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. While examples often use outbreak scenarios for clarity, participants should consider how these approaches apply to their epidemiological contexts.
R and statistical concepts used
- understanding of common probability distributions used for epidemiological delays
- familiarity with using stan to estimate parameters of a probability distribution
Delay distributions
- understanding of the ubiquity of delays in epidemiological data
- understanding of how delays affect population-level epidemiological data via discrete convolutions
- ability to apply convolutions of discrete probability distributions to epidemiological data in R
Biases in delay distributions
- understanding of how censoring affects the estimation and interpretation of epidemiological delay distributions
- ability to estimate parameters of probability distributions from observed delays, taking into account censoring, using R
- understanding of right truncation in epidemiolgical data
- ability to estimate parameters of probability distributions from observed delays, taking into account truncation, in R
Using delay distributions to model the data generating process
- understanding of using delay distributions to model population-level data generating processes
- ability to use convolutions to combine count data with a distribution of individual probabilities, adjusting continuous probability distributions with discretisation
- 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
- awareness of broad categories of methods for estimating the reproduction number, including estimation from population-level data
- understanding of the renewal equation as an epidemiological model
- awareness of connections of the renewal equation with other epidemiological models
- familiarity with the generation time as a particular type of delay distributions
- ability to estimate static and time-varying reproduction numbers from time-series data in R
Nowcasting
- understanding of nowcasting as a particular right truncation problem
- Ability to perform a simple nowcast in R
- awareness of the breadth of methods to perform nowcasting
- \(R_t\) estimation as a nowcasting problem
Forecasting
- understanding of forecasting as an epidemiological problem, and its relationship with nowcasting and \(R_t\) estimation
- understanding of the difference between forecasts, projections and scenarios
- familiarity with common forecasting models and their properties, and applicability in epidemiology
- ability to use a common forecasting model on an epidemiological time series in R
- ability to use a semi-mechanistic model for forecasting an epidemiological time series in R
Ensemble models
- understanding of predictive ensembles and their properties
- ability to create a predictive ensemble of forecasts in R
Evaluating forecasts (and nowcasts)
- ability to visually assess forecasts and nowcasts
- familiarity with metrics for evaluating probabilistic forecasts and their properties
- ability to score probabilistic forecasts in R