Susan L Cassels
Covers the basic tools for building and analyzing mathematical models of infectious disease epidemics. Model types include deterministic and stochastic models, compartmental and individual-based models. Laboratory provides hands-on model building experience in Excel, Stella, and R. Offered: A.
Course Description & Objectives: This course is designed to provide students with the basic tools for building and analyzing mathematical models of disease epidemics. Dynamical systems, such as those that represent infectious disease transmission dynamics, are fundamentally different than traditional statistical models, and this course will provide insight into the fun, complex, and sometimes unexpected world of modeling these systems. This course seeks to prepare public health graduate students to build and analyze the mathematical models of disease that they will encounter in the scientific literature and use in their work as public health professionals. We will use Excel and Stella, a user- friendly modeling package, to run simple disease models. The handsâ?on lab experience will demystify mathematical modeling and lead to a clear understanding of the various types, characteristics, and qualities of models.
Student learning goals
Describe the philosophy of model-building and the relationships between modeling and other forms of scientific inquiry
Identify research questions that can be addressed with epidemic modeling methods
Discuss the role of epidemic modeling in public health policy and resource allocation
Interpret and critique mathematical models published in the scientific literature
Demonstrate proficient use of Excel, and Stella for building simple disease models
General method of instruction
The course combines a number of approaches to achieve these aims. Course time will be a mixture of lecture, seminarâstyle discussion of readings, in which we dissect a series of modeling papers, and time in the computer lab where you will recreate, manipulate, build your own mathematical models.
This course will involve basic programming, but prior experience with programming is not required. Expertise in calculus in not required either! We will review the basic concepts in math that we use throughout the course, including logarithms, differentiation and integration.
Class assignments and grading
Paper critiques: Throughout the course we will be discussing a wide range of published modeling papers. The two paper critiques will require you to build upon what we are learning in class by choosing a paper from the modeling literature and writing a critique. Critiques need to entail a discussion of the scientific question at hand, a detailed description of the model used, and an assessment of the model. For each paper, you may want to consider questions such as: What are the authors modeling? Is the model reasonable? What questions are being answered with the model, and what questions were not answered? What are good features of the model? How could the model be improved?
Each critique should fit on one page (single spaced if needed).
Class Participation: In addition to actively participating in each class, students will lead one journal-club style discussion of a modeling paper.
Lab Assignments: Students will submit completed labs assignments. We will go over lab assignments as a group during the computer lab sessions. However, students are responsible for completing the assignments and uploading the final models. Successful lab assignments are models that run correctly. Excellent labs are those with tidy programming as well. (Programming is considered an art by some!)
Final Project: The final project is a chance for you to gain practice building your own mathematical model as it relates to your own research interests. Students are encouraged to begin shaping their ideas early and to work with me on this process.
Successful projects are those that are well-grounded in the existing literature on the topic of interest, employ modeling to expand on that literature in a way not easily accomplished through other means, and are mindful of both the uses and limitations of modeling. They are also clearly written and engaging. Very successful papers will help you make great strides in your dissertation research, and/or form the first steps towards a publishable paper.
Not all projects will be able to take the same form; some may entail a fully developed mathematical model that you both program and explore, while others may recreate and modify a published model in order to answer a different question. We will discuss this in more detail as the quarter progresses, and determine together what the most fruitful approach is for each of your interests.
The project write-up should be roughly 15-20 pages, including text, figures, tables and perhaps snippets of code. The text should comprise roughly 10 pages of the total.
Your grade is broken down as follows: Paper Critiques: 20% (10% X 2) Class Participation: 15% Lab Assignments: 25% Final paper/project: 40%