CS:4980 Topics in Computer Science II: Computational Epidemiology

Spring 2020, 9:30-10:45 TTh 221 MLH (MacLean Hall)

Sriram V. Pemmaraju
101G MLH, sriram-pemmaraju@uiowa.edu, 319-353-2956
Office Hours: 1:30-2:30 M, 10:30-11:30 W, 2:00-3:00 F.
Alberto M. Segre
14G MLH, alberto-segre@uiowa.edu, 319-335-1713
Office: TBA.

Our office hours are "walk-in" hours and you don't need to make an appointment to see us during office hours. We are also happy to meet with students outside office hours by prior appointment.

Course webpage: homepage.cs.uiowa.edu/~sriram/4980/spring20/
Department website: http://www.cs.uiowa.edu/

This is a graduate-level Computer Science (CS) course on computational epidemiology, which is the study and development of computational techniques and tools for modeling, simulating, predicting, forecasting, surveilling, mitigating, and visualizing the spread of disease. In this course, we will use techniques from different areas of CS including algorithms, data mining, discrete-event simulations, machine learning, and network science. The course is organized into four parts: (i) Disease-spread models and analysis of disease dynamics, (ii) Inference, prediction, and forecasting problems related to disease-spread, (iii) Infection control and disease surveillance problems, (iv) Additional topics including a discussion of disease-related datasets and the use of technology for gathering contact data. A more detailed list of topics and readings appears further below.

No prior background in epidemiology or biology is assumed. However, a solid background in discrete mathematics, especially graph theory and discrete probability, and a solid background in programming and data structures will be assumed. Background in linear algebra and statistics is not required, but mathematical maturity in these areas will likely be helpful. A substantial portion of student evaluation will be via a group project, that will include an end-of-term technical paper and presentation. Additional modes of evaluation will include solving homework problems, writing short technical pieces, participating in classroom and online discussions, and scribing lecture notes. There will be no exams in this course.

Syllabus document, Announcements, Assignments, Weekly Topics



Weekly Topics