| Name: | EpiInvert |
|---|---|
| Abbreviation: | AMM-EpiInvert |
| Owner: | epiforecasts |
| Team name: | AMM |
| Description: | Learning from the past a short time forecast of the COVID-19 incidence curve trend. |
| Contributors: | [{'name': 'Luis Alvarez', 'affiliation': 'Universidad de Las Palmas de Gran Canaria, Spain', 'email': 'lalvarez@ulpgc.es'}, {'name': 'Jean-David Morel', 'affiliation': 'Ecole Polytechnique Fédérale de Lausanne, Switzerland', 'email': 'jeandavid.morel@gmail.com'}, {'name': 'Jean-Michel Morel', 'affiliation': 'Université Paris-Saclay, France', 'email': 'moreljeanmichel@gmail.com'}] |
| License: | Creative Commons Attribution 4.0 |
| Notes: | primary |
| Citation: | https://doi.org/10.1073/pnas.2105112118 , https://doi.org/10.3390/biology11040540 , https://ctim.ulpgc.es/covid19/EpiInvertForecast.html |
| Methods: | We design a learning procedure, EpiInvertForecast, to forecast the future trend of the daily incidence. This learning procedure is based on a database of 27,418 incidence trend curves computed in the past, with EpiInvert, using real data. The forecast of the current incidence trend is obtained as a weighted average of the 27,418 database curves where the weight of each database curve depends on the similarity between the current curve and the database curve in the past. We also compute empirical confidence intervals for the forecast estimation and, using the weekly seasonality computed by EpiInvert, we also estimate a forecast of the original incidence curve. |
| Home: | https://github.com/lalvarezmat/EpiInvert |
| Auxiliary data: | NA |
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