CECURE - Computational Epidemiology Curriculum Repository

Intro to Mathematical and Computational Epidemiology

MODULE (LABS ONLY)

This module introduces some simple population and epidemic models used in the study of population dynamics processes as well as some analytical tools for studying the qualitative properties of these models. This module includes labs for the (a) Ricker model, (b) SIR model, and (c) Bifurcation analysis. Find the labs here.

Data Science for Epidemiology

FULL COURSE (NO LABS)

This course covers the foundations of computational & networked epidemiology and data science algorithms & systems in the context of public health applications. The objective is to introduce students to this emerging multi-disciplinary domain. The course will touch on the following from an application viewpoint: (a) Foundations of modeling disease dynamics; (b) Calibration, Surveillance, and Forecasting of disease spread; (c) Detection, Reverse-engineering, and Control; and (d) Additional topics such as Phylodynamics, Tracing, and Data collection. From a methodology viewpoint, the course features (i) Non-linear systems, (ii) Network algorithms, (iii) Stochastic Optimization, (iv) ML and neural models for spatiotemporal, graphical, and social media data, (v) HPC simulations, and (vii) Visualization techniques. Find the overall course information here and the slides and readings here.

Computational Epidemics

MODULE (LECTURES ONLY)

The course provides a detailed theoretical analysis of the prominent epidemic spread models (i.e. the SI, SIS, SIR, and SIRS models), and guides students through simulations of their behavior in the general population, as well as, in a graph-theoretic setting. The course covers algorithms and metrics for effective vaccination to contain the spread of diseases and also examines the use of machine learning algorithms in predictive analytics of epidemic data. Find the overall course information here and the lecture recordings, etc here.

Computational Epidemiology and Public Health Policy Planning

MODULE (LECTURES ONLY)

This module provides an overview of computational epidemiology and public health policy planning with a focus on computing, AI concepts, and data science.  This module will cover (a) open problems and future directions in computational epidemiology, (b) a unified framework based on graphical dynamical systems and associated proof-theoretic techniques (e.g. stochastic processes, spectral graph theory, randomized algorithms, mathematical programming, and Bayesian inference), (c) the multidisciplinary aspects of computational epidemiology, and (d) how data/computational science can be applied to public health epidemiology for social good. Find the lectures here.

Generating Synthetic Populations for Social Modeling

MODULE (LECTURES ONLY)

This module provides an overview of the state-of-the-art in generating synthetic populations. It will discuss (a) how to synthesize populations, networks, and information; (b) challenges in the field; (c) how advances in computation and data are useful in advancing the field; (d) applications of synthetic populations; and (d) open problems and challenges in the area. Find the lectures here.

Machine Learning with Graphs

FULL COURSE WITH LABS

Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Find the lectures here and the slides and homework here.

An additional collection of Graph Machine Learning tutorial blog posts created by Stanford students as the capstone project of CS224W can be found here.