Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.
The Carter Center, One Copenhill, 453 Freedom Parkway, Atlanta, GA, 30307, USA.
Sci Rep. 2020 Mar 6;10(1):4235. doi: 10.1038/s41598-020-61194-w.
Concern is emerging regarding the challenges posed by spatial complexity for modelling and managing the area-wide elimination of parasitic infections. While this has led to calls for applying heterogeneity-based approaches for addressing this complexity, questions related to spatial scale, the discovery of locally-relevant models, and its interaction with options for interrupting parasite transmission remain to be resolved. We used a data-driven modelling framework applied to infection data gathered from different monitoring sites to investigate these questions in the context of understanding the transmission dynamics and efforts to eliminate Simulium neavei- transmitted onchocerciasis, a macroparasitic disease that causes river blindness in Western Uganda and other regions of Africa. We demonstrate that our Bayesian-based data-model assimilation technique is able to discover onchocerciasis models that reflect local transmission conditions reliably. Key management variables such as infection breakpoints and required durations of drug interventions for achieving elimination varied spatially due to site-specific parameter constraining; however, this spatial effect was found to operate at the larger focus level, although intriguingly including vector control overcame this variability. These results show that data-driven modelling based on spatial datasets and model-data fusing methodologies will be critical to identifying both the scale-dependent models and heterogeneity-based options required for supporting the successful elimination of S. neavei-borne onchocerciasis.
人们对空间复杂性给寄生虫病区域消除建模和管理带来的挑战感到担忧。这导致人们呼吁采用基于异质性的方法来解决这种复杂性，但有关空间尺度、发现局部相关模型以及其与寄生虫传播中断选择的相互作用等问题仍有待解决。我们使用数据驱动的建模框架，将其应用于从不同监测点收集的感染数据，以在理解传播动态和消除乌干达西部和非洲其他地区因西氏班氏丝虫传播的河盲症（一种引起河盲症的大型寄生虫病）的努力的背景下，调查这些问题。我们证明，我们的贝叶斯数据模型同化技术能够可靠地发现反映当地传播条件的盘尾丝虫病模型。由于特定地点的参数限制，诸如感染临界点和实现消除所需的药物干预持续时间等关键管理变量在空间上存在差异；然而，发现这种空间效应在较大的重点层面上起作用，尽管有趣的是，包括病媒控制在内，克服了这种可变性。这些结果表明，基于空间数据集和模型数据融合方法的数据驱动建模对于确定支持成功消除西氏班氏丝虫传播的盘尾丝虫病所需的规模相关模型和基于异质性的选择将是至关重要的。
