COVID-19 short-term forecasts Deaths 2020-03-20


Disclaimer

  • Forecasts produced by Jennie Castle, Jurgen Doornik, and David Hendry, researchers at the University of Oxford. These are our forecasts, and should not be considered official forecasts from, or endorsed by, any of: University of Oxford, Oxford Martin School, Nuffield College, Magdalen College, or any other University of Oxford institute.
  • These forecasts are short term time-series extrapolations of the data. They are not based on epidemiological modelling or simulations. The documentation that is provided is still in progress and not peer reviewed. All forecasts are uncertain: their success can only be determined afterwards. Many mitigation strategies are in place, which, if successful, invalidate our forecasts. An explanation of our methods is provided below.

Further information

  • We believe these forecasts fill a useful gap in the short run. They give an indication of what is likely to happen in the next few days, removing some aspect of surprise. Moreover, a noticeable drop in comparison to the extrapolations could be an indication that the implemented policies are having some impact. It is difficult to understand exponential growth. We hope that these forecasts may help to convince viewers to adhere to the policies implemented by their respective governments, and keep all arguments factual and measured.
  • We use the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering. This is updated daily, but it seems that our forecasts need slightly less frequent updating.
    US state data as of 2020-03-28 is courtesy of the New York Times. This seems to be a day behind the Johns Hopkins data.
  • We can only provide forecasts of what is measured. If confirmed cases are an underestimate of actual cases, then our forecasts will also be underestimates. Again: no other epidemiological data is used.
  • We will probably revise or update the methodology as we learn what is happening in the next few days or weeks.
  • Once the number of cases levels off, there is no need to provide these forecasts anymore.
  • Countries where the counts are very low or stable have been omitted.
  • We will probably revise or update the methodology as we learn what is happening in the next few days or weeks.
  • Once the number of cases levels off, there is no need to provide these forecasts anymore.
  • Countries where the counts are very low or stable have been omitted.
  • The graphs have dates on the horizontal axis (yyyy-mm-dd) and cumulative counts on the vertical axis. They show
    1. bold dark grey line (with circles): observed counts (Johns Hopkins CSSE);
    2. many light grey lines (with open circles): forecasts using different model settings and starting up to four periods back;
    3. red line (with open circles): single forecasts path using default model settings;
    4. black line (with crosses): average of all forecasts, recentered on the last observation;
    5. thin green lines: some indication of uncertainty around the red forecasts, but we do not know how reliable that is.
    Both the red line forecasts and the black lines are also given in the tables above. These forecasts differ, we are currently inclined to use the average forecasts.
  • The forecasts are constructed as follows:
    1. An overall `trend' is extracted by taking a window of the data at a time. In each window we draw `straight lines' which are selected using an automatic econometric procedure (`machine learning'). All straight lines are collected and averaged, giving the trend.
    2. Forecasts are made using the estimated trend, but we note that this must be done carefully, because simply extrapolating the flexible insample trend would lead to wildly fluctuating forecast. We use the `Cardt' method, which has been found to work well in other settings.
    3. Residuals from the trend are also forecasted, and combined with trend forecasts into an overall forecast.
  • EU-BS is Estonia, Latvia, and Lithuania together.
  • This paper describes the methodology and gives further references, but is still preliminary.

Deaths count forecast average (bold black line in graphs) 2020-03-21 to 2020-03-25

DateUKEUBEDEESFRITNLCH
2020-03-20 177 5799 37 67 1043 450 4032 106 54
2020-03-21 230 6900 50 80 1280 560 4660 130 70
2020-03-22 290 7900 50 90 1550 630 5290 160 80
2020-03-23 360 9100 60 110 1870 720 6020 190 100
2020-03-24 450 10600 70 120 2260 830 6840 230 110
2020-03-25 570 12200 90 140 2740 970 7790 290 140

Deaths count forecast average (bold black line in graphs) 2020-03-21 to 2020-03-25

DateIranUS
2020-03-20 1433 244
2020-03-21 1610 300
2020-03-22 1800 340
2020-03-23 2020 400
2020-03-24 2260 470
2020-03-25 2530 540

Deaths count forecast (bold red line in graphs) 2020-03-21 to 2020-03-25

DateUKEUBEDEESFRITNLCH
2020-03-20 177 5799 37 67 1043 450 4032 106 54
2020-03-21 240 7100 40 80 1230 600 4860 120 70
2020-03-22 310 8600 50 100 1430 820 5760 150 80
2020-03-23 410 10400 70 130 1660 1140 6770 180 90
2020-03-24 520 12500 90 160 1930 1550 7950 220 110
2020-03-25 670 15100 110 190 2240 2110 9330 270 130

Deaths count forecast (bold red line in graphs) 2020-03-21 to 2020-03-25

DateIranUS
2020-03-20 1433 244
2020-03-21 1630 310
2020-03-22 1840 390
2020-03-23 2070 480
2020-03-24 2320 590
2020-03-25 2610 720

Initial visual evaluation of forecasts of Deaths