Location: Subtropical Plant Pathology Research
2011 Annual Report
2. Utilize these models to test alternative disease control strategies and to conduct scenario testing.
1)To develop a series of flexible stochastic models to predict the temporal increase and spatial spread of citrus huanglongbing (HLB) and canker. They can be used in a number of ways: to predict spread and to analyze the effectiveness of control strategies both in plantations and State-wide; and.
2)Test various control methods under field conditions to evaluate effects and collect data to parameterize models. SEIDR model: Using Markov-chain Monte Carlo methods, and extensive data from infected areas of South Florida for successive snapshots of the occurrence of symptomatic detected trees in known populations of susceptible trees, we have been able to estimate the transmission rates and dispersal kernel for HLB. We now have a working model that focuses on the differential effects of host age on epidemiological parameters as well as variability across the plantation and that allows for uncertainty in the parameters as well as variability over time and through space. We used Baysian methods to infer posterior densities on the model parameters. The uncertainty is then incorporated in models to predict spread and to allow for uncertainty in the efficiency and comparison of control methods. A front-end (a web based version of the model) was developed for non-researcher users has been nearly finalized and is in validation testing. Both residential and commercial citrus scenarios are being tested and a wide variety of epidemiological and climate/weather variables have been included and are user selectable and changeable via sliding controls. Currently we are examining various disease control/mitigation parameters and are visualizing the effect of these various control strategies. This web tool runs simulations one at a time and is highly instructive to growers and regulators. It is based on a more formal analytical model that can run thousands of simulations based on the same parameters and provide more statistically valid predictions for regulatory intervention strategy building and regulatory/industry decision making. Via this model we have been able to examine the effects of various controls such as using insecticide applications or not, removing infected trees or not, and the effect of HLB infection in young versus older trees. Model output suggests that at least for older trees controlling secondary infections by diseased tree removal and insecticide applications plus controlling primary infection from new insect immigrations by areawide control strategies, can reduce disease increase to a manageable 2 to 5% increase per year, which appears to be economically sustainable. The model continues to be improved upon for perhaps TX, CA or AZ.