Oxford University Press.
-
17.
  Vazquez-Prokopec GM , Stoddard ST , Paz-Soldan V , Morrison AC , Elder JP , Kochel TJ , Scott TW , Kitron U , 2009. Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. Int J Health Geogr 8: 68.
-
18.
  Searle KM , Lubinda J , Hamapumbu H , Shields TM , Curriero FC , Smith DL , Thuma PE , Moss WJ , 2017. Characterizing and quantifying human movement patterns using GPS data loggers in an area approaching malaria elimination in rural southern Zambia. R Soc Open Sci 4: 170046.
-
19.
  Paz-Soldan VA , Stoddard ST , Vazquez-Prokopec G , Morrison AC , Elder JP , Kitron U , Kochel TJ , Scott TW , 2010. Assessing and maximizing the acceptability of global positioning system device use for studying the role of human movement in dengue virus transmission in Iquitos, Peru. Am J Trop Med Hyg 82: 723–730.
-
20.
  Stoddard ST et al.2013. House-to-house human movement drives dengue virus transmission. Proc Natl Acad Sci USA 110: 994–999.
-
21.
  Vazquez-Prokopec GM et al.2013. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 8: e58802.
-
22.
  Hast M et al.for the Central Africa International Centers of Excellence for Malaria Research , 2019. The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia. Int J Health Geogr 18: 19.
-
23.
  Hast M , Mharakurwa S , Shields TM , Lubinda J , Searle KM , Gwanzura L , Munyati S , Moss WJ , Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border. Preprint (Version 1). Available at: https://doi.org/10.21203/rs.3.rs-1755399/v2. Accessed June 25, 2022.
-
24.
  Moss WJ , Hamapumbu H , Kobayashi T , Shields T , Kamanga A , Clennon J , Mharakurwa S , Thuma PE , Glass G , 2011. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey. Malar J 10: 163.
-
25.
  Harris PA , Taylor R , Thielke R , Payne J , Gonzalez N , Conde JG , 2009. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42: 377–381.
-
26.
  Campello RJGB , Moulavi D , Sander J , 2013. Density-Based Clustering Based on Hierarchical Density Estimates. Berlin, Germany: Springer; pp. 160–172.
-
27.
  Hahsler M , Piekenbrock M , Doran D , 2019. dbscan: fast density-based clustering with R. J Stat Softw 91: 1–30.
-
28.
  Rigby RA , Stasinopoulos DM , 2005. Generalized additive models for location, scale and shape [with discussion]. Appl Stat 54: 507–554.
-
29.
  Venables WN , Ripley BD , 2002. Modern Applied Statistics with S. New York, NY: Springer.
-
30.
 R Development Core Team , 2010. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
-
31.
  Yasuno M , Tonn RJ , 1970. A study of biting habits of Aedes aegypti in Bangkok, Thailand. Bull World Health Organ 43: 319–325.
-
32.
  Chadee DD , 1988. Landing periodicity of the mosquito Aedes aegypti in Trinidad in relation to the timing of insecticidal space-spraying. Med Vet Entomol 2: 189–192.
-
33.
  Moiroux N , Gomez MB , Pennetier C , Elanga E , Djènontin A , Chandre F , Djègbé I , Guis H , Corbel V , 2012. Changes in Anopheles funestus biting behavior following universal coverage of long-lasting insecticidal nets in Benin. J Infect Dis 206: 1622–1629.
-
34.
  Carrasco D , Lefèvre T , Moiroux N , Pennetier C , Chandre F , Cohuet A , 2019. Behavioural adaptations of mosquito vectors to insecticide control. Curr Opin Insect Sci 34: 48–54.
-
35.
  Sougoufara S , Diédhiou SM , Doucouré S , Diagne N , Sembène PM , Harry M , Trape J-F , Sokhna C , Ndiath MO , 2014. Biting by Anopheles funestus in broad daylight after use of long-lasting insecticidal nets: a new challenge to malaria elimination. Malar J 13: 125.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 2043 | 2043 | 10 |
| Full Text Views | 274 | 274 | 18 |
| PDF Downloads | 137 | 137 | 10 |
What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of Mobility
ABSTRACT.
Human movement drives spatial transmission patterns of infectious diseases. Population-level mobility patterns are often quantified using aggregated data sets, such as census migration surveys or mobile phone data. These data are often unable to quantify individual-level travel patterns and lack the information needed to discern how mobility varies by demographic groups. Individual-level datasets can capture additional, more precise, aspects of mobility that may impact disease risk or transmission patterns and determine how mobility differs across cohorts; however, these data are rare, particularly in locations such as sub-Saharan Africa. Using detailed GPS logger data collected from three sites in southern Africa, we explore metrics of mobility such as percent time spent outside home, number of locations visited, distance of locations, and time spent at locations to determine whether they vary by demographic, geographic, or temporal factors. We further create a composite mobility score to identify how well aggregated summary measures would capture the full extent of mobility patterns. Although sites had significant differences in all mobility metrics, no site had the highest mobility for every metric, a distinction that was not captured by the composite mobility score. Further, the effects of sex, age, and season on mobility were all dependent on site. No factor significantly influenced the number of trips to locations, a common way to aggregate datasets. When collecting and analyzing human mobility data, it is difficult to account for all the nuances; however, these analyses can help determine which metrics are most helpful and what underlying differences may be present.
- Supplemental Materials (PDF 1154 KB)
Author Notes
Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Financial support: This work was supported by funds from the National Institutes of Health awarded to the Southern and Central Africa International Centers of Excellence in Malaria Research (U19AI089680), the Bloomberg Philanthropies, and the Johns Hopkins Malaria Research Institute. K. L. S. and A. W. are supported by the U.S. National Institutes of Health through the National Library of Medicine (DP2LM013102). A. W. is also supported by the National Institute of Allergy and Infectious Diseases (1R01A1160780-01) and a Career Award at the Scientific Interface from the Burroughs Welcome Fund.
Authors’ addresses: Kathryn L. Schaber, Tamaki Kobayashi, Marisa Hast, Timothy M. Shields, and Amy Wesolowski, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mails: kathrynschaber@gmail.com, tkobaya2@jhu.edu, marisahast@gmail.com, tshields@jhu.edu, and awesolowski@jhu.edu. Kelly M. Searle, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, E-mail: ksearle@umn.edu. Harry Hamapumbu, Macha Research Trust, Choma District, Zambia, E-mail: harry.hamapumbu@macharesearch.org. Jailos Lubinda, Telethon Kids Institute, Malaria Atlas Project, Nedlands, Australia, E-mail: jailos.lubinda@telethonkids.org.au. Philip E. Thurma, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD and Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: phil.thuma@macharesearch.org. James Lupiya and Mike Chaponda, The Tropical Diseases Research Centre, Ndola, Zambia, E-mails: jamlupiya@gmail.com and mikechaponda@yahoo.com. Shungu Munyati, Biomedical Research and Training Institute, Harare, Zimbabwe, E-mail: smunyati@brti.co.zw. Lovemore Gwanzura, Sungano Mharakurwa, Biomedical Research and Training Institute, Harare, Zimbabwe, and College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe, E-mails: gwanzura@mweb.co.zw and mharakurwas@africau.edu. William J. Moss, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: wmoss1@jhu.edu.