seabbs/ecdc-weekly-growth-forecasts
Abbott, S
and Sherratt, K
(2022).
seabbs/ecdc-weekly-growth-forecasts.
[Dataset].
Zenodo.
https://doi.org/10.5281/zenodo.7189620
and Sherratt, K
(2022).
seabbs/ecdc-weekly-growth-forecasts.
[Dataset].
Zenodo.
https://doi.org/10.5281/zenodo.7189620
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.
Alternative Title
COVID-19 cases forecasts for the ECDC Forecast Hub
Keywords
COVID-19 cases forecasts; COVID-19; Coronavirus| Item Type | Dataset |
|---|---|
| Resource Type |
Resource Type Resource Description Software R script |
| Capture method | Other |
| Date | 12 October 2022 |
| Language(s) of written materials | English |
| Creator(s) |
Abbott, S and Sherratt, K
|
| LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology (-2023) |
| 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 |
No files available. Please consult associated links.
