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 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 (NO 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.

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

INTERESTED IN SUBMITTING YOUR CONTENT? 

We Need Help Building Our Courseware Repository!

We are excited to share educational resources that instructors can use in the classroom. All resources will be sorted into the following categories:

  • MODULE: A module should consist of 3 – 6 lessons of related material and can be stand-alone or part of a course. All modules include a module description document with a brief description of the module and a list of lessons, labs, and homework.

  • SMALL EXERCISE: A small exercise is for an individual or small team (or multiple teams) to complete over a few hours. In terms of scope, a small exercise might be likened to a capstone laboratory event in a college course.

  • ONE-DAY (8-HOUR) OR TWO-DAY (16-HOUR) COURSE: One- and two-day courses are intended to be approximately 50% lecture/discussion and 50% hands-on exercises covering a specific computational epidemiology topic.

  • SINGLE HANDS-ON LABORATORY: Hands-on exercise intended as a laboratory event to reinforce a specific concept or concepts, normally as part of a specific computational epidemiology course. Would include a scenario description, detailed instructions for participants, separate instructions for course instructors administering the exercise, as well as a grading rubric.

  • FULL COURSE WITH LABS: Course content to support a full, one semester, 3.0 credit hour (or more) course with syllabus, individual lesson plans, presentation slides, homework, exams, and hands-on labs with necessary documentation. All courses should be broken down in modules so that educators using the content can select them à la carte from various content offerings to build a course or workshop.

  • FULL COURSE (NO LABS): Course content to support a full, one semester, 3.0 credit hour (or more) course with syllabus, individual lesson plans, presentation slides, homework, and exams. All courses should be broken down in modules so that educators using the content can select them à la carte from various content offerings to build a course or workshop.

To submit materials to CECURE, please email [email protected]!