End of course summary and discussion

Introduction

We hope that you’ve enjoyed taking this course on nowcasting and forecasting infectious disease dynamics. We will be very happy to have your feedback, comments or any questions on the course discussion page.

Slides

End of course summary

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.

Delay estimation

• Park et al., Estimating epidemiological delay distributions for infectious diseases provides a comprehensive overview of challenges in estimating delay distribution and how to overcome them.
• Charniga et al., Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data summarises challenges in estimating delay distributions into a set of best practices.

\(R_t\) estimation

• Gostic et al., Practical considerations for measuring the effective reproductive number, \(R_t\) provides an overview of some of the challenges in estimating reproduction numbers.
• Brockhaus et al., Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany compares reproduction number estimates from different models and investigates their differences.

Nowcasting

• Wolffram et al., Collaborative nowcasting of COVID-19 hospitalization incidences in Germany 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., Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates 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., A Powerful Modelling Framework for Nowcasting and Forecasting COVID-19 and Other Diseases contains a nice review of different methods for nowcasting evaluates a range of methods, in addition to introducing a new approach.

Forecasting

• Funk et al., Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15 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, Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture makes a compelling argument for the use of probabilistic forecasts and evaluates spatial forecasts based on routine surveillance data.
• Lopez et al., Challenges of COVID-19 Case Forecasting in the US, 2020-2021 describes the difficulty encountered in making accurate forecasts in COVID-19 cases in the US COVID-19 Forecast Hub.
• Sherratt et al., Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations investigates the performance of different ensembles in the European COVID-19 Forecast Hub.
• Asher, Forecasting Ebola with a regression transmission model describes model that implements an extension to the random walk with a drift term.
• CDC, code repo for Wastewater-informed COVID-19 forecasting.
• Hyndman and Athanasopoulos, Forecasting: Principles and Practice (free online text book) is a textbook on forecasting with a range of time series models and a great resource for finding out more about them.