team_name: Swiss Data Science Center / University of Geneva model_name: Trend Model model_abbr: SDSC_ISG-TrendModel model_contributors: - name: Ekaterina Krymova affiliation: Swiss Data Science Center - name: Dorina Thanou affiliation: Center for Intelligent Systems, EPFL - name: Benjamin Bejar Haro affiliation: Swiss Data Science Center - name: Tao Sun affiliation: Swiss Data Science Center email: tao.sun@datascience.ch - name: Gavin Lee affiliation: Swiss Data Science Center - name: Elisa Manetti affiliation: University of Geneva - name: Christine Choirat affiliation: Swiss Data Science Center - name: Antoine Flahault affiliation: University of Geneva - name: Guillaume Obozinski affiliation: Swiss Data Science Center website_url: https://renkulab.shinyapps.io/COVID-19-Epidemic-Forecasting/ license: cc-by-4.0 team_model_designation: primary methods: The Trend Model predicts daily cases and deaths using linear extrapolation on the linear or log scale of the underlying trend estimated by a robust LOESS seasonal-trend decomposition model. repo_url: https://renkulab.io/gitlab/covid-19/covid-19-forecast data_inputs: JHU CSSE (confirmed cases; reported fatalities) citation: https://renkulab.shinyapps.io/COVID-19-Epidemic-Forecasting/ methods_long: Our forecasts are based on the reported numbers of cases and deaths at the country or regional level. Our modeling substantially relies on estimation of the underlying trend by a robust LOESS seasonal-trend decomposition model, which allows to account for non-stationary weekly seasonality, outliers, missing data and delayed reports. To further predict daily cases and deaths we use linear extrapolation of the estimated smooth trend either on the original or on the logarithmic scale.