Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model
Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model
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DOI：
10.1093/cid/ciac568
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发表时间：
2022-07-19
影响因子：
11.8
通讯作者：
Plowe, Christopher, V
中科院分区：
文献类型：
--
作者：
Li, Yao;Stewart, Kathleen;Plowe, Christopher, V
建立多智能体流动模拟模型，对缅甸2个乡镇的村民流动进行模拟，以更好地了解流动如何影响不同职业结构地区疟疾感染暴露风险。背景：为了在更精细的尺度上评估日常旅行与疟疾风险之间的关系，需要更多关于人类运动模式的细节。建立了多智能体移动仿真模型，对缅甸2个乡镇的村民在家庭和工作场所之间的移动进行了仿真。方法基于出行调查问卷的反馈，建立基于agent的模型，模拟日常上下班出行。ABM的关键要素是土地覆盖、旅行时间、旅行方式、职业、疟疾流行率和详细的道路网络。提取并比较了不同职业和疟疾阳性病例访问最多的网络段。来自另一项调查的数据被用来验证模拟。结果不同职业群体的流动特征表明，在某些职业群体之间存在共同的流动模式的同时，也存在某些职业群体特有的流动模式。据估计，森林工人是最具流动性的职业群体，并且与他们在安镇的日常旅行相关的潜在疟疾暴露也最高。在辛古镇，林业工人并不是流动性最大的群体；然而，据估计，他们访问的地区疟疾感染率高于其他职业群体。结论利用ABM模拟不同职业人群的日常出行流动模式。这些空间格局因职业而异。我们的模拟确定了暴露于疟疾风险较高的职业，以及这些暴露更有可能发生的职业。
A multiagent mobility simulation model was built to simulate the movements of villagers in 2 townships in Myanmar to gain a better understanding of how mobility impacts risk of exposure to malaria infection in a region with different occupation structures.Background More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar. Methods An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation. Results Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups. Conclusions Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.