Lyme Disease

Detecting sites at risk of becoming foci for Lyme disease

funded by National Institutes of Health (NIH)

Specific Aims:

        The objective of the Phase I research was to "(1) develop a computer-based platform for users to construct set-based qualitative models, and (2) customize this program to analyze data for the detection of areas at high risk for becoming foci for Lyme disease."  The premise for constructing this program was to develop a novel kind of approach for rating parcels of land for the risk of becoming foci for Lyme disease.  Solving this type of problem is typically attacked by applying the techniques of statistical analysis to empirical data.  My approach was to implement recently developed qualitative procedures as computer software so that these techniques could be applied to a problem for which the relationships between vectors and their habitats are not yet clearly known.  As stated by the CDC(1989), "Data concerning risk factors for acquiring Lyme disease are limited."  Thus, the overall goal was to develop a tool for analyzing the data which are available in order to develop a more precise picture of the factors which permit foci of Lyme disease to develop.

        The mathematical techniques that I proposed to utilize are commonly referred to as fuzzy logic inference.  At the time, the use of fuzzy logic to solve biological problems was extremely limited and no computing platform existed.  Almost exclusively, fuzzy logic techniques have been limited to use in engineering control systems.  I saw an opportunity to apply these techniques to a wide class of problems of public health importance such as how to efficiently assess the risk that a particular habitat can support arthropod vectors of human diseases, such as the Ixodes dammini - Borrelia burgdorferi system.

        The three-month Phase I research period allowed me to develop and test a compact fuzzy logic inference engine.  This module uses fuzzy associative memory architecture and works by having users specify variables, adjectives, and a rule base.  Adjectives are described graphically.  The rule base has its own syntax and supports the modification of adjectives using adverbs or complementation.  In addition, two types of fuzzy inference are supported; these are correlation-minimum and correlation-product inference.  The system can generate syntactically correct C code for the variable definitions, the rule base and for the adjective set.  This code will be used in the final stand-alone system.  The inference module has been connected with crude data input and output routines for testing the general idea of rating habitat to determine the likelihood that Lyme disease foci could develop.  Although a significant amount of progress was made, more work is necessary before the system is ready for commercial production.

Also see: List of AB Publications

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