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Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems: an example from Eastern Province, Zambia

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Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems : an example from Eastern Province, Zambia . / Alderton, Simon; Noble, Jason; Schaten, Kathrin; Wellburn, Susan C.; Atkinson, Peter Michael.

In: PLoS ONE, 30.09.2015.

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@article{8844ecae06a9445c936105485f6d89a0,
title = "Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems: an example from Eastern Province, Zambia ",
abstract = "In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra{\textquoteright}s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.",
author = "Simon Alderton and Jason Noble and Kathrin Schaten and Wellburn, {Susan C.} and Atkinson, {Peter Michael}",
year = "2015",
month = sep,
day = "30",
doi = "10.1371/journal.pone.0139505",
language = "English",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",

}

RIS

TY - JOUR

T1 - Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems

T2 - an example from Eastern Province, Zambia

AU - Alderton, Simon

AU - Noble, Jason

AU - Schaten, Kathrin

AU - Wellburn, Susan C.

AU - Atkinson, Peter Michael

PY - 2015/9/30

Y1 - 2015/9/30

N2 - In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

AB - In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

U2 - 10.1371/journal.pone.0139505

DO - 10.1371/journal.pone.0139505

M3 - Journal article

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

M1 - e0139505

ER -