The recent Ebola outbreak in west Africa and the ongoing Zika outbreak in South 
America are reminders of the age-old societal concerns of controlling the spread 
of infectious dis- eases. Examples of fundamental questions studied by epidemiologists 
and public officials during these, and other epidemic outbreaks, include: (1) Where did 
the disease originate? (2) How does it spread, and how does it compare with earlier 
outbreaks? (3) Where has it spread so far? (4) How can the disease spread be controlled? 

Mathematical models are key to understanding the spread of epidemics and studying 
such questions. There have been signi cant advances in the recent years on 
developing national and international scale epidemic models for Inuenza, Dengue, 
Zika and other diseases. We will focus on complex spatial and network based epidemic 
models which are used for studying disease spread at such scales. As researchers 
attempt to capture more realistic characteristics of disease dynamics into such models, 
they are becoming increasingly complex to analyze, calibrate, simulate and understand. 
Scalable computational and statistical methods are necessary for analyzing such models, 
and using them for real world problems. 

This course will cover recent research in computational epidemiology, which is at the 
interface of different areas of computer science, applied mathematics and statistics. 
The objective of the course is to introduce students to this multi-disciplinary area, 
and help them identify some of the key research directions. This is largely a seminar 
and project based course and students will be expected to participate in class discussion, 
present one or more papers, and work on a final project. Problems and datasets related 
recent outbreaks will be used in some of the course topics and projects, which include: 

1. Mathematical modeling in epidemiology: this will cover a broad class of models, 
   ranging from simple coupled differential equation models to network based models. 
   Our focus will be on analyzing dynamical properties of such models, and the impact 
   of network structure on the dynamics. 

2. Computing epidemic properties and simulations: as epidemic models increase in 
   complexity, their simulations and computing their properties are becoming 
   challenging computational problems. This topic will focus on some of the key 
   computational advances that have enabled the analysis of very large models. 

3. Calibration and inference problems for epidemic models: there is usually limited 
   amount of good quality data on outbreaks, and often sources such as social media 
   are used as indirect source to get signals about outbreaks. Calibration of complex 
   models, and state inference are challenging problems, and we will discuss some of 
   the recent research on these topics. 

4. Control and resource optimization in public health: this will include problems 
   from public health policy planning, such as detection and control of epidemic spread. 
   We will discuss combinatorial formulations for different kinds of multi-criteria 
   optimization problems, as well as game-theoretical approaches that capture individual 
   incentives. We will also discuss briey the issues of designing implementable policies, 
   and the effect of individual behavior on the efficacy of interventions. 

5. Forecasting problems in epidemiology: This involves forecasting the time course 
   of an epidemic, and has attracted a lot of interest in recent years. There are 
   forecasting challenges run by di erent agencies. This problem is complicated by 
   the fact that behaviors, disease dynamics, and social networks co-evolve. We will 
   discuss some recent results on this topic, including the results from forecasting 
   challenges. 

Background and Prerequisites. The course is highly multi-disciplinary, and all the topics will
span multiple areas. Students should have background in at least one of the following areas: data
analytics, algorithms, parallel computation, mathematical modeling, statistics and optimization.
Students are encouraged to work in teams with complementary expertise, allowing them to explore
new areas.