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.
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
Evaluating forecasts
- 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
- 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