Department of Epidemiology

Fall 1999

Professor Jim Koopman MD MPH

Meets in the Shapiro computer classroom in the Shapiro Library, Tuesdays and Thursdays 3-5.

Contents:

Course Objectives,

Course Materials,

Class Organization and Procedures,

Course Project,

Daily Class Schedule

This course is being managed through the World Wide Web from the Epidemiology 802 Course Home Page. A more extensive overview of the course is found in Chapter 1 of the online text.

A. Course Objectives:

Conceptual and methodological objectives: Taking this course should enable the student to:

1. Understand why epidemiology must often deal with populations as systems and not just collections of individuals and be able to model populations accordingly.
2. Construct and analyze compartmental models of epidemiological processes in ways that help organize what is known and unknown about the causes of disease, predict future patterns of disease given various scenarios, and determine what new knowledge is most crucial to pursue.
3. Conceptualize, analyze, and generate hypotheses about causes of disease.
4. Describe different approaches to modeling epidemiological processes and understand how compartmental models and statistical models fit into those approaches.

Content area objectives:

1. Learn to construct and analyze simple demographic models of birth and death processes.
2. Learn and understand the dynamic and causal implications of standard epidemiological measures such as cumulative risks, incidence rates, rate ratios, risk ratios, odds ratios, risk differences, and various measures of attributable risk.
3. Learn to model simple transmission systems to explore how risk factors, the natural history of infection, transmissions system conformation, treatment programs, and vaccination programs will affect the circulation of infectious agents.
4. Learn to model the dynamic processes through which vaccine effects are manifest.
5. Learn to model multi-stage disease processes and assess how risk factors acting at different stages of disease affect the pattern of relationships between exposure and disease.
6. Learn to model the processes through which screening programs affect disease patterns.
7. Learn to model different ways that multiple variables can act jointly to generate disease and understand the dynamic and causal implications of joint effect measures such as the interaction terms in linear and logistic regression models.
8. (If time permits) Learn to conceptualize and model competing risks and their effects upon risk assessments.
9. Expand the doctoral student's conceptualization of their thesis project to encompass broader dynamic and causal issues.

Two of the above objectives provide special illustrations of the value that a dynamic systems approach to epidemiology can have in addressing epidemiological problems more thoroughly than is possible using classical risk based approaches. These are objectives 4 and 6. Objective 4 deals with vaccine evaluation. The dynamic models taught are better able to relate vaccine effects to the circulation of infectious agents in a population than are the static models embodied in traditional measures of vaccine effects. Moreover these models allow greater integration of biological information into the process of epidemiological hypothesis formulation. Unlike standard vaccine effect evaluations, they can relate effects of vaccines on immune system responses to effects on the population circulation of infectious agents. These models demonstrate that classical vaccine evaluation can miss vaccine effects on contagiousness that could stop the circulation of infectious agents in a population.

Objective 6 has to do with screening. The approach to screening taught in lower level courses uses static measurements of disease prevalence to calculate predictive values for screening tests. But screening changes prevalence and this effect must be captured with a dynamic model. The dynamic approach can better optimize the cost-efficiency of screening programs, the choice of screening tools, the frequency of rescreening, and the choice of ages for screening.

It is hoped that all of the content areas covered would provide the student with a perspective that would lead them to consider the issues they address in terms of dynamic systems. For that reason the application of the methods taught in the course to the student's thesis topic is a central part of the course.

B. Course Materials:

• Stella Simulation Software, available for either Macintosh or Windows will be available for purchase at Michigan Book and Supply and other bookstores. The student price is $65.
• Most course materials will be published on the Web. Some articles will be reproduced.

