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J R Soc Interface. 2014 Oct 6;11(99). pii: 20140642. doi: 10.1098/rsif.2014.0642.

Theory and data for simulating fine-scale human movement in an urban environment.

Author information

1
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Entomology and Nematology, University of California, Davis, CA, USA taperkins@nd.edu.
2
Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA Department of Geography, University of Florida, Gainesville, FL, USA.
3
Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
4
Department of Entomology and Nematology, University of California, Davis, CA, USA.
5
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Entomology and Nematology, University of California, Davis, CA, USA.
6
Department of Environmental Sciences, Emory University, Atlanta, GA, USA.
7
United States Naval Medical Research Unit No. 6, Lima, Peru.
8
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
9
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Environmental Sciences, Emory University, Atlanta, GA, USA.
10
Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Geography and Environment, University of Southampton, Southampton, UK Flowminder Foundation, Stockholm, Sweden.

Abstract

Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.

KEYWORDS:

activity space; agent-based model; co-location and contact networks; human mobility; simulation; synthetic population

PMID:
25142528
PMCID:
PMC4233749
DOI:
10.1098/rsif.2014.0642
[Indexed for MEDLINE]
Free PMC Article

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