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.

Forecasting

  • Funk et al. (2019) evaluates the performance of a forecasting method that combines a mechanistic SEIR model with a random walk prior for the reproduction number.
  • Held et al. (2017) makes a compelling argument for the use of probabilistic forecasts and evaluates spatial forecasts based on routine surveillance data.
  • Lopez et al. (2024) describes the difficulty encountered in making accurate forecasts in COVID-19 cases in the US COVID-19 Forecast Hub.
  • Asher (2018) describes model that implements an extension to the random walk with a drift term.
  • CDCgov/Wastewater-Informed-Covid-Forecasting (2025), code repo for Wastewater-informed COVID-19 forecasting.
  • Hyndman and Athanasopoulos (2021) is a free online text book on forecasting with a range of time series models and a great resource for finding out more about them.

Ensembles

  • Sherratt et al. (2023) investigates the performance of different ensembles in the European COVID-19 Forecast Hub.
  • Amaral et al. (2025) discusses the challenges in improving on the predictive performance of simpler approaches using weighted ensembles.

References

Amaral, André Victor Ribeiro, Daniel Wolffram, Paula Moraga, and Johannes Bracher. 2025. “Post-Processing and Weighted Combination of Infectious Disease Nowcasts.” PLoS Computational Biology 21 (3): e1012836. https://doi.org/10.1371/journal.pcbi.1012836.
Asher, Jason. 2018. “Forecasting Ebola with a Regression Transmission Model.” Epidemics 22 (March): 50–55. https://doi.org/10.1016/j.epidem.2017.02.009.
CDCgov/Wastewater-Informed-Covid-Forecasting. 2025. Centers for Disease Control and Prevention.
Funk, Sebastian, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, and W. John Edmunds. 2019. “Assessing the Performance of Real-Time Epidemic Forecasts: A Case Study of Ebola in the Western Area Region of Sierra Leone, 2014-15.” PLOS Computational Biology 15 (2): e1006785. https://doi.org/10.1371/journal.pcbi.1006785.
Held, Leonhard, Sebastian Meyer, and Johannes Bracher. 2017. “Probabilistic Forecasting in Infectious Disease Epidemiology: The 13th Armitage Lecture.” Statistics in Medicine 36 (22): 3443–60. https://doi.org/10.1002/sim.7363.
Hyndman, Rob J, and George Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. OTexts.
Lopez, Velma K., Estee Y. Cramer, Robert Pagano, et al. 2024. “Challenges of COVID-19 Case Forecasting in the US, 2020–2021.” PLOS Computational Biology 20 (5): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
Sherratt, Katharine, Hugo Gruson, Rok Grah, et al. 2023. “Predictive Performance of Multi-Model Ensemble Forecasts of COVID-19 Across European Nations.” eLife 12 (April): e81916. https://doi.org/10.7554/eLife.81916.