Further reading

The following is a highly subjective list of papers we would recommend to read for those interested in engaging further with the topics discussed here. You can also access this via Zotero in an open living library, which you are welcome to contribute to.

Delay estimation

  • Park et al. (2024) provide a comprehensive overview of challenges in estimating delay distribution and how to overcome them.
  • Charniga et al. (2024) summarises challenges in estimating delay distributions into a set of best practices.

\(R_t\) estimation

  • Gostic et al. (2020) provides an overview of some of the challenges in estimating reproduction numbers.
  • Brockhaus et al. (2023) compares reproduction number estimates from different models and investigates their differences.

Nowcasting

  • Wolffram et al. (2023) compares the performance of a range of methods that were used in a nowcasting hub and investigates what might explain performance differences.
  • Lison et al. (2024) develops a generative model for nowcasting and \(R_t\) estimation and compares its performance to an approach where the steps for estimating incidence and reproduction number are separated.
  • Stoner et al. (2020) contains a nice review of different methods for nowcasting evaluates a range of methods, in addition to introducing a new approach.

Modelling workflow

  • Abbott et al. (2026) sets out the workflow we follow when we jointly fit multiple data sources, from research question through process model and observation models to inference and model checking.
  • Gelman et al. (2026) is the book behind that workflow. It describes the general Bayesian workflow, of which the infectious disease workflow is one instance, and goes into much more depth on prior and posterior predictive checks, model comparison and what to do when a model fails to fit. Read it if you want the reasoning behind the steps we take in the course.

Packages we use

The course teaches Stan models written from scratch, but the same ideas are packaged up in tools you can use directly.

  • nfidd.nowcasting is the course package. It holds the data, the helper functions and every Stan model shown in the sessions (see the Stan model index).
  • cmdstanr is the R interface to Stan we use to compile and fit all of our own models.
  • epinowcast is the nowcasting package we use in the session on complex reporting processes. It fits the reporting triangle models we build by hand, plus day of week effects, batch reporting and time-varying delays.
  • primarycensored handles the censoring and truncation corrections from the delay distribution sessions.
  • scoringutils scores probabilistic nowcasts and forecasts, as used when we combine nowcasting and forecasting.
  • EpiNow2 is an \(R_t\) estimation and forecasting package built on the renewal equation models we develop.
  • EpiAware is a Julia package that builds the same models from composable parts, and is where much of this work is heading.

References

Abbott, Sam et al. 2026. A Workflow for Infectious Disease Modelling. Manuscript and code repository. https://github.com/seabbs/a-workflow-for-infectious-disease-modelling.
Brockhaus, Elisabeth K., Daniel Wolffram, Tanja Stadler, et al. 2023. “Why Are Different Estimates of the Effective Reproductive Number so Different? A Case Study on COVID-19 in Germany.” PLOS Computational Biology 19 (11): e1011653. https://doi.org/10.1371/journal.pcbi.1011653.
Charniga, Kelly, Sang Woo Park, Andrei R. Akhmetzhanov, et al. 2024. “Best Practices for Estimating and Reporting Epidemiological Delay Distributions of Infectious Diseases.” PLOS Computational Biology 20 (10): e1012520. https://doi.org/10.1371/journal.pcbi.1012520.
Gelman, Andrew, Aki Vehtari, Richard McElreath, et al. 2026. Bayesian Workflow. Chapman & Hall/CRC Press. https://avehtari.github.io/Bayesian-Workflow/.
Gostic, Katelyn M., Lauren McGough, Edward B. Baskerville, et al. 2020. “Practical Considerations for Measuring the Effective Reproductive Number, Rt.” PLOS Computational Biology 16 (12): e1008409. https://doi.org/10.1371/journal.pcbi.1008409.
Lison, Adrian, Sam Abbott, Jana Huisman, and Tanja Stadler. 2024. “Generative Bayesian Modeling to Nowcast the Effective Reproduction Number from Line List Data with Missing Symptom Onset Dates.” PLOS Computational Biology 20 (4): e1012021. https://doi.org/10.1371/journal.pcbi.1012021.
Park, Sang Woo, Andrei R. Akhmetzhanov, Kelly Charniga, et al. 2024. Estimating Epidemiological Delay Distributions for Infectious Diseases. medRxiv. https://doi.org/10.1101/2024.01.12.24301247.
Stoner, Oliver, Theo Economou, and Alba Halliday. 2020. A Powerful Modelling Framework for Nowcasting and Forecasting COVID-19 and Other Diseases. arXiv:1912.05965. arXiv. https://doi.org/10.48550/arXiv.1912.05965.
Wolffram, Daniel, Sam Abbott, Matthias an der Heiden, et al. 2023. “Collaborative Nowcasting of COVID-19 Hospitalization Incidences in Germany.” PLOS Computational Biology 19 (8): e1011394. https://doi.org/10.1371/journal.pcbi.1011394.