Introduction and course overview

From an epidemiological line list to informing decisions in real-time

Note

This is the Forecasting & evaluation course, the second half of the SISMID week. The companion Nowcasting & \(R_t\) estimation course covers the first half (delay distributions, the renewal equation, and nowcasting).

Aim of the course

In this course we will address how we can use data typically collected in an outbreak, or in routine surveillance, to answer questions like

• What does the recent trend mean for the near future (forecasting)?
• How good are our predictions, and how can we tell (forecast evaluation)?
• How can we combine predictions from multiple models (ensembles), and contribute them to a shared forecasting effort (collaborative modelling / hubs)?

To answer these questions, we need to understand the epidemiological processes that create the kinds of data that we typically have available for outbreak analysis and infectious disease surveillance, and the principles of good probabilistic prediction.

There are particular challenges when trying to do these analyses in real time (i.e. whilst transmission and data collection is ongoing) rather than retrospectively, which we will address in turn.

In this course, we focus on predicting the future (forecasting), evaluating how good those predictions are, combining models into ensembles, and contributing forecasts to collaborative modelling hubs.

Why this course?

• These are common questions in outbreak response and disease surveillance
• Accounting for underlying processes can get surprisingly complicated quickly ¹, and it’s easy to make mistakes
• There’s currently (at the time of devising this course) no comprehensive training resource that links these common questions and challenges

Approach

Throughout the course we will

1. work with real epidemiological surveillance data in R
2. fit time-series forecasting models, make predictions, and evaluate them using proper scoring rules
3. combine models into ensembles and contribute forecasts to collaborative modelling hubs

Each session in the course:

• builds on the previous one so that participants will have an overview of the real-time analysis workflow by the end of the course;
• starts with a short introductory talk;
• mainly consists of interactive content that participants will work through;
• has optional/additional material that can be skipped or completed after the course ends;

For those attending the in-person version the course also:

• has multiple instructors ready to answer questions about this content; if several people have a similar question we may pause the session and discuss it with the group;
• follows a stop-and-review approach where we pause after each section of self-guided material to discuss and review together and address any questions;
• ends with a wrap-up and discussion where we review the sessions material.

Timeline for the course

This forecasting & evaluation course runs over the second half of the SISMID week (Wednesday midday to Friday), but of course if you are studying this on your own using the web site you can go through the material at your own pace and in your own time. Broadly, the intended timeline is:

• forecasting concepts and models (day 1)
• forecast evaluation and ensembles (day 2)
• collaborative forecasting with hubs and course wrap-up (day 3)

Let’s get started!

• Have a more detailed look at the learning objectives

• Introduction to the course and the instructors

• If you haven’t already, start with getting started with the course

• Once you’re all set up, let’s start with the first session on forecasting concepts.

Footnotes

1. Time travel is messy stuff