Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness.
Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness.
复制标题
数据驱动的建模和空间复杂性支持基于异质性的综合管理，以消除尼氏蝽传播的河盲症。
DOI：
10.1038/s41598-020-61194-w
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发表时间：
2020
影响因子：
4.6
通讯作者：
Richards,FrankO
中科院分区：
文献类型：
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作者：
Michael,Edwin;Smith,MorganE;Singh,BrajendraK;Katabarwa,MosesN;Byamukama,Edson;Habomugisha,Peace;Lakwo,Thomson;Tukahebwa,Edridah;Richards,FrankO
空间复杂性对在整个地区消除寄生虫感染的建模和管理构成了挑战，这一点正在引起人们的关切。虽然这导致了呼吁应用异质性为基础的方法来解决这种复杂性，与空间尺度，发现当地相关的模型，其相互作用的选择中断寄生虫传播的问题仍然有待解决。我们使用了一个数据驱动的建模框架，应用于从不同监测点收集的感染数据，以了解传播动力学和消除Simuelneavei传播的盘尾丝虫病的努力，这是一种在乌干达西部和非洲其他地区导致河盲症的大型寄生虫病。我们证明，我们的贝叶斯数据模型同化技术是能够发现的盘尾丝虫病模型，反映当地的传播条件可靠。关键的管理变量，如感染断点和所需的持续时间的药物干预，以实现消除空间变化，由于网站特定的参数限制，然而，这种空间效应被发现在更大的重点水平，虽然有趣的是，包括载体控制克服了这种变异性。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S所需的尺度依赖模型和基于异质性的选项至关重要。尼韦传播的盘尾丝虫病
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 eliminateSimulium 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 ofS. neavei-borne onchocerciasis.