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

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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.