Location: Subtropical Plant Pathology Research2013 Annual Report
1a. Objectives (from AD-416):
1. Develop a survey methodology that will maximize the probability to detect high-risk pathogens of agricultural significance at an early phase. 2. Developed a tool to calculate resources required and optimize the use of sampling resources. 3. Deliver user friendly Excel-spreadsheet based module that can be used by regulatory agencies.
1b. Approach (from AD-416):
Data sets to parameterize the travel risk model will be collected at ports of entry from international travelers and compiled for model use. An existing preliminary model will be enhanced by the use of zip codes and by extensive consideration of epidemiological characteristics used to parameterize risk. Using dynamically-linked spreadsheets a resources estimator will be developed that calculates necessary personnel (surveyors, supervisors, support staff, etc), plus fiscal and infrastructure costs (vehicles, equipment, and miscellaneous requirements) needed to accomplish the surveys and goals.
3. Progress Report:
This project is related to inhouse project objective 3: Develop or improve comprehensive integrated disease management strategies. We have extensively tested the approximation formula against exact solutions of the early detection algorithm. The approximation formula shows to be robust for parameters of most citrus diseases. This enables us to advise growers and regulatory agencies on the sampling frequency and intensity for citrus pathogens that need to be detected at an early stage of the development on an epidemic. The test of the early detection system against a spatially explicit stochastic models is underway. A large range of cases, with different grove size and age and also a range of parameter values needs to be simulated. We have set up the model and will start the simulations. A survey method has been developed for plant animal and human disease pathosystems. We intend to test this method against data in the next quarter. The method can then be used for early detection of pathogen entry in large scale areas. We have gathered the data necessary to validate the MPS. Data sets on HLB in Florida show to be the best test example. For the purposes of this study USDA-APHIS made the HLB component of its MPS dataset available for the period during 2010 and 2011 whereby six survey cycles were conducted over 38 weeks. During each individual survey cycle a number of plantings were selected using an algorithm which randomly samples all citrus plantings in Florida without replacement. These data will be used in the coming year to validate the MPS system. A first try, with estimated parameters, shows that the validation method and data set work very well. Work on this objective is underway. Initial spreadsheets are being set up. The estimator will be developed such that it reacts dynamically to sampling frequency, intensity and distribution of samples. We intent to link the estimator to various survey programs that provide an estimate of the number of samples needed. Delivery of the user friendly model and excel spreadsheet is scheduled for later in the grant timeline.