Learning outcomes

The skills and methods taught in this course apply broadly across infectious disease epidemiology, from outbreak response to routine surveillance of endemic diseases.

Delay distributions

  • understanding of common probability distributions used for epidemiological delays
  • familiarity with using Stan to estimate parameters of a probability distribution
  • understanding of the ubiquity of delays in epidemiological data
  • familiarity with interpreting posterior distributions and quantifying parameter uncertainty

Biases in delay distributions

  • understanding of how interval censoring affects the estimation and interpretation of epidemiological delay distributions
  • understanding of right truncation in epidemiological data and its impact on real-time analysis
  • familiarity with statistical methods for adjusting delay estimates for censoring and truncation
  • understanding of how biases compound during exponential growth phases of epidemics

Using delay distributions to model the data generating process

  • understanding of using delay distributions to model population-level data generating processes
  • familiarity with convolutions to combine count data with individual probability distributions
  • understanding of double interval censoring and discretisation for population-level data
  • understanding of the need to introduce additional uncertainty to account for the observation process at a population level

\(R_t\) estimation and the renewal equation

  • understanding of the reproduction number and challenges in its estimation
  • understanding of the renewal equation as an epidemiological model for infection generation
  • familiarity with the generation time as a particular type of delay distribution
  • understanding of the role of the generative model in the estimation of \(R_t\)
  • familiarity with geometric random walk models for smoothing \(R_t\) estimates

Nowcasting

  • understanding of nowcasting as a particular right truncation problem
  • understanding of the difference between report date and event dates
  • familiarity with simple nowcasting using known delay distributions
  • familiarity with improving the model of the data generating process with geometric random walk models to improve nowcast performance in some circumstances

Joint nowcasting with unknown delays

  • understanding of the reporting triangle structure for epidemiological surveillance data
  • understanding of the benefits of joint estimation of delay distributions and nowcasts
  • understanding of population-level modelling with observation error
  • understanding of the link between Rt estimation and nowcasting

Forecasting

  • understanding of forecasting as an epidemiological problem
  • familiarity with ARIMA models for forecasting epidemiological time series
  • understanding of autocorrelation and partial autocorrelation functions for time series analysis
  • understanding of stationarity and data transformations for forecasting

Combining nowcasting and forecasting

  • understanding of the challenges of forecasting with incomplete data due to reporting delays
  • understanding of the link between nowcasting and forecasting
  • understanding of pipeline approaches for combining nowcasting and forecasting
  • understanding of joint approaches for simultaneous nowcasting and forecasting
  • understanding of the trade-offs between timeliness and completeness in real-time analysis

Evaluating forecasts (and nowcasts)

  • understanding of the four principles of good probabilistic forecasts: calibration, unbiasedness, accuracy, and sharpness
  • familiarity with scoring metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Continuous Ranked Probability Score (CRPS)
  • understanding of time-series cross-validation for forecast evaluation
  • familiarity with visual assessment of forecasts and nowcasts
  • understanding the challenges with, and possible solutions for, evaluating datasets with incomplete or missing forecast data

Ensemble models

  • understanding of predictive ensembles and their properties
  • familiarity with different forecast representation formats (samples, quantiles, bins)
  • understanding of linear opinion pools and Vincent averaging for ensemble methods
  • familiarity with hubverse data standards for collaborative forecasting

Collaborative modeling

  • understanding of modeling hubs and hubverse-style tools
  • developing and evaluating forecasting models using real epidemiological data
  • implementing time-series cross-validation for model assessment
  • generating and formatting forecasts for submission to a hub
  • submitting forecasts to a local and/or online hub, generating and interpreting evaluation metrics