Program
| MSE1 | Woodward 1; 10:15 am - 12:15 pm, July 29 |
|---|---|
| Title | Application of control theory to infectious disease modeling |
| Organizers | Elsa Hansen |
| Queen's University | |
| Troy Day | |
| Queen's University | |
| Abstract | Mathematical analysis is playing an increasingly important role in the study of infectious diseases. Mathematical models are now routinely used to gain a better understanding of the factors that govern the spread of infectious diseases, and also to determine the best strategies for public health interventions. From a practical point of view, the ultimate aim of disease modeling is to gain the insight necessary to best achieve disease control, and possibly even eradication. During a disease outbreak, there are several public health interventions available including isolation, quarantine, other forms of social distancing, the use of drugs (both as a prophylactic and as a treatment), and vaccination. The vast majority of mathematical analyses of the pros and cons of each strategy, and of their optimal deployment, take a somewhat static approach. Specifically, most analyses are based on the assumption that the intervention in question is applied at a constant level during the entire outbreak. In many ways this static approach is unnatural. Rather, one can readily imagine that varying the level of intervention in certain ways as the epidemiological dynamics play out might yield a better outcome. This minisymposium will bring together researchers who are using mathematical techniques from control theory to analyze such dynamic optimization problems in epidemiology. Although the application of control theory to mathematical epidemiology is of clear practical value, surprisingly little analysis has been done in this area. In addition, many of the previously published results have not been fully assimilated by practicing epidemiologists and public health officials. This session will highlight current research, encourage further application of control theory to epidemiology, and identify key questions for future research. It will also provide a venue to discuss how to bridge the gap from research to policy making. Invited speakers will present work that either highlights the application of classical control theory in epidemiology or that highlights an area of epidemiology to which control theory might be profitably applied. The session will emphasize the interplay between model design and the application of mathematical techniques from control theory, including Pontryagin's Maximum Principle and dynamic programming. Two key goals will be to illustrate how taking a control-theoretic approach broadens one's perspective on how best to intervene during disease outbreaks, and to show how new insights can be gained using this approach. |
| Speaker 1 | Zhilan Feng |
| Purdue University | |
| Control and Prevention Strategies for the Spread of Influenza | |
| Speaker 2 | Christopher Bowman |
| National Research Council of Canada, Institute for Biodiagnostics | |
| Optimal Control for an Influenza Pandemic | |
| Speaker 3 | Elsa Hansen |
| Queen's University | |
| Characterizing the Effect of Emerging Resistance on the Optimal Treatment Strategy | |
| Speaker 4 | Suzanne Lenhart |
| University of Tennessee | |
| Optimal Control in Epidemic Models of Rabies in Raccoons |
-- Minisymposium talks are scheduled for 30 min each, including time for questions.