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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

Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems: an example from Eastern Province, Zambia
Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems: an example from Eastern Province, Zambia
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.
1932-6203
1-18
Alderton, Simon
ec893713-8c3f-465e-8a22-c719744d9f8c
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Schaten, Kathrin
adefd1c5-2fec-4966-ae31-4c865e45fbcb
Welburn, Susan C.
531d82a4-0190-4ff9-a7bb-576ae38fa0b3
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Alderton, Simon
ec893713-8c3f-465e-8a22-c719744d9f8c
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Schaten, Kathrin
adefd1c5-2fec-4966-ae31-4c865e45fbcb
Welburn, Susan C.
531d82a4-0190-4ff9-a7bb-576ae38fa0b3
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Alderton, Simon, Noble, Jason, Schaten, Kathrin, Welburn, Susan C. and Atkinson, Peter M. (2015) Exploiting human resource requirements to infer human movement patterns for use in modelling disease transmission systems: an example from Eastern Province, Zambia. PLoS ONE, 10 (9), 1-18. (doi:10.1371/journal.pone.0139505).

Record type: Article

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'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.

Full text not available from this repository.

More information

Accepted/In Press date: 12 September 2015
Published date: 30 September 2015

Identifiers

Local EPrints ID: 418149
URI: https://eprints.soton.ac.uk/id/eprint/418149
ISSN: 1932-6203
PURE UUID: d764e18a-8405-4ef5-bfea-33fb7fa91df4
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 22 Feb 2018 17:30
Last modified: 20 Jul 2019 01:20

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