COVID-19 short-term forecasts Deaths 2020-03-22


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 The documentation that is provided is still in progress and not peer reviewed.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) 2020-03-23 to 2020-03-27

DateUKEUBEDEESFRITNLCH
2020-03-22 281 8388 75 94 1772 674 5476 179 98
2020-03-23 370 9900 90 120 2160 850 6300 220 120
2020-03-24 470 11400 120 140 2560 1030 7100 270 140
2020-03-25 610 13200 140 170 3050 1250 8100 320 160
2020-03-26 780 15400 170 200 3630 1530 9200 390 190
2020-03-27 1000 17800 210 250 4340 1900 10500 480 220

Deaths count forecast average (bold black line in graphs) 2020-03-23 to 2020-03-27

DateIranUS
2020-03-22 1685 417
2020-03-23 1850 520
2020-03-24 2040 610
2020-03-25 2260 730
2020-03-26 2500 880
2020-03-27 2760 1050

Deaths count forecast (bold red line in graphs) 2020-03-23 to 2020-03-27

DateUKEUBEDEESFRITNLCH
2020-03-22 281 8388 75 94 1772 674 5476 179 98
2020-03-23 380 9800 90 110 2180 840 6200 220 110
2020-03-24 490 11400 110 130 2620 1020 7100 260 140
2020-03-25 620 13200 140 150 3130 1250 8100 310 160
2020-03-26 770 15400 180 170 3730 1520 9100 360 190
2020-03-27 950 17900 220 190 4430 1840 10400 430 230

Deaths count forecast (bold red line in graphs) 2020-03-23 to 2020-03-27

DateIranUS
2020-03-22 1685 417
2020-03-23 1840 560
2020-03-24 2000 720
2020-03-25 2180 930
2020-03-26 2370 1170
2020-03-27 2580 1490

Initial visual evaluation of forecasts of Deaths