BlueDot, 207 Queens Quay West #820, Toronto, Ontario, Canada.
BlueDot, 207 Queens Quay West #820, Toronto, Ontario, Canada.
Spat Spatiotemporal Epidemiol. 2023 Feb;44:100558. doi: 10.1016/j.sste.2022.100558. Epub 2022 Dec 5.
The Democratic Republic of the Congo's (DRC) 10 known Ebola virus disease (EVD) outbreak occurred between August 1, 2018 and June 25, 2020, and was the largest EVD outbreak in the country's history. During this outbreak, the DRC Ministry of Health initiated traveller health screening at points of control (POC, locations not on the border) and points of entry (POE) to minimize disease translocation via ground and air travel. We sought to develop a model-based approach that could be applied in future outbreaks to inform decisions for optimizing POC and POE placement, and allocation of resources more broadly, to mitigate the risk of disease translocation associated with ground-level population mobility. We applied a parameter-free mobility model, the radiation model, to estimate likelihood of ground travel between selected origin locations (including Beni, DRC) and surrounding population centres, based on population size and drive-time. We then performed a road network route analysis and included estimated population movement results to calculate the proportionate volume of travellers who would move along each road segment; this reflects the proportion of travellers that could be screened at a POC or POE. For Beni, the road segments estimated to have the highest proportion of travellers that could be screened were part of routes into Uganda and Rwanda. Conversely, road segments that were part of routes to other population centres within the DRC were estimated to have relatively lower proportions. We observed a posteriori that, in many instances, our results aligned with locations that were selected for actual POC or POE placement through more time-consuming methods. This study has demonstrated that mobility models and simple spatial techniques can help identify potential locations for health screening at newly placed POC or existing POE during public health emergencies based on expected movement patterns. Importantly, we have provided methods to estimate the proportionate volume of travellers that POC or POE screening measures would assess based on their location. This is critical information in outbreak situations when timely decisions must be made to implement public health interventions that reach the most individuals across a network.
刚果民主共和国(DRC)的 10 次已知埃博拉病毒病(EVD)暴发发生在 2018 年 8 月 1 日至 2020 年 6 月 25 日之间,是该国历史上最大的 EVD 暴发。在此期间,刚果民主共和国卫生部在控制(POC,非边境地区)和入境点(POE)开展旅行者健康筛查,以尽量减少通过地面和空中旅行传播疾病。我们试图开发一种基于模型的方法,可应用于未来的暴发,以优化 POC 和 POE 的位置,并更广泛地分配资源,从而降低与地面人口流动相关的疾病传播风险。我们应用了一种无参数的流动模型——辐射模型,根据人口规模和行驶时间,来估算从选定的起点(包括刚果民主共和国的贝尼)到周围人口中心的地面旅行的可能性。然后,我们进行了道路网络路径分析,并包含了估计的人口流动结果,以计算每个道路段的旅行者比例;这反映了可以在 POC 或 POE 进行筛查的旅行者比例。对于贝尼,据估计,旅行者比例最高的道路段是通往乌干达和卢旺达的路线的一部分。相反,通往刚果民主共和国其他人口中心的道路段估计其比例较低。我们事后观察到,在许多情况下,我们的结果与通过更耗时的方法选择的实际 POC 或 POE 位置相符。这项研究表明,在公共卫生紧急情况下,移动模型和简单的空间技术可以帮助确定新设立的 POC 或现有 POE 进行健康筛查的潜在地点,这是基于预期的流动模式。重要的是,我们提供了根据其位置评估 POC 或 POE 筛查措施的旅行者比例的估计方法。在暴发情况下,必须及时做出决策,实施可覆盖网络中最多人数的公共卫生干预措施,这是关键信息。