Abbott, S and Sherratt, K. 2022. seabbs/ecdc-weekly-growth-forecasts. [Online]. Zenodo. Available from: https://doi.org/10.5281/zenodo.7189620
Abbott, S and Sherratt, K. seabbs/ecdc-weekly-growth-forecasts [Internet]. Zenodo; 2022. Available from: https://doi.org/10.5281/zenodo.7189620
Abbott, S and Sherratt, K (2022). seabbs/ecdc-weekly-growth-forecasts. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.7189620
Alternative Title
COVID-19 cases forecasts for the ECDC Forecast Hub
Description
A Bayesian autoregressive model using weekly incidence data designed to run as a Github action. Both cases and the growth rate are assumed to be AR(1) processes with the growth rate being differenced and scaled by a decay parameter. The model is implemented using the forecast.vocs R package.
Keywords
| Data capture method | Other |
|---|---|
| Date (Date published in a 3rd party system) | 12 October 2022 |
| Language(s) of written materials | English |
| Data Creators | Abbott, S and Sherratt, K |
|---|---|
| LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology |
| Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
| Date Deposited | 13 Oct 2022 09:12 |
|---|---|
| Last Modified | 13 Oct 2022 09:12 |
| Publisher | Zenodo |
