Combining Models and Data to Understand the Spatial Dynamics of Host-Pathogen Interactions

这个项目的目标是建立数学模型，描述导致传染病的病原体在太空中的传播，并确定疾病热点发展的情况。模型通常有助于理解和预测病原体的传播，但大多数模型假设宿主生物移动如此之快，以至于生物体之间的距离无关紧要。这种假设可能适用于人类和其他动物的某些病原体，但对于其他宿主的移动速度足够慢，距离很重要。当移动速度缓慢时，可能会出现局部疾病热点，但预测这些热点将在何时何地发生需要新的模型。该项目将首先建立模型，描述道格拉斯杉木毛毡蛾（Orgyia pseudosugata）昆虫病原体的传播，这是美国西部森林的一种严重害虫。该物种每隔10至11年爆发一次，其密度从无法检测到的水平增加到整个森林被摧毁的水平。如果不是病原兽疫（兽疫是动物中的流行病），这种昆虫造成的破坏会严重得多，因为它会使昆虫数量大量减少。然后，通过简化这些模型，使它们可以应用于广泛的病原体，该项目将构建疾病空间传播的一般理论。由于最初的模型将侧重于昆虫病原体，因此这些模型将有助于预测害虫种群何时将被病原体控制，从而使害虫管理人员能够确定何时不需要人工杀虫剂。研究人员将与美国林务局合作，将他们的研究结果应用于控制道格拉斯冷杉tussock蛾的努力。此外，该项目将培训研究生和本科生，包括来自科学领域代表性不足的群体的个人。该项目的第一步将是使用统计模型选择，通过将模型与道格拉斯冷杉tussock蛾病毒病原体的空间数据进行比较，在相互竞争的空间模型之间进行选择。pi先前的工作表明，小森林斑块的动物流行病可以通过非空间疾病模型准确预测，但在更大的尺度上，毛毡蛾的数据显示出强烈的空间模式。这些模式与一些疾病传播的空间模型预测的模式大致相似，但这些模型是否能解释这些数据尚不清楚。因此，该项目将比较一系列空间模型解释数据的能力。为此，研究人员将使用贝叶斯统计方法，将实验数据构建的信息贝叶斯先验与基于大尺度空间数据的似然相结合，计算模型选择统计量，从而选择最佳模型。这种方法将使研究人员能够确定纳入模型的机制是否真正有助于理解疾病在自然界的传播。毫无疑问，最能解释这些数据的模型将包含特定于tussock飞蛾-病毒相互作用的机制。因此，研究人员将通过产生更简单的模型来发展疾病空间传播的更一般的理论，这些模型可用于产生将病原体的空间传播与宿主-病原体相互作用生物学联系起来的分析结果。因此，该项目旨在建立一种疾病空间传播的一般理论，有助于描述自然界中真实疾病的传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to create mathematical models that describe the spread of pathogens that cause infectious diseases across space, and to identify the circumstances under which disease hot spots develop. Models are often useful for understanding and projecting pathogen spread, but most models assume that host organisms move so rapidly that the distances between organisms do not matter. This assumption may be appropriate for some pathogens of humans and other animals, but for others host movement rates are slow enough that distances matter. When movement rates are slow, it is possible for local hot spots of disease to develop, but predicting where and when these hot spots will occur requires new models. This project will begin by constructing models that describe the spread of insect pathogens of the Douglas-fir tussock moth, Orgyia pseudotsugata, which is a serious pest of forests in the western U.S. This species undergoes outbreaks at 10- to 11-year intervals, such that its densities increase from levels that are undetectable, to levels at which entire forests are destroyed. The devastation that the insect imposes would be far worse if not for pathogen epizootics (epizootics are epidemics in animals), which decimate the insect population. By then simplifying these models so that they can be applied to a wide range of pathogens, the project will construct a general theory of the spatial spread of disease. Because the initial models will focus on insect pathogens, the models will be useful in predicting when populations of pest insects will be controlled by pathogens, allowing pest managers to determine when artificial insecticides are not needed. The researchers will be working with the US Forest Service to apply their results to contol efforts for the Douglas-fir tussock moth. In addition, the project will train graduate and undergraduate students, including individuals from groups that are underrepresented in the sciences.The project’s first step will be to use statistical model selection to choose between competing spatial models, by comparing the models to spatial data on viral pathogens of the Douglas-fir tussock moth. Previous work by the PIs showed that epizootics in small forest patches can be accurately predicted by non-spatial disease models, but at larger scales the data for the tussock moth show strong spatial patterning. These patterns roughly resemble the patterns predicted by some spatial models of disease spread, but whether the models can explain the data is unknown. The project will therefore compare the ability of a range of spatial models to explain the data. To do this, the investigators will use a Bayesian statistical approach, in which informative Bayesian priors constructed from experimental data are combined with likelihoods based on large-scale spatial data to calculate model selection statistics, and thus to choose the best model. This approach will allow the investigators to determine whether the mechanisms incorporated in the models are truly useful for understanding the spread of disease in nature. The models that best explain the data will undoubtedly incorporate mechanisms that are specific to the tussock moth-virus interaction. The investigators will therefore develop a more general theory of the spatial spread of disease by producing simpler version of their models that can be used to generate analytic results relating the spatial spread of pathogens to the biology of host-pathogen interactions. The project thus aims to create a general theory of the spatial spread of disease that is useful for describing the spread of real diseases in nature.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.