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.

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.

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.

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