Abstract
This course aims to introduce students to models that describe the spread of a pathogen through a population, and how models can support public health decisions. Students will be expected to complete mathematical/statistical exercises and write code that simulates infectious processes.
Course materials
The notes/text are free for use and available at Book . Students should note that the “book” may be updated periodically throughout the semester.
BSTA 395/495: Outbreak Science and Public Health Forecasting
Coordinates and Contact
- Instructor: Tom McAndrew
- Email: mcandrew@lehigh.edu
- Office coordinates: HST 175
- Office hours: To be voted on by students | By appointment
Abstract
This course aims to introduce students to models that describe the spread of a pathogen through a population and how models can support public health decisions.
Students will complete mathematical/statistical exercises and write code that simulates infectious processes.
Class Logistics and Resources
Class Time and Location
- Tuesdays, Thursdays 3:00 PM - 4:15 PM in STEPS Room 121
Remote Students
Tentative Timeline
| Topic | Timeline |
|---|---|
| Reed-Frost Model | |
| Dynamics assumed at start of outbreak | Week 1 |
| Reproduction number and Growth Rate | Week 2 |
| Compartmental models | |
| The SIR and Freq vs Density transmission | Week 3 |
| Condition for an outbreak and Herd Immunity Threshold | Week 4 |
| Lasting infections and Steady-state analysis | Week 5 |
| Latent and Endemic Diseases | Week 6 |
| Public health surveillance and policy | Week 7 |
| Midterm | Week 8 |
| Computational modeling | |
| Simulating stochastic epidemic models (Reed-Frost) | Week 9 |
| Simulating compartmental models (including the use of LLMs) | Week 10 |
| Fitting compartmental models via loglikelihood | Week 11 |
| Fitting a compartmental model to real influenza data | Week 12 |
| Disease propagation with heterogeneous risk of hosts (WMM) | Weeks 13–14 |
Additional Graduate Student Requirements
Graduate students must pose and answer their own question for each homework assignment.
These questions may be used in future iterations of the course.
Graduate students are not eligible for extra credit.
Textbook
The course will follow notes available at: Outbreak Book
.
Additional resources include:
- An Introduction to Infectious Disease Modeling by Emilia Vynnycky and Richard White
- Modeling Infectious Diseases in Humans and Animals by Keeling and Rohani
Students should understand introductory probability, statistics, and the algebra of random variables. A free reference is available here .
Policies
Attendance
Attendance is crucial. If sick, let the instructor know and stay home. Excused absences allow for makeup evaluations.
Collaboration
Work together, but submit your own answers. Collaboration on quizzes, midterms, and finals is not allowed.
Frustration
If frustrated, take a break, return later, and ask for help if needed.
Technology
Computing
We will use Python 3 for simulations and statistical training. VSCode is recommended as an IDE. Students will use DataCamp for Python programming training.
Assignments
Grading Breakdown
| Item | Weight |
|---|---|
| Quizzes | 25% |
| Homework | 50% |
| Midterm | 12.5% |
| Final | 12.5% |
Homework
Homework is due in person, one week after assignment. Late homework grades are reduced as follows:
$
f(\text{grade}, \text{days late}) = \text{grade} \times e^{-0.35 \cdot \text{days late}}
$
No submissions via course site. Late assignments beyond a week receive zero.
Exams
Midterms and finals are in-person oral exams. Quizzes are due by midnight on class days and test class engagement.
Datacamp
Datacamp assignments will accompany homeworks.
Extra Credit
Extra credit involves attending seminars and writing reflections, contributing as an additional quiz score.
Accommodations for Students with Disabilities
Lehigh University provides accommodations through Disability Support Services. More details are available here .
Principles of Our Equitable Community
Lehigh University endorses The Principles of Our Equitable Community .