End of course summary

Nowcasting and forecasting of infectious disease dynamics

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 the companion course)

in real time.

Timeline

This was the nowcasting course (first half of the week):

  • delay distributions and how to estimate them
  • biases in delays: censoring and right truncation
  • using delays to model the data generating process
  • \(R_t\) estimation and the renewal equation
  • nowcasting, and joint estimation of delays and nowcasts
  • linking nowcasting and forecasting (bridge to the forecasting course)

Key takeaways

Delay distributions

  • delays play a fundamental role in nowcasting (and forecasting)
  • we characterise them with probability distributions
  • estimating delays requires correction for biases due to
    • double interval censoring (daily data)
    • right truncation (real-time analysis)

\(R_t\) estimation

  • \(R_t\) estimation using the renewal equation is a convolution problem
  • improving the generative model leads to improvements in estimation (geometric random walk vs. independent priors)
  • generation time is a key delay distribution for transmission dynamics
  • understanding the role of the generative model in the estimation of \(R_t\)

Nowcasting

  • nowcasting is the task of predicting what data will look once delays have resolved
  • it is a right truncation problem (same as discussed before)
  • a joint generative model can combine delay estimation, nowcasting and \(R_t\) estimation

Bridge: from nowcasting to forecasting

  • nowcasts feed naturally into forecasts of the near future
  • meaningful nowcasts and forecasts are probabilistic
  • we can assess them using proper scoring rules (e.g. CRPS)
  • the companion forecasting course (second half of the week) covers forecasting models, evaluation, ensembles, and hubs

Outlook

  • the methods introduced here have wide applications in infectious disease epidemiology
  • open-source tools are available to make this task easier in practice
  • a real-world example of collaborative nowcasting and forecasting: the European Respiratory Forecasting Hub

https://respicast.ecdc.europa.eu/

Feedback

  • please tell us if you enjoyed the course, what worked / didn’t work etc.
  • we will send out a survey for feedback

Thank you for attending!

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