Template-type: ReDIF-Paper 1.0 Author-Name: Jennifer L. Castle Author-Workplace-Name: Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Magdalen College, University of Oxford Author-Email: jennifer.castle@magd.ox.ac.uk Author-Name: Jurgen A. Doornik Author-Workplace-Name: Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford Author-Email: jurgen.doornik@nuffield.ox.ac.uk Author-Name: David F. Hendry Author-Workplace-Name: Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford Author-Email: david.hendry@nuffield.ox.ac.uk Title: Short-term forecasting of the Coronavirus Pandemic - 2020-04-27 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. Length: 13 pages Creation-Date: 2020-04-27 Number: 2020-W06 File-URL: https://www.nuffield.ox.ac.uk/economics/Papers/2020/2020W06_COVID-19_shortterm_forecasts.pdf File-Format: application/pdf Handle: RePEc:nuf:econwp:2006