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United States Department of Agriculture

Agricultural Research Service

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Research Project: Development of a Generic Multiple-Pest Model & User-Friendly Front End to Test Control Strategies & Establish Necessary Resources for High-Risk Path

Location: Subtropical Plant Pathology Research

2012 Annual Report

1a. Objectives (from AD-416):
1. Develop a generic epidemiological model that can be used to compare control scenarios and to optimize the probability of controlling and managing high-risk pathogens of agricultural significance. 2. Develop an economic extension to the epidemiological models to balance efforts between detection and control to maximize use of fiscal and manpower resources. 3. Develop user friendly model ‘front ends’ that can be used by researchers and regulatory agencies.

1b. Approach (from AD-416):
Data sets to parameterize the model will be collected from U.S. epidemics of high-risk pathogens such as citrus huanglongbing and citrus canker and compiled for model use. Parsimonious, stochastic computational models will be used to provide an efficient generic modeling platform in combination with Markov Chain Monte Carlo and ABC modeling methods to estimate model parameters. An economic module will be developed for the model to simulate multiple scenarios for parsing manpower and fiscal resources between detection and control efforts to maximize use of resources. A user friendly ‘front end’ interface will be developed in Adobe Flash or equivalent platform for use by research and regulatory personal that can be placed on the Web or deployed directly.

3. Progress Report:
This research is related to inhouse project objective 3. Develop or improve comprehensive integrated disease management strategies. A Susceptible Exposed Infected Diseased Removed (SEIDR) model was developed that uses Markov-chain Monte Carlo methods, and extensive data from South Florida. This model portrays successive snapshots of the occurrence of symptomatic detected trees in known populations of susceptible trees. To date we have been able to estimate the transmission rates and dispersal kernel for huanglongbing (HLB) by interrogating this prior data center. Currently we are working on differential effects of host age on epidemiological parameters and variability across the plantation. This allows us to explore the uncertainty how the predictions which is then incorporated in models to predict spread and to allow for uncertainty in the efficiency and comparison of control methods. A web based version of the model is in the final stages of development. This web-based model is for non-researcher users you can utilize it as a tool to understand how the disease will increase and spread both residential and commercial situations. It can also be used to test a wide variety of epidemiological and climate/weather variables and their effects on an HLB epidemic as well as various disease control/mitigation parameters to see the effect of these various control strategies. The team has been using the data from southern gardens citrus that includes both young and mature trees, since host age is likely to be an important consideration for HLB. The model continues to be improved upon to capture more and more of the true features of the data and the disease. The model is being extended to estimate spread in diverse situations, should HLB be introduced into new areas such as Texas, California or Arizona. Field plots were established at the USDA, ARS Picos Farm to examine the effects of insecticide control and roguing in various combinations, both to evaluate treatment effects and to collect epidemiological data for model development. These field studies are underway as well however, at the time of this writing there are no detectable statistical differences among various control strategies.

4. Accomplishments

Last Modified: 08/19/2017
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