Pyrros A. Telionis, PhD, is a postdoctoral research assistant, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, and Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA. Patrick Corbett is an undergraduate research assistant; Srinivasan Venkatramanan, PhD, is a Research Scientist; and Bryan Lewis, PhD, is a Research Associate Professor; all in Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA.
Health Secur. 2020 Jan/Feb;18(1):1-15. doi: 10.1089/hs.2019.0101.
When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security.
在时间紧迫的情况下，疫情调查人员通常会使用同质混合模型来对数据匮乏地区的传染病进行建模。但是，最近的一些疫情爆发，如 2014 年西非的埃博拉疫情，已经表明在城市化和连通性不断增强的时代，这种方法存在局限性。疫情检测和预测建模都依赖于对人类和疾病流动性的现实估计，但这些数据很难及时获得。当涉及到资源匮乏国家的新出现的疫情时，这一点尤其如此。带有现实人口流动估计值的加权旅行网络通常是专有的、昂贵的或不存在的。在这里，我们提出了一种从开源数据快速生成移动性模型的方法。作为一个例子，我们使用道路和河流网络数据以及人口估计值，在刚果民主共和国（DRC）的卫生区之间构建了一个现实的人类移动模型。然后，我们使用这些移动性数据拟合一个传染病模型，以分析来自刚果民主共和国北基伍地区最近埃博拉疫情的真实监测数据，以说明生成的移动性估计值的潜在用途。除了提供快速风险估计的方法外，这种方法还结合了新技术，可以合并不同的 GIS 数据集，然后可以使用这些数据集来解决与公共卫生和全球卫生安全相关的问题。
