COVID-19 short-term forecasts Deaths 2020-05-20


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, 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 The documentation that is provided is still in progress and not peer reviewed.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.

Recent changes

[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.
[2020-04-09] 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.
[2020-04-10] Updated documentation with better description of short-term estimates and peak determination.
[2020-04-16] Added scenario forecasts to all graphs now. This would now be the preferred forecast for most.
This is the first time with a peak in confirmed UK cases (also for deaths, but this is uncertain because it is at the same date).
[2020-04-17] Bird and Nielsen look into nowcasting death counts in England.
[2020-04-24] A summary of our work on short-term COVID-19 forecasting appeared as a voxeu.
[2020-04-27] Our short-term COVID-19 forecasting paper is now available as Nuffield Economics Discussion Paper 2020-W06.
A small adjustment has been made to the scenario forecast methodology, and will be documented shortly.
[2020-04-29] See our blog entry at the International Institute of Forecasters.
US history of death counts revised in Johns Hopkins/CSSE data.
UK death counts have been revised to include the deaths in care homes. In the Johns Hopkins/CSSE data set, which we use, the entire history has been revised. So forecasts made up to 2020-04-29 cannot be compared to later outcomes. In the ECDC data set only the last observation has changed, causing a jump in the series.
[2020-05-06] The New York Times is in the process of redefining its US state data. Unfortunately, at the moment only the last observation has changed (e.g New York deaths jumped from 19645 on 2020-05-05 to 25956 a day later). This means the data is currently useless; however it does bring it close to the Johns Hopkins/CSSE count (25626 on 2020-05-06). The aggregate US count is based on JH/CSSE so unaffected. We now use Johns Hopkins/CSSE US state data, including all states with sufficient counts. So the new forecasts cannot be compared to those previously.
A minor change is that we show the graph without scenario forecast if no peak has been detected yet.
[2020-05-13] We now omit countries with fewer than 200 confirmed cases in the last week (25 for deaths).
The short-term paper has some small updates, including further comparisons with other models.
Data for Ecuador are not reliable enough for forecasting.
Switched to an improved version of scenario forecasting.
[2020-05-18] Minor fixes to the improved version of scenario forecasting, backported to 2020-05-13.
[2020-05-20] Problem with UK confirmed cases: negative daily count. This makes the forecasts unreliable.
Updated the second paper.

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 we tend to update our forecasts only every other day.
    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. No other epidemiological data is used. Data definition and collection differs between countries and may change over time.
  • We will 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 forecast, and combined with trend forecasts into an overall forecast.
  • Scenario forecasts are constructed very differently: smooth versions of the Chinese experience are matched at different lag lengths with the path of each country. This probably works best from the peak, or the slowdown just before (but we include it for the UK nonetheless).
  • The forecast evaluation shows past forecasts, together with the outcomes (in the grey line with circles).
  • EU-BS is Estonia, Latvia, and Lithuania together.
  • This paper describes the methodology and gives further references. Also available as Nuffield Economics Discussion Paper 2020-W06. Still preliminary is the documentation of the medium term forecasts.

Deaths count average forecast Latin America (bold black line in graphs) 2020-05-21 to 2020-05-27

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-20 403 18859 544 630 446 2888 6090 287 3024
2020-05-21 420 19600 570 640 450 2940 6260 290 3160
2020-05-22 430 20600 600 650 450 3010 6560 290 3320
2020-05-23 440 21600 640 670 460 3080 6880 300 3480
2020-05-24 450 22700 670 690 460 3150 7220 300 3660
2020-05-25 470 23900 710 700 460 3220 7580 310 3840
2020-05-26 480 25100 750 720 470 3290 7960 310 4030
2020-05-27 500 26400 790 740 470 3370 8350 310 4230

Deaths count forecast Latin America (bold red line in graphs) 2020-05-21 to 2020-05-27

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-20 403 18859 544 630 446 2888 6090 287 3024
2020-05-21 420 20000 580 650 450 2960 6310 290 3160
2020-05-22 430 21100 620 660 450 3010 6580 300 3310
2020-05-23 440 22300 650 680 460 3070 6850 300 3470
2020-05-24 460 23600 690 690 460 3120 7150 300 3630
2020-05-25 470 24900 730 710 470 3170 7460 310 3800
2020-05-26 480 26300 780 730 470 3220 7780 310 3980
2020-05-27 500 27800 820 750 470 3270 8110 320 4160

Deaths count scenario forecast (bold purple line in graphs) 2020-05-21 to 2020-05-29

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-20 403 18859 544 630 446 2888 6090 287 3024
2020-05-21 420 19700 580 640 450 2960 6290 290 3180
2020-05-22 430 20600 620 660 450 3040 6580 290 3340
2020-05-23 440 21500 660 670 450 3100 6840 300 3500
2020-05-24 460 22400 700 680 460 3180 7090 300 3660
2020-05-25 470 23200 740 700 460 3240 7330 300 3820
2020-05-26 480 24200 770 710 460 3300 7590 300 3970
2020-05-27 490 25000 810 720 470 3370 7850 310 4120
2020-05-28 510 26000 840 740 470 3460 8100 310 4220
2020-05-29 520 26800 870 740 470 3520 8320 310 4350

Peak increase in estimated trend of Deaths in Latin America 2020-05-20

ArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
Peak date -- -- --05-0504-1205-02 --05-06 --
Peak daily increment 18 16 178 7
Days from 100 to peak 23 4 30 20
Days from peak/2 to peak 37 18 31 44
Days since peak 15 38 18 14

Initial visual evaluation of forecasts of Deaths