Dynamic Modeling of Diseases and Pests

Dynamic Modeling of Diseases and Pests

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The ease of use of the programs in the application to ever more complex cases of disease and pestilence. The lack of need on the part of the student or modelers of mathematics beyond algebra and the lack of need of any prior computer programming experience. The surprising insights that can be gained from initially simple systems models.
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Product details

  • Paperback | 290 pages
  • 155 x 235 x 16mm | 462g
  • New York, United States
  • English
  • 2009 ed.
  • VIII, 290 p.
  • 148999503X
  • 9781489995032

Back cover copy

Models help us understand the nonlinear dynamics of real-world processes by using the computer to mimic the actual forces that result in a system's behavior. The growing complexity of human social systems, from individual behavior to that of entire populations makes us increasingly vulnerable to diseases and pests. The ecology of the disease agents and the pests when considered in this social context only adds to the complexity. The feedbacks, lags in the effects of our preventive actions and the randomness in the environment make understanding of these vulnerabilities seem insurmountable. The amount and pace of modern travel provides virus and pest alike with the means to quickly find new hosts in untouched human populations and the ecosystems.



We thus have compelling reasons to understand the dynamics of these combined systems. This book begins with simple examples of human epidemics and then insect dynamics. Next comes the models of ever more complex models of disease carried by interaction of the two. An invasive species model is followed by insect-ecosystem interactions. The general models of chaos and catastrophe are linked to models of disease and pest. The final model is a spatial dynamic spread of disease among a wild animal population.



By using the STELLA programs (runtime versions and digital forms of all models are available with the book) we show how with a minimum of mathematical preparation and programming experience, these complex processes can be simulated and their emergent properties discovered. The programs run on both Macintosh and PC based machines.
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Table of contents

Part I: Introduction
1. The Why and How of Dynamic Modeling
1.1. Introduction
1.2. Static, Comparative-Static and Dynamic Models
1.3. Model Complexity and Explanatory Power
1.4. Model Components
1.5. Modeling in STELLA
1.6. Analogy and Creativity
1.7. STELLA's Numeric Solution Techniques
1.8. Sources of Model Errors
1.9. The Detailed Modeling Process
1.10. Questions and Tasks
2. Theory and Concepts
2.1. Basic Epidemic Model
2.2. Basic Epidemic Model with Randomness
2.3. Loss of Immunity
2.4. Two-population Epidemic Model
2.5. Epidemic with Vaccination
2.6. Questions and Tasks
3. Insect Dynamics
3.1. Matching Experiments and Models of Insect Life Cycles
3.2. Optimal Insect Switching
3.3. Two Age Class Parasite Model
3.4. Questions and Tasks
Part II: Applications
4. Malaria and Sickle Cell Anemia
4.1. Malaria
4.1.1. Basic Malaria Model
4.1.2. Questions and Tasks
4.2. Sickle Cell Anemia and Malaria in Balance
4.2.1. Sickle Cell Anemia
4.2.2. Questions and Tasks
5. Encephalitis
5.1. St. Louis Encephalitis
5.2. Questions and Tasks
6. Chagas Disease
6.1. Chagas Disease Spread and Control Strategies
6.2. Questions and Tasks
7. Lyme Disease
7.1. Lyme Disease Model
7.2. Questions and Tasks
8. Chicken Pox and Shingles
8.1. Model Assumptions and Structure
8.2. Questions and Tasks
9. Toxoplasmosis
9.1. Introduction
9.2. Model Construction
9.3. Results
9.4. Questions and Tasks
10. The Zebra Mussel
11. Biological Control of Pestilence
11.1. Herbivory and Algae
11.1.1. Herbivore-Algae Predator-Prey Model
11.1.2. Questions and Tasks
11.2. Bluegill Population Management
11.2.1. BluegillDynamics
11.2.2. Impacts of Fishing
11.2.3. Impacts of Disease
11.2.4. Questions and Tasks
11.3. Woolly Adelgid
11.3.1. Infestation of Fraser Fir
11.3.2. Adelgid and Fir Dynamics
11.3.3. Questions and Tasks
12. Western Corn Rootworm Population Dynamics and Coevolution
12.1. Western Corn Rootworm
12.2. Model Development
12.3. Questions and Tasks
13. Chaos and Pestilence
13.1. Basic Disease Model with Chaos
13.1.1. Model Setup
13.1.2. Detecting and Interpreting Chaos
13.1.3. Questions and Tasks
13.2. Chaos with Nicholson-Bailey Equations
13.2.1. Host-Parasitoid Interactions
13.2.2. Questions and Tasks
14. Catastrophe and Pestilence
14.1. Basic Catastrophe Model
14.2. Spruce Budworm Catastrophe
14.3. Questions and Tasks
15. Spatial Dynamics of Pestilence
15.1. Diseased and Healthy Migrating Insects
15.1.1. Introduction
15.1.2. Model Design
15.1.3. Results
15.1.4. Questions and Tasks
15.2. The Spatial Dynamic Spread of Rabies in Foxes
15.2.1. Introduction
15.2.2. Fox Rabies in Illinois
15.2.3. Previous Fox Rabies Models
15.2.4. The Rabies Virus
15.2.5. Fox Biology
15.2.6. Model Design
15.2.7. Cellular Model
15.2.8. Model Assumptions
15.2.9. Georeferencing the Modeling Process
15.2.10. Spatial Characteristics
15.2.11. Model Constraints
15.2.12. Model Results
15.2.13. Rabies Pressure
15.2.14. The Effects of Disease Alone
15.2.15. Hunting Pressure
15.2.16. Controlling the Disease
Part III: Conclusions
16. Conclusions
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Review Text

From the reviews:
"The authors have written several textbooks that became classical in modeling dynamic systems, comprising various subjects and disciplines. Now they bring an exceptional work specially dedicated to diseases and pests. It is a book that can be very useful for beginners and intermediate or advanced modelers." (Ruben La Rossa, Integrated Pest Management Bulletin, December, 2010)
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Review quote

From the reviews:

"The authors have written several textbooks that became classical in modeling dynamic systems, comprising various subjects and disciplines. Now they bring an exceptional work specially dedicated to diseases and pests. It is a book that can be very useful for beginners and intermediate or advanced modelers." (Ruben La Rossa, Integrated Pest Management Bulletin, December, 2010)
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About Bruce Hannon

Bruce Hannon is Jubilee professor of the College of Liberal Arts and Sciences and is associated with the departments of Geography, Ecology and Evolutionary Biology, Epidemiology and Preventive Medicine and Bioengineering and the National Center for Super Computing Applications and the Illinois Natural History Survey.

Matthias Ruth is Roy F. Weston Chair in Natural Economics, founding Director of the Center for Integrative Environmental Research at the Division of Research, Director of the Environmental Policy Program at the School of Public Policy, and founding Co-Director of the Engineering and Public Policy Program at the University of Maryland.
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