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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.


Individual errors for the single agent per village simulation using the H10G25 heuristic and 1 km borehole threshold.The square root of the errors is shown, for ease of interpretation (Produced using Landsat 7 imagery from USGS.)
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pone.0139505.g007: Individual errors for the single agent per village simulation using the H10G25 heuristic and 1 km borehole threshold.The square root of the errors is shown, for ease of interpretation (Produced using Landsat 7 imagery from USGS.)

Mentions: To identify a possible cause for the elongated tail in (Fig 6), the spatial distribution of the error was plotted by household coordinate on an xy grid (Fig 7). Green circles indicate households with very low errors in individual walk times, red circles indicate those with larger errors, and red crosses represent the locations of known boreholes. For this figure, the square root of the error was plotted. The most obvious cluster of large errors is in the area furthest north with the households that lie close to the airport; these are located furthest from known boreholes and river sites. Households around the areas where boreholes are known to be located have smaller errors in general.


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)

Individual errors for the single agent per village simulation using the H10G25 heuristic and 1 km borehole threshold.The square root of the errors is shown, for ease of interpretation (Produced using Landsat 7 imagery from USGS.)
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139505.g007: Individual errors for the single agent per village simulation using the H10G25 heuristic and 1 km borehole threshold.The square root of the errors is shown, for ease of interpretation (Produced using Landsat 7 imagery from USGS.)
Mentions: To identify a possible cause for the elongated tail in (Fig 6), the spatial distribution of the error was plotted by household coordinate on an xy grid (Fig 7). Green circles indicate households with very low errors in individual walk times, red circles indicate those with larger errors, and red crosses represent the locations of known boreholes. For this figure, the square root of the error was plotted. The most obvious cluster of large errors is in the area furthest north with the households that lie close to the airport; these are located furthest from known boreholes and river sites. Households around the areas where boreholes are known to be located have smaller errors in general.

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.