Department of Geographical Sciences, Center for Geospatial Information Science, University of Maryland, College Park, Maryland, USA.
Department of Medical Research, Ministry of Health and Sports, Yangon, Myanmar.
Clin Infect Dis. 2023 Feb 8;76(3):e867-e874. doi: 10.1093/cid/ciac568.
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.
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.
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.
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.
为了在更精细的尺度上评估日常出行与疟疾风险之间的关系，需要更详细的人类活动模式信息。我们构建了一个多主体移动模拟模型，以模拟缅甸两个镇的村民在家庭和工作场所之间的移动。
基于出行调查的响应，我们构建了一个基于主体的模型（ABM）来模拟每日往返工作的出行。ABM 的关键要素包括土地覆盖、出行时间、出行方式、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的最常访问网络段。使用单独的调查数据来验证模拟。
不同职业群体的流动性特征表明，虽然某些模式在某些群体中是共享的，但也有一些模式是特定于职业群体的。林业工人被估计是最具流动性的职业群体，他们在安镇的日常出行中面临着最高的潜在疟疾暴露风险。在新古镇，林业工人不是最具流动性的群体；然而，他们被估计会访问疟疾感染率高于其他职业群体的地区。
使用 ABM 模拟日常出行可以生成不同职业群体的移动模式。这些空间模式因职业而异。我们的模拟确定了职业群体面临更高的疟疾暴露风险，以及这些暴露更可能发生的地点。
