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

Nowcasting and forecasting of infectious disease dynamics

“We were losing ourselves in details […] all we needed to know is, are the number of cases rising, falling or levelling off?”

Hans Rosling, Liberia, 2014

  • what is the number of cases now?
  • is it rising/falling and by how much?
  • what does this mean for the near future?

Data usually looks like this

Aggregated data can look like this

UKHSA, 2022
Overton et al., PLOS Comp Biol, 2023

Aim of this course:

How can we use data typically collected in an outbreak to answer questions like

  • what is the number of cases now? (nowcasting)
  • is it rising/falling and by how much? (\(R_t\) estimation)
  • what does this mean for the near future (forecasting)

in real time.

Approach

Throughout the course we will

  1. simulate typical infectious disease data in R
    (the generative model)
  2. apply the generative model to data in stan to
    • learn about the system (conduct inference)
    • make predictions (nowcasting/forecasting)

Timeline

  • delay distributions and how to estimate them (day 1)
  • \(R_t\) estimation and the generation interval (day 1)
  • nowcasting (day 2)
  • forecasting and evaluation, ensemble methods (day 2)
  • applications (day 3)

To start the course go to: https://nfidd.github.io/nfidd/ and get started on the first sesion (Probability distributions and parameter estimation)

Return to the session

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