CS:4980 Topics in Computer Science II: Computational Epidemiology

Instructors:
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.

Prerequisite
For undergraduates: CS:2210 with a minimum grade of C- and CS:2230 with a minimum grade of C-.
For graduate students: a solid background in discrete mathematics, especially graph theory and discrete probability, and a solid background in programming and data structures.

Reading Material You are not required to purchase a textbook for this course. We will use a variety of sources, including research articles, lecture notes, and textbook chapters. Much of this material can be found online and we will post links as the course proceeds. Here are some readings that provide a broad coverage of computational epidemiology; material from these readings will form the basis for the first third of the course.

• Networks: An Introduction, Newman, Oxford University Press, 2010.
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Easley and Kleinberg, Cambridge University Press, 2010.
• Mathematical Approaches to Infectious Disease Prediction and Control, Dimitrov and Meyers. INFORMS: Tutorials in Operations Research, 2010.
  http://www.bio.utexas.edu/research/meyers/_docs/publications/DimitrovINFORMS10.pdf.
• Computational Epidemiology, Marathe and Vullikanti. CACM July 2013.
  https://cacm.acm.org/magazines/2013/7/165478-computational-epidemiology/fulltext.
• Computational Epidemiology, Marathe, Ramakrishnan, and Vullikanti. KDD tutorial slides, 2014.
  http://ndssl.vbi.vt.edu/supplementary-info/vskumar/kdd-slides.pdf.

Tentative* List of Topics

• PART I: Introduction, models, and disease dynamics. What is epidemiology? What is computational epidemiology? Historical overview of epidemiology and computational epidemiology. The importance of understanding and controlling disease-spread. Mass action disease models. Networked computational epidemiology. Dynamics of disease-spread in mass action models and on contact network models.
• PART II: Inference, prediction, and forecasting problems. Given noisy and sparse disease incidence data, how to infer disease model parameters such as transmission probability, period of infectivity, and reproduction number? How to infer infection source and most likely cascades? How to infer latent spreaders? How to predict future cases, forecast future epidemics?
• PART III: Infection surveillance and control problems. Given contact network model and disease model, how to design low-cost disease surveillance and infection control policies? Surveillance includes placing data collection sites in clinics, performing extra testing on selected patients, swabbing selected surfaces in a hospital, etc. Infection control policies include vaccination, prescribing antibiotics and antivirals, quarantining, sequestering, etc.
• Part IV: Additional Topics. Sources of disease data: UIHC databases, CDC MMWR, Healthcare Cost and Utilization Project (HCUP) datasets, Truven Health Analytics datasets, MIMIC dataset, other publicly available datasets for research. Deployment and use of technology for gathering contact and behavior data. High-performance computing for large-scale simulations. Game-theory of vaccination.

Grading
Plus/Minus grading will be used for the course. There are four components that will determine your grade.

• Homeworks and Reading Responses: 20% 1-2 homeworks will be assigned during the early portion of semester. Students will be assigned 3-5 readings to which they will have to provide written responses. Written reponses will be evaluated for correctness, technical accuracy, clarity, and following standard stylistic conventions for technical writing.

• Scribe Notes: 15% For selected classes, students in groups of two will be responsible for scribing notes for that class. The scribe schedule will be distributed ahead of time. Students will have one week after the lecture to submit polished, LaTeX'ed scribed notes. We will then provide feedback and students will have two days to edit their notes in response. Scribe notes will be posted on the course page for all students to use. Scribe notes should reflect a good understanding of the material covered in class, should be clear, precise, and well-formatted, should be free of grammatical errors and typos, and follow standard stylistic conventions for technical writing.

• Discussion participation: 15% There will be a number of in-class and online discussions of research papers. Students will be evaluated based on the quality of their comments and questions during these discussions.

• Course Project: 50% About 4-5 weeks into the semester, students (in groups of 3) will be required to submit project proposals. Prior to this submission, we will provide suggestions for projects and datasets, both in class and on the course website. In addition to the proposal, there will be a number of project "deliverables" throughout the rest of the semester, possibly including a literature survey, a prototype demonstration, a draft project report, and a peer-review. During the finals week, each group will do a final presentation and submit a final project report.

CLAS Final Examination Policies
The final exam schedule for each semester is announced around the fifth week of classes; students are responsible for knowing the date, time, and place of a final exam. Students should not make travel plans until knowing this final exam information. In this course, we will use the final exam slot for final project presentations. (See https://clas.uiowa.edu/faculty/teaching-policies-resources-examination-policies).

Collaboration
As mentioned earlier, the scribe notes and course project will be group assignments and collaboration with your group members is expected and strongly encouraged. How the groups are constructed is yet to be determined. All of your other submissions are expected to be individual work.

