What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of Mobility

Author:

Schaber Kathryn L.1,Kobayashi Tamaki1,Hast Marisa1,Searle Kelly M.2,Shields Timothy M.1,Hamapumbu Harry3,Lubinda Jailos4,Thuma Philip E.15,Lupiya James6,Chaponda Mike6,Munyati Shungu7,Gwanzura Lovemore78,Mharakurwa Sungano79,Moss William J.1510,Wesolowski Amy1,_ _

Affiliation:

1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;

2. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota;

3. Macha Research Trust, Choma District, Zambia;

4. Telethon Kids Institute, Malaria Atlas Project, Nedlands, Australia;

5. Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;

6. The Tropical Diseases Research Centre, Ndola, Zambia;

7. Biomedical Research and Training Institute, Harare, Zimbabwe;

8. College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe;

9. College of Health, Agriculture and Natural Sciences, Africa University, Mutare, Zimbabwe;

10. Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

Abstract

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.

Publisher

American Society of Tropical Medicine and Hygiene

Subject

Virology,Infectious Diseases,Parasitology

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