Division of Computing Science and Mathematics, University of Stirling , Stirling FK9 4LA, UK.
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow , Glasgow G12 8QQ, UK.
J R Soc Interface. 2024 Aug;21(217):20240004. doi: 10.1098/rsif.2024.0004. Epub 2024 Aug 7.
Mechanistic mathematical models such as ordinary differential equations (ODEs) have a long history for their use in describing population dynamics and determining estimates of key parameters that summarize the potential growth or decline of a population over time. More recently, geographic information systems (GIS) have become important tools to provide a visual representation of statistically determined parameters and environmental features over space. Here, we combine these tools to form a 'GIS-ODE' approach to generate spatiotemporal maps predicting how projected changes in thermal climate may affect population densities and, uniquely, population dynamics of , an important tick vector of several human pathogens. Assuming habitat and host densities are not greatly affected by climate warming, the GIS-ODE model predicted that, even under the lowest projected temperature increase, nymph densities could increase by 26-99% in Scotland, depending on the habitat and climate of the location. Our GIS-ODE model provides the vector-borne disease research community with a framework option to produce predictive, spatially explicit risk maps based on a mechanistic understanding of vector and vector-borne disease transmission dynamics.
机制数学模型，如常微分方程（ODEs），在描述种群动态和确定关键参数估计方面有着悠久的历史，这些参数可以总结一个种群随时间的潜在增长或下降。最近，地理信息系统（GIS）已成为提供统计确定的参数和环境特征在空间上的可视化表示的重要工具。在这里，我们将这些工具结合起来，形成了一种“GIS-ODE”方法，生成时空地图，预测热气候的预期变化可能如何影响种群密度，以及独特的几种人类病原体的重要蜱传媒介的种群动态。假设栖息地和宿主密度不受气候变暖的影响很大，GIS-ODE 模型预测，即使在预测的最低温度升高的情况下，苏格兰的若虫密度也可能增加 26-99%，具体取决于地点的栖息地和气候。我们的 GIS-ODE 模型为虫媒疾病研究界提供了一个框架选择，可根据对媒介和虫媒疾病传播动力学的机制理解来生成预测性的、空间明确的风险地图。
