2012 Annual Report
1a.Objectives (from AD-416):
Infectious diseases such as foot and mouth disease (FMD) and the Ug99 strain of wheat stem rust (Ug99) present significant threats to national security because of the potential for the rapid spread of these diseases on a continental scale resulting in severe social and economic disruption. In most scenarios, these dislocations would certainly involve extended embargo of agricultural products, restriction of interstate commerce and costs associated managing the outbreak itself (e.g., diagnosis, testing, removal and quarantine).
The potential for high socio-economic impact makes it essentially impossible to conduct designed experiments to validate epidemiological models for the spread of these diseases in real-world settings. In the absence of data from controlled experiments to test specific hypotheses and predictions, it is common to rely on endorsements by trusted subject matter experts or by fitting to extant observational data. In contrast, plant diseases such as wheat leaf rust, sudden oak death and late blight of potato and tomato offer the opportunity to conduct extensive validation of models and thereby to gain better insight into the spatiotemporal dynamics of infectious diseases.
Since infectious diseases of plants and animals can spread over similar scales of space and time, it makes sense to use plant diseases as proxies for highly contagious foreign animal diseases such as FMD in order to address generic, high-level questions relating to commonalities among pathosystems and methodologies for constructing quantitative models. By first addressing some more general, high-level questions in the context of particular well-characterized model plant systems, we propose to identify and address generic statistical and methodological issues, and then to test solutions to these problems in the context of specific models. This knowledge can then be used to guide the development, implementation and testing of models for specific animal diseases, despite the differences in the details of the models and biology of the organisms involved.
1b.Approach (from AD-416):
The primary focus of this work will be on several generic aspects of epidemiological models: (a) identification and examination of existing models and datasets as case studies; (b) analysis of models to examine dynamics of disease spread, parameter estimation, and contact network structure in models of interest; (c) analysis of statistics of extreme events and first passage times in models of interest; (d) development of a flexible toolkit for implementing spatio-temporal models for national-scale outbreaks of agriculturally-related infectious diseases; (e) applications to fungal diseases of plants capable of rapid dispersal over long distances. Related issues arise in essentially all models for FAD epidemics on a national scale.
The USDA and DHS, in collaboration with researchers at Cornell University, are currently working together closely to address these problems. Within USDA and Cornell, this performance site is uniquely qualified to provide expertise in statistical physics, dynamical systems, applied mathematics, computational science, and close cooperation with subject matter experts in plant diseases. In addition, there are well-established relationships across a range of technical and scientific disciplines relevant to the project. The performer has already established a working relationship with DHS S&T and USDA-APHIS, as well as other DHS S&T performers (e.g., RAPPID).
Since award of this IAA on 21 January 2010, the Ithaca group has established a highly productive working relationship with several RAPIDD Working Groups. In addition, the group is now collaborating with researchers at Cornell (Ithaca, NY) and Cambridge (Cambridge, UK).
The Ithaca group has been developing methods that exploit land use and related geographic and remote sensing data into spatial models of disease outbreaks. This is a unique and time critical opportunity for the DHS S&T FAD Modeling Program. Existing funding will be used to support two students, two full time senior scientific staff members and two postdoctoral researchers. These positions will all be supported for the remainder of this IAA (through Jan. 2014).
Developed mathematical and statistical techniques for modeling the dynamics of “epidemic waves” in spatially distributed systems such as complex landscapes and large networks. These techniques make it possible to predict how fast these waves propagate through the system, as well as overall outbreak sizes. Briefly, these models suggest that speed of the wave depends on the detailed local behavior of the disease at the leading edge; differences in the resulting structure of the wave front may influence the long-term success of various control strategies. Finally, we have performed a detailed mathematical analysis of how the predictions of several simple stochastic models for disease dynamics on variations of the input parameters. This sensitivity analysis reveals that small variations in the input parameters can lead to very large differences in long-term predictions such as the duration and overall size of the epidemics. Understanding how in estimated disease parameters are compounded over time makes it possible to do a better job of assessing the uncertainties in model predictions used to predict the success or failure of control strategies.