COVID-19 short-term forecasts Deaths 2020-04-06


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, or Magdalen College.
  • 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 The documentation that is provided is still in progress and not peer reviewed.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.

Moderation of forecast

[2020-03-24] Our forecasts are starting to overestimate in some cases. This was always expected to happen when the increase starts to slow down. Scenario forecasts that are based on what happened in China earlier this year, but only for Italy and Spain sofar.
[2020-03-26] Scenario forecasts that are based on what happened in China earlier this year, only for Italy.
[2020-03-31] Scenario forecasts, based on what happened in China earlier this year, are presented for several countries (line marked with x). Created more plausible 90% confidence bands (dotted line in same colour).
[2020-04-02] Now including more US States, based on New York Times data. And the world.
[2020-04-06] Added a post hoc estimate of the peak number of cases. This needs at least three confirmed observations (four for deaths) after the event. It is based on the averaged smooth trend, and can change later or be a local peak. It is marked with a vertical line with the date label, or a date with left arrow in the bottom left corner of the graph. This is backported to 2020-04-04.
[2020-04-08] Minor correction to peak estimates. Added table with scenario forecasts.
[2020-04-08] Added table with estimated peak dates (if happened) and dates to and since the peak. Note that this can be a local peak, and subsequent re-acceleration (or data revisions) can result in a new peak later.

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.
  • 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 remains preliminary. Also preliminary is the documentation of the medium term forecasts.

Deaths count average forecast (bold black line in graphs) 2020-04-07 to 2020-04-11

DateUKEUATBEDEESFRITNLPTSECH
2020-04-06 5373 46113 220 1632 1810 13341 8911 16523 1867 311 477 765
2020-04-07 6100 49300 240 1820 2010 14100 10000 17100 2010 340 540 830
2020-04-08 7100 53100 250 2010 2210 14900 11500 17800 2180 360 620 910
2020-04-09 8100 57200 270 2210 2430 15700 13100 18500 2350 390 720 1000
2020-04-10 9400 61600 290 2440 2670 16500 15000 19300 2540 420 830 1090
2020-04-11 10800 66400 310 2690 2940 17400 17100 20000 2750 460 970 1200

Deathscount average forecast (bold black line in graphs) 2020-04-07 to 2020-04-11

DateBrazilCanadaIranUSUS-CAUS-CTUS-FLUS-GAUS-ILUS-LAUS-MAUS-MIUS-NJUS-NYUS-WA
2020-04-06 564 339 3739 10783 386 206 253 294 309 512 260 727 1005 4758 383
2020-04-07 640 400 3880 12300 420 240 280 330 360 570 300 820 1150 5440 410
2020-04-08 720 450 4020 14000 470 280 320 350 420 620 360 910 1360 6120 440
2020-04-09 800 500 4160 16000 510 320 360 380 500 680 430 1010 1600 6880 470
2020-04-10 900 570 4300 18200 560 370 410 410 600 740 530 1110 1880 7730 490
2020-04-11 1000 640 4450 20800 610 430 460 440 710 800 640 1230 2210 8680 530

Deaths count forecast (bold red line in graphs) 2020-04-07 to 2020-04-11

DateUKEUATBEDEESFRITNLPTSECH
2020-04-06 5373 46113 220 1632 1810 13341 8911 16523 1867 311 477 765
2020-04-07 5930 49300 230 1870 2020 14000 9900 17100 2000 330 540 820
2020-04-08 6520 52600 250 2100 2250 14700 11100 17600 2130 360 600 870
2020-04-09 7160 56100 260 2340 2500 15400 12400 18200 2260 380 680 930
2020-04-10 7850 59800 280 2610 2770 16000 13800 18800 2390 410 760 990
2020-04-11 8610 63800 290 2920 3080 16800 15400 19300 2540 430 840 1050

Deaths count scenario forecast (bold green line in graphs) 2020-04-07 to 2020-04-15

DateEUATESITNLPTCH
2020-04-06 46113 220 13341 16523 1867 311 765
2020-04-07 49000 240 14100 17100 2040 340 830
2020-04-08 51700 250 14800 17700 2190 370 880
2020-04-09 54300 270 15900 18200 2340 390 940
2020-04-10 56700 290 17100 18700 2510 420 1000
2020-04-11 58900 300 18600 19200 2660 450 1060
2020-04-12 61100 300 19800 19600 2820 480 1110
2020-04-13 63300 310 20900 20000 3030 510 1160
2020-04-14 65500 320 22100 20300 3180 550 1190
2020-04-15 68200 320 23400 20600 3180 560 1240

Initial visual evaluation of forecasts of Deaths