C. Class Organization and Procedures:

• The class will meet four hours a week in a computer lab room. Two hours are considered laboratory time where the professor helps students complete their exercises. Consequently the course gets three hours of credit. All four hours must be attended.
• I plan to simplify exercises and go at a pace where I have some assurance that all students are up to speed with the material being covered. I intend to be flexible and make up homework assignments as needed and drop most of the very extensive homework exercises that I required of students in past years. I will not have daily quizzes but I will have many unannounced spot quizzes to help me assess what students are learning or not learning.
• All exercises are to be handed in electronically. Microsoft Word and Stella files are to be sent FTP to /afs/sph.umich.edu/user/j/jkoopman/Public/homework. You are to e-mail the professor at jkoopman@shp.umich.edu when you put something into this Public file. Note that this is not an ifs address like those you use for sending files to each other. File names should begin with hw(relevant #) followed by an underscore, your initials, period, then stm or ms depending on whether it is a Stella or a Microsoft file. For example I would send in my first homework stella file as hw1_jsk.stm.
• Grades will be based 40% upon homework, 20% upon a late mid-term exam, and 40% on a final class project which should relate to the thesis topic of the doctoral student. Spot quizzes will not affect grades. They will just be used to help orient the teaching.

D. Course Project:

This project is intended to contribute directly to the student's thesis work. Individual meetings with professor Koopman will be scheduled early in the course to explore appropriate projects. The project should entail the construction of a compartmental model relevant to the student's thesis work. The write up should:

1. Formulate a dynamic process relevant to the thesis area.
2. Define the purpose for which the dynamic process is being analyzed. For example, is the main purpose to advance understanding of causal processes? Is it to define areas where current knowledge is insufficient? Is it to make disease control decisions? Is it to define what data should be collected?
3. Present the results of model analysis.
4. Summarize the implications of the model analysis for the student's thesis work. The model analysis might affect the issues addressed in the thesis, how thesis results are interpreted, or what significance the thesis work has either on a theoretical or public health basis.

E. Class Schedule

This class schedule presents an ideal that will be adjusted according to necessity to assure that all students are learning the material.

Sept. 9:

• Introduction to compartmental modeling and the Stella program: Population growth models
• Reading: Stella manual, including working out examples, and Chapter 1 of the online text.
• Four articles by Koopman -- two from AJPH and two from AJE. You can get these from the professor before class starts.
• Lecture Notes From First Class

Sept. 14:

• Differential equations and Stella Models
• Reading: Stella manual and Chapter 2. (Focus mainly on the section about difference and differential equations)

Sept. 16:

• The purposes of epidemiological modeling
• Discussion of: Chapter 1 and 4 Koopman articles.

Sept. 21:

• Rates and Risks in Epidemiology
• Reading: Chapter 2

Sept. 23: Lab day

Sept. 28

• Population Growth Models and Equilibria Analysis
• Reading: Chapter 3

Sept. 30: Lab day

Oct 5:

• Risk Factor Effects and Epidemiological Parameters
• Reading: Chapter 4

Oct. 7: Lab day

Oct. 12

• Infectious Disease: Three Basic Epidemiological Models (I will be rewriting this. If this message is still in the syllabus, that means I have not finished with the rewriting.)

Oct. 14 Lab day

Oct. 19:

• Risk Factor Assessment for Fast Spreading Infections that Induce Immunity
• Reading: Chapter 6, Assessing Risk Factors for infection, Article in Amer. J. Epidemiol.

Oct. 21 Lab Day

Oct. 26:

• Vaccine effects: two models of susceptibility effects.
• Reading: Vaccine effects paper in Amer. J Epidemiol.

Oct. 28:

• Vaccine effects on the course of infection.
• Reading: Article in JAIDS, Chapter 7

Nov. 2 Lab day

Nov. 4:

• Multistage disease processes and the Erlang distribution.
• Reading Chapter 9

Nov. 9: lab day:

Nov. 11:

• How to distinguish a cause that acts early or late in a disease process.
• Reading: Chapter 10

Nov. 16: Lab day:

Nov. 18: Late Mid-term examination

Nov. 23:

• Screening:
• Reading: Kelsy Chapter, Eddy articles

Nov. 25: Thanksgiving

Nov. 30: Lab day Dr. Koopman out of town. Time to work on class project.

Dec. 2: Lab day Dr. Koopman out of town. Time to work on class project.

Dec. 7 Lab day Dr. Koopman out of town. Time to work on class project.

Dec. 9

• Models of Joint effects:
• Reading: Epigenesis theory paper

Dec. 4: Lab day:

Dec. 9: Lab day for class projects. Class projects will be due on final examination day: Thursday December 18