Instructor: Jong-Hoon Kim
Office hours: Please email to make an appointment
This course will introduce basic theory of infection transmission, particularly the biological and socioligical background and mathematics and develop basic computational skills using mainly R to model infection transmission. The intended audience for this course is those who have backound in biology, sociology, but not necessarily in mathematics or computer programming.
Topics to be covered will include empirical studies of infectious disease outbreaks, models and theory, introduction to R, differential equations, and model fitting.
Students should have studied calculus before taking the course, and should in particular be comfortable with the solution of linear differential equations. In addition, a later substantial portion of the course, perhaps six weeks, will deal with computer methods to model infection transmission. Some experience with computer programming, especially R, will be a great help in understanding this part of the course.
There will be weekly graded problem sets, consisting of questions on both theory and applications. There will be three midterm exams but no final. The midterms will be in class at the usual time on October 13, November 10, and December 10.
There will be reading assignments for each lecture. The assignments are listed on the schedule below. Students are expected to do the reading for each lecture in a timely manner.
Coursepack (required): Modeling disease transmission, Jong-Hoon Kim, Oxford University Press, Oxford (2010)
In addition to this required coursepack, a list of other useful books is given below. None of them is required, but you may find them useful if you want a second opinion or more detail on certain topics.
General books on modeling disease transmission:
Books on specific modeling:
|Tuesday, Sept. 8||Introduction||Chapter 1||Info sheet|
|Thursday, Sept. 10||Technological and social networks||Chapters 2 and 3|
|Tuesday, Sept. 15||Information networks and biological networks||Chapters 4 and 5||Homework 1||Homework 1 handed out|
|Thursday, Sept. 17||Basic mathematics of networks||6.1-6.11|
|Tuesday, Sept. 22||Centrality, transitivity, assortativity||Chapter 7||Homework 2||Homework 1 due, Homework 2 handed out|
|Thursday, Sept. 24||Network structure and degree distributions||8.1-8.6|
|Tuesday, Sept. 29||Computer algorithms 1||Chapter 9||Homework 3, Network data, Internet data||Homework 2 due, Homework 3 handed out|
|Thursday, Oct. 1||Computer algorithms 2||10.1-10.4|
|Tuesday, Oct. 6||Random graphs 1||12.1-12.5||Homework 3 due, no new homework this week|
|Thursday, Oct. 8||Random graphs 2||12.6-12.8|
|Tuesday Oct. 13||Midterm 1||Homework 4||Homework 4 handed out, due Tuesday, Oct. 27|
|Thursday, Oct. 15||Configuration models 1||13.1-13.4|
|Tuesday, Oct. 20||No class||Fall Break|
|Thursday, Oct. 22||Configuration models 2||13.5-13.8|
|Tuesday, Oct. 27||Configuration models 3||13.9-13.11||Homework 5||Homework 4 due, Homework 5 handed out|
|Thursday, Oct. 29||Generative models 1||14.1-14.2|
|Tuesday, Nov. 3||Generative models 2||14.3-14.5||Homework 5 due, no new homework this week|
|Thursday, Nov. 5||Community structure||11.2-11.6|
|Tuesday, Nov. 10||Midterm 2||Homework 6||Homework 6 handed out|
|Thursday, Nov. 12||Spectral methods||11.7, 11.8|
|Tuesday, Nov. 17||Maximum likelihood methods|
|Thursday, Nov. 19||The expectation-maximization method||Homework 7, Blog network data||Homework 6 due, Homework 7 handed out|
|Tuesday, Nov. 24||Belief propagation and detectability|
|Thursday, Nov. 26||No class||Thanksgiving|
|Tuesday, Dec. 1||Percolation||Chapter 16||Homework 8||Homework 7 due, Homework 8 handed out|
|Thursday, Dec. 3||Epidemics on networks||17.1-17.8|
|Tuesday, Dec. 8||Network search||Chapter 19||Homework 8 due|
|Thursday, Dec. 10||Midterm 3||In class, usual time and place|