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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

机译：开发人力资源需求以推断人类运动模式以用于疾病传播系统建模：来自赞比亚东部省的一个例子

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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.
机译：在这项研究中，基于人口分布，土地覆盖和景观资源的常用地理空间数据集，开发了一种基于主体的模型（ABM）以生成人在农村和农村之间水资源与水之间的移动路线。 ABM是一种面向对象的计算方法，用于对系统进行建模，其重点是自治代理的交互作用，旨在评估这些代理及其交互对整个系统的影响。在给定区域地形数据的情况下，实施了A *寻路算法以产生步行路线。 A *是Dijkstra算法的扩展，通过使用启发式算法提高了时间性能。在此示例中，有可能在横跨赞比亚卢安瓜谷的75公里长的研究样带中，将所有村庄的日常活动运动模式归因于水资源，并且模拟的人类运动在统计上类似于对前往该村庄的时间的经验观察。水资源（卡方，置信区间为95％）。这表明可以生成有关人类运动的真实数据，而无需进行通常通过例如GPS或回顾性或实时日记实现的昂贵测量。该方法可在不同地理位置之间转换，该产品可用于深入了解人类运动模式，因此可用于许多与人类暴露相关的应用，特别是在农村地区的流行病学研究中，该地区的疾病景观具有空间异质性以及个体的时空接近性，可以在疾病传播中发挥关键作用。

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