R and statistical concepts used
- familiarity with R concepts used in the course
- to be completed once the course has been fully written but likely includes functions, accessing documentation, etc.
- understanding of statistical concepts used in the course
- to be completed once the course has been fully written but likely includes discrete and continuous probability distributions
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
\(R_t\) 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)
- familiarity with metrics for evaluating probabilistic forecasts and their properties
- ability to score probabilistic forecasts in R