End of course summary and discussion
Introduction
We hope that you’ve enjoyed taking this course on understanding and evaluating forecasts of infectious disease burden. We will be very happy to have your feedback, comments or any questions on the course discussion page.
Slides
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.
- 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.
- Ribeiro et al., Post-processing and weighted combination of infectious disease nowcasts discusses the challenges in improving on the predictive performance of simpler approaches using weighted ensembles.