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Short-term forecasting of the Coronavirus Pandemic - 2020-04-27

Jennifer Castle, Jurgen Doornik and David Hendry

No 2020-W06, Economics Papers from Economics Group, Nuffield College, University of Oxford

Abstract: We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 online at www.doornik.com/COVID-19 from mid-March 2020. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts than some epidemiological models.

Keywords: Autometrics; Cardt; COVID-19; Epidemiology; Forecasting; Forecast averaging; Machine learning; Smoothing; Trend Indicator Saturation. (search for similar items in EconPapers)
Pages: 13 pages
Date: 2020-04-27
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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