New spatially explicit approaches for estimating malaria parasite migration

这项研究提高了估计和绘制疟疾寄生虫迁移路径的能力。它将通过考虑寄生虫基因组数据中存在的空间模式以及与基因流动相关的景观和建筑环境特征的空间属性来做到这一点，例如，用于阻碍或促进寄生虫迁移的特征。研究人员考虑寄生虫基因组采样位置的空间分布如何影响估计的迁移模式，并确定即使在采样位置分布不规律的情况下，绘制的迁移路径是否仍然有意义。这项研究为如何将当地已知的移民障碍以及其他相关的自然或建筑环境特征纳入绘制的移民估计提供了见解。随着公共卫生官员努力减少并最终消除疟疾，当地关于疟疾风险驱动因素的信息对于优先安排资源和优化控制和消除战略非常重要。这项研究将使用计算方法进行，以产生估计的有效迁移面。这些方法使用马尔科夫链蒙特卡罗模拟来估计迁徙，并创建迁徙等值线，这些等值线可以绘制成地图，以显示相对较高或较低的基因流动区域。这项研究将整合当地的空间特征，以改进对基因流动的估计，并考虑到可能的障碍，从而减少目前寄生虫迁徙地图中常见的空间不确定性。将进行一系列分析，以确定寄生虫迁移面估计对基因组数据样本位置的不同空间分布的稳健性。预计这项研究不仅将对负责消除恶性疟原虫和间日疟原虫的当地公共卫生官员产生深远影响，还将改变更广泛的研究人员在当地范围内为其他种类物种提供寄生虫迁移估计的能力，并为研究寄生虫迁移提供新的计算工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research improves the ability to estimate and map malaria parasite migration pathways. It will do so by accounting for the spatial patterns present in parasite genomic data as well as the spatial properties of features of the landscape and built environment that are relevant for gene flow, for example, features that serve to either impede or enable parasite migration. The investigators consider how the spatial distribution of parasite genomic sampling locations impacts estimated migration patterns and determine whether the mapped migration paths are still meaningful even when there is an irregular distribution of sampling locations. The research provides insights on how to include locally known migration barriers as well as other relevant natural or built environmental features into mapped migration estimates. As public health officials work toward reducing and eventually eliminating malaria, local information about factors driving malaria risk is important for prioritizing resources and optimizing control and elimination strategies. The research will be undertaken using computational methods that generate estimated effective migration surfaces. These methods use Markov chain Monte Carlo simulations to estimate migration and create migration contours that can be mapped to show relative high or low areas of gene flow. This research will integrate local spatial features to improve gene flow estimates and account for possible barriers thereby reducing spatial uncertainty that is commonplace in current maps of parasite migration. A set of analyses will be undertaken to determine the robustness of parasite migration surface estimates to varying spatial distributions of genomic data sample locations. This research is expected to not only have a profound impact for local public health officials tasked with eliminating P. falciparum and P. vivax malaria but will transform the ability of a broader group of researchers to produce parasite migration estimates for other kinds of species at local scales and provide new computational tools for studying parasite migration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.