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A workshop in Fuzzy Modelling of Infectious Disease
Given by Dr. Vahid Anvari
Department of Mathematics and Statistics at York University
A Result of The Statistics and Risk Assessment Section of The Public Health Agency of Canada’s collaboration with The York University Centre for Disease Modelling, Mathematics of Information Technology and Complex Systems, McLaughlin Centre for Population Health Risk Assessment, and the Department of Mathematics and Statistics at the University of Ottawa
Date: May 31-June 1, 2010
Time: 8am – 4pm
Location: University of Ottawa Morisset Hall (University Library building), Room 256, 65 University DriveRegistration: Free online registration (available on the University of Ottawa website)
Class size: Maximum 35
Target learner: Graduate students, Researchers, Public Health Agency of Canada
Workshop Description:
The notion of fuzzy logic has been developed to deal with the concept of partial truth values. Fuzzy-based modeling is quite applicable to epidemiology and to other life science disciplines as well due to the uncertainty involved in the numerical measurements of human behaviors. Uncertainty in epidemiology is not restricted to random variations. There are two kinds of uncertainty: one arises as variability resulting from heterogeneity or stochasticity and another arises as partial ignorance resulting from incomplete knowledge and/or systematic measurement errors which is subjective (epistemic) uncertainty. Therefore, variability and ignorance should be treated separately; probability theory is an accepted tool for variability, while multi-valued fuzzy logic can be a proper tool for ignorance. The main advantage of fuzzy techniques applied to epidemiology is its natural way of representing human cognitive processes. This natural way of reasoning makes fuzzy logic a much understandable tool and easily grasped by medical professionals but it has been not well introduced to the field yet.
Workshop Agenda:
The purpose of the workshop is to provide a brief review on fuzzy theory and its applications to epidemiology. We hope that the medical and epidemiological applications of fuzzy theory described in these lectures encourage researchers to engage in this promising area of research. The lectures will be application oriented and designed for epidemiologists, biologists and public health researchers. Prior background in mathematics or statistics is not required. Outline of the
materials covered in the workshop are as follows.
- Fuzziness as a Measure of Uncertainty
- Introduction to Fuzzy Set Theory
- Possibility (Fuzzy Probability) of Epidemic Events
- Risk estimators and Fuzzy Logic Approach
- Fuzzy Rule-Based Models in Epidemiology
- Decision Making with Uncertainty in Epidemiology
- Fuzzy Dynamical Systems in Epidemic Modeling
- Role of Fuzzy Logic in Biomedicine and Epidemiology, Historical Perspective
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