Introduction to forecasting

Nowcasting and forecasting of infectious disease dynamics

Forecasting in infectious disease epidemiology

Metcalf & Lessler, Science, 2017

Modelling the future can help with …


… decision making

  • how many beds do we need?
  • where should we allocate vaccines?
  • where should we trial vaccines?

…situational awareness

  • nowcast: where are we now?
  • forecast: where are we heading?


Warning

But…

  • not all modelling is prediction
  • not all modelling of the future is forecasting

Different ways of modelling the future

  • Nowcasts make statements about current trends based on partial data

  • Forecasts are unconditional statements about the future: what will happen

  • Scenarios state what would happen under certain conditions

Figure credit: Scenario Modeling Hub

CDC use of influenza forecasts

CDC: About Flu Forecasting

Forecasting: relationship with \(R_t\) estimation

  • In infectious disease epidemiology, many relevant interventions and other changes affect the strength of transmission
  • Things that affect transmission don’t affect the predicted outcomes directly, but via \(R_t\)
  • In that sense, predicting infections comes down to predicting \(R_t\)
  • Commonly, forecast models assume no change in \(R_t\). Is this a good assumption?

Forecasting: relationship with nowcasting

  • Nowcast: we have some data from the dates we’re predicting
  • Forecast: we have no data from the dates we’re predicting (usually b/c they’re in the future)

Figure credit: Scenario Modeling Hub

Forecasts are often probabilistic

  • Because the future is uncertain, it is natural to express predictions in probabilities, e.g. there is a X% chance of exceeding Y hospital admissions.

Probabilistic epi forecasts

Figure credit: Evan Ray

The epi forecasting philosophical debate

Camp A

“It’s a machine learning problem! We can gather enough data to make good predictions, and we don’t really have to understand the underlying processes that well.”


Camp B

“We have clear theory that explains transmission of pathogens in a networked population. I don’t need to confront my model’s statements with data.”


What camp are you in?

Exhibit from Camp A:

xkcd

Your Turn

  1. Learn about and explore seasonal influenza-like illness data from US CDC.
  2. Build a simple forecast model and understand how it is represented in R.

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