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.
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
Jointly fitting multiple data streams
- understanding of why combining multiple surveillance streams can improve inference
- understanding of the difference between parallel (conditionally independent given infections) and sequential modelling of streams
- familiarity with extending the renewal-with-delays model to several parallel observation streams with their own delays, scalings and likelihoods
- understanding of the joint estimation of \(R_t\) and infections from cases, deaths and a wastewater signal
- awareness of the data-conflict problem and how a shared latent process maybe used to reconcile disagreeing streams
Combining nowcasting and forecasting (bridge to the forecasting course)
- 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
- familiarity with evaluating nowcasts and short-horizon forecasts using proper scoring rules (e.g. the Continuous Ranked Probability Score, CRPS)