Abstract

This course will focus on the dynamics, simulation, and inference of: the Kermack-McKendrick model (including age of infection), meta-population, and network models. The course will emphasize both the theory of advanced epidemic models and mathematical/statistical programming.

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 397/497: Outbreak Science and Public Health Forecasting 2

Coordinates and Contact

  • Instructor: Tom McAndrew
  • Email: mcandrew@lehigh.edu
  • Office coordinates: HST 175
  • Office hours: To be voted on by students | By appointment

Class Logistics and Resources

Class Time and Location

  • Tuesdays, Thursdays 12:10 PM - 1:25 PM in Maginnes 103

Tentative Timeline

TopicTimeline
Review of outbreak science 1Week 1
Discrete time Kermack-McKendrick ModelWeek 2
Fixed points and stability analysis for compartmental modelsWeek 3
Temporal forcing and seasonal infectious modelsWeek 4/5
Metapopulation modelsWeek 6/7
Modeling multiple pathogensWeek 8
Discussion of Antibiotic resistance modelsWeek 8
Spatial infectious disease modelsWeek 9/10
Stochastic infectious disease modelsWeek 11/12
Lattice-type modelsWeek 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:

  1. An Introduction to Infectious Disease Modeling by Emilia Vynnycky and Richard White
  2. 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

ItemWeight
Homework100%

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.

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 .