Welcome! This repository contains all materials from the Canadian Ecological Forecasting Initiative short course on Forecasting for Decision-Making: An Epidemiological & Ecological Perspective. The course took place at the Fields Institute in Toronto, ON from July 24th - 28th 2023.
In the spirit of having this course be as open as possible, we have put all the course materials here on this GitHub page, including lectures, exercises and forecast modelling materials for three case studies: Infectious Disease Control, Fisheries Management, Water Quality Monitoring. For a description of the course's overall objectives, please see here.
However, please note that the content here does not all belong exclusively to CEFI. CEFI-specific content presented here is governed by a CC-BY 4.0 licence, but all other content herein is owned by it's original creator and CEFI does not hold rights or permissions.
For any questions about the course or about this repository please email [email protected].
This work is licensed under a Creative Commons Attribution 4.0 International License.
All code and documentation herein is free to use! If you were unable to make it to the course in person, or simply want to go through this material on your own time, please feel free. This repository is structured into 3 main sections.
First, is the Lectures folder, where the various lecture materials can be found for the course. These lectures were recorded, and the recording videos can be found on the EFI YouTube channel.
Here is the course schedule. We suggest you roughly follow along with the order of lectures here.
| Time Slots | Monday | Tuesday | Wednesday | Thursday | Friday | ||
|---|---|---|---|---|---|---|---|
| 9:00 - 10:00 |
Lecture 1 | Topic(s): (1) Introductions & Schedule Overview; (2) Introduction to Forecasting Lead(s): (1) KB, CRF & Instructors; (2) Mike Dietze |
Topic: Bayesian Analysis - Part 2 Instructor: Mike Dietze |
Topic: Model Assessment Instructor: Mike Irvine |
Topic: Delivering Forecasting Models to Decision Makers Instructor: Colin Daniel and Alex Filazzola |
Topic: OCAP Training Part 1 Instructor: OCAP |
|
| 10:00 - 10:20 |
Coffee Break | ||||||
| 10:20 - 11:20 |
Lecture 2 | Topic: Introduction to the Modelling Landscape Instructor: Irena Papst |
Topic: Reproducibility & Transparency Instructor: Mike Irvine |
Topic: Combining Fish Population Forecasting with Fisheries Management: an Introduction to Management Strategy Evaluation (MSE) Instructor: Brooke Davis |
Topic: Experiences Building Collaborations and Bridging Communication Instructor: Brooke Davis + Other Instructors |
(1) Group Work: Finalize Presentation (2) Overview of NEON Ecological Forecasting Challenge Lead: (2) Quinn Thomas |
|
| 11:20 - 11:30 |
Break | ||||||
| 11:30 - 12:30 |
Extra Practice or Lecture 3: |
Topic: Bayesian Analysis - Part 1 Instructor: Mike Dietze |
Exercise 2: Paired Coding Lead: Mike Dietze |
Exercise 3: MSE Exercise Lead: Brooke Davis |
Topic: Decision Analysis in Health Instructor: Beate Sander |
Group Work: Finalize Presentation Closing Remarks |
|
| 12:30 - 1:30 |
Lunch | ||||||
| 1:30 - 3:00 |
Extra Practice or Lecture 3: |
Exercise 1: Introduction to Bayesian Analysis Lead(s): Mike Dietze + Mike Irvine |
Topic(s): (1) Code Review Example; (2) Propagating, Analyzing, & Reducing Uncertainty Instructor: Mike Dietze + Irena Papst |
Group work | Exercise 4: Writing Lay Summaries Exercise Lead: Korryn Bodner and Carina Rauen Firkowski |
Group Project Presentations Part 1 |
|
| 3:00 - 3:20 |
Coffee Break | ||||||
| 3:20 - 5:30 |
Group Work | Case Study Introductions (30 mins) Case Study Overviews (in small groups) Lead(s): All Instructors |
Group work | Group work | Group work | Group Project Presentations Part 2 End at 3:50pm |
|
| 6:00 - 8:00 |
Group Dinner |
There are a series of exercises in the Exercises folder for you to work through. Instructions are in the README_EXERCISES.md file in that directory for how to make use of those.
There are a number of exercises that span a couple of programming approaches to the current course content.
Perhaps most importantly, we have our four case studies. A focal point of this course is having students take a case study of interest, and work on building and expanding a forecast for a focal system. In this iteration of the course there are four case studies, one on fisheries, one on water quality, and two on COVID-19. Each of the COVID-19 case studies are from different locations (Ontario and BC respectively) and take slightly different approaches.
There are extensive documentation for each of the case studies in their respective folders, along with troubleshooting tips. Please take the time to read through each case study before starting, as each of them have slightly different software requirements. During the in-person version of this course, each case study had two teams of four students working on each one. They have tackled some of the "Decision making problems" that the instructors suggested as ways the models could be extended. You're welcome to look through their code and results as examples of how each group chose to tackle the problem.