Academic Integrity
Your work on scribe notes and the course project may be collaborative in a limited manner, as described above. All of your other submissions are expected to be individual work. You are, of course, encouraged to discuss all aspects of your assignments with your instructors. You do need to be extremely careful about any other unsanctioned collaboration or help. For example, you are welcome to talk to classmates outside your groups about concepts and ideas that relate to the class, but avoid talking about specifics directly connected to the assignments. Also, any help you receive from online sources or books/articles that are not officially sanctioned should be carefully attributed.

If you are unclear about what constitutes academic dishonesty contact the instructor or consult the CLAS Code of Academic Honesty at http://clas.uiowa.edu/students/handbook/academic-fraud-honor-code.

Tardiness and Absences
Late submissions will not be accepted and make-up exams should not be expected. To get credit for assignments you should plan on turning in what you have on time. Having said this, we want to note that this course will follow the University and College of Liberal Arts and Sciences (CLAS) policies that require that students be allowed to make up missed examinations and assignments due to illness, certain University activities, circumstances beyond a student's control (such as a death in the family), or mandatory religious obligations. See http://clas.uiowa.edu/students/handbook/attendance-absences for more details on this policy. Attendance will not be marked, however there is a strong correlation between attendance and performance in courses. For most students, to be successful in this course will at the minimum require near-100% attendance. Beyond physical presence in the classroom, it will be expected that students will be fully engaged during lectures and enthusiastic particpants in classroom discussions.

The key to completing assignments on time is starting early and asking questions. We will be glad to help with any questions you may have on any of the assignments, either by e-mail or in person. So please feel free to visit us often during our office hours and if necessary, outside our office hours as well by making appointments.

Effort Level
According to University guidelines, a student should expect to work for 2 hours per week (outside the classroom) for each course credit. This is a 3 credit course and so you should expect to spend on average about 6 hours per week studying lecture notes and the textbook, solving homeworks, preparing for exams, etc. However, the "6 hours per week" estimate is an average and it also presupposes that you attend classes regularly, pay attention in class, visit me with your questions during my office hours, etc.

Nondiscrimination in the Classroom
UI is committed to making the classroom a respectful and inclusive space for all people irrespective of their gender, sexual, racial, religious or other identities. Toward this goal, students are invited to optionally share their preferred names and pronouns with their instructors and classmates. The University of Iowa prohibits discrimination and harassment against individuals on the basis of race, class, gender, sexual orientation, national origin, and other identity categories set forth in the University’s Human Rights policy. For more information, contact the Office of Equal Opportunity and Diversity at diversity@uiowa.edu or diversity.uiowa.edu.

Students with disabilities
UI is committed to an educational experience that is accessible to all students. A student may request academic accommodations for a disability (such as mental health, attention, learning, vision, and physical or health-related condition) by registering with Student Disability Services (SDS). The student should then discuss accommodations with the course instructor. For more information visit the website of Student Disability Services at http://sds.studentlife.uiowa.edu/.

Student Complaints
If you have any complaints or concerns about how the course is being conducted please feel free to talk to the instructors. Since one of the instructors is the department chair, you should see Prof. Jim Cremer (101P MacLean Hall, 319-321-1893, james-cremer@uiowa.edu) the previous department chair, if you have concerns about the course that you do not feel comfortable discussing with the instructors. Students may then bring the concern to CLAS. Consult the CLAS statement on Student Rights and Responsibilities at http://clas.uiowa.edu/students/handbook/student-rights-responsibilities for more information.

Administrative Home
This course is run by the Computer Science department which is part of the College of Liberal Arts and Sciences. This means that class policies on matters such as requirements, grading, add/drop deadlines, second-grade options and sanctions for academic dishonesty are governed by the College of Liberal Arts and Sciences. Students wishing to add or drop this course after the official deadline must receive the approval of the Dean of the College of Liberal Arts and Sciences. These policies vary by college https://clas.uiowa.edu/students/handbook.

Classroom Etiquette
Showing up to class late, leaving your cell phone ringer on, reading a newspaper in class, chatting with your friends, etc., can be quite distracting to fellow students and the instructor. If you are in class, it is your responsibility to pay attention and to make sure that you are not doing anything that makes it harder for fellow-students to pay attention. When disruptive activity occurs, a University instructor has the authority to determine classroom seating patterns and to request that a student exit immediately for the remainder of the period. One-day suspensions are reported to appropriate departmental, collegiate, and Student Services personnel (Office of the Vice President for Student Services and Dean of Students). For more information consult the CLAS statement on Student Rights and Responsibilities at http://clas.uiowa.edu/students/handbook/student-rights-responsibilities.

University Statement on Sexual Harassment
Sexual harassment subverts the mission of the University and threatens the well-being of students, faculty, and staff. All members of the UI community have a responsibility to uphold this mission and to contribute to a safe environment that enhances learning. Incidents of sexual harassment should be reported immediately. See the UI policy on sexual harassment at https://osmrc.uiowa.edu/ for assistance, definitions, and the full University policy.