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.

References

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.
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.