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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 S, Noble J, Schaten K, Welburn SC, Atkinson PM - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Complex System Simulation, School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Southampton, United Kingdom.

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.

No MeSH data available.


Sample section of the land classification map, with forest (green), crop (off-white), and bare land (magenta) areas highlighted.Road and river are represented by gold and blue lines respectively.
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pone.0139505.g003: Sample section of the land classification map, with forest (green), crop (off-white), and bare land (magenta) areas highlighted.Road and river are represented by gold and blue lines respectively.

Mentions: The GPS coordinates obtained for household location were overlain on fine spatial resolution Bing aerial imagery, accessed through the open layers plug-in in QGIS. The previously mentioned 30 m spatial resolution land cover classification was then overlain on this finer spatial resolution imagery, before cross-referencing and editing through the digitizing of river and road detail which was too detailed to be captured at the coarser spatial resolution. The product was a multi-layer input map for the model, comprising spatial information on household, river, road and borehole locations, along with pixels allocated to the classes of bare ground, forest and crop growing fields, at a spatial resolution of 11 m. Fig 3 shows a sample section of the land cover classification layer for the model.


Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia.

Alderton S, Noble J, Schaten K, Welburn SC, Atkinson PM - PLoS ONE (2015)

Sample section of the land classification map, with forest (green), crop (off-white), and bare land (magenta) areas highlighted.Road and river are represented by gold and blue lines respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589342&req=5

pone.0139505.g003: Sample section of the land classification map, with forest (green), crop (off-white), and bare land (magenta) areas highlighted.Road and river are represented by gold and blue lines respectively.
Mentions: The GPS coordinates obtained for household location were overlain on fine spatial resolution Bing aerial imagery, accessed through the open layers plug-in in QGIS. The previously mentioned 30 m spatial resolution land cover classification was then overlain on this finer spatial resolution imagery, before cross-referencing and editing through the digitizing of river and road detail which was too detailed to be captured at the coarser spatial resolution. The product was a multi-layer input map for the model, comprising spatial information on household, river, road and borehole locations, along with pixels allocated to the classes of bare ground, forest and crop growing fields, at a spatial resolution of 11 m. Fig 3 shows a sample section of the land cover classification layer for the model.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Complex System Simulation, School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Southampton, United Kingdom.

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.

No MeSH data available.