COVID-19 short-term forecasts Deaths 2020-06-10


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 The documentation that is provided is still in progress and not peer reviewed.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 temporarily unreliable.
Updated the second paper.
[2020-06-04] Data issues with confirmed cases for France.
Added an appendix to the short term paper with further forecast comparisons for European and Latin American countries.
Both Sweden and Iran have lost their peak in confirmed cases. For Sweden the previous peak was on 24 April (daily peak of 656 cases), for Iran it was on 31 March (peak of 3116). For Iran this looks like a second wave, with increasing daily counts for the last four weeks. For Sweden this is a sudden jump in confirmed cases in the last two days, compared to a fairly steady weekly pattern over the previous six weeks.
[2020-06-06] Removed Brazil from yesterday's forecasts (last observation 2020-06-05).

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) 2020-06-11 to 2020-06-17

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-10 735 512 39680 2475 1439 550 3720 316 290 15357 413 5903
2020-06-11 760 520 40100 2650 1500 550 3740 330 290 15500 420 6080
2020-06-12 780 520 40600 2960 1570 560 3760 360 300 15800 420 6270
2020-06-13 810 540 41400 3330 1640 560 3790 380 310 16100 430 6470
2020-06-14 840 550 42200 3780 1710 560 3820 410 310 16500 440 6680
2020-06-15 870 560 43100 4330 1790 570 3850 440 320 16900 450 6890
2020-06-16 900 580 44000 5000 1870 570 3880 470 330 17300 450 7110
2020-06-17 930 590 44900 5730 1950 580 3910 510 340 17800 460 7340

Deaths count forecast Latin America (bold red line in graphs) 2020-06-11 to 2020-06-17

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-10 735 512 39680 2475 1439 550 3720 316 290 15357 413 5903
2020-06-11 750 520 40600 2630 1500 550 3750 360 300 15900 420 6080
2020-06-12 770 540 41600 2800 1570 560 3780 390 310 16400 430 6260
2020-06-13 790 550 42500 2930 1630 560 3800 420 320 16800 440 6450
2020-06-14 810 570 43500 3060 1690 570 3830 460 330 17300 440 6650
2020-06-15 830 580 44400 3190 1760 570 3860 500 340 17900 450 6860
2020-06-16 850 600 45400 3340 1830 570 3890 550 350 18400 460 7070
2020-06-17 870 610 46500 3480 1900 580 3920 600 370 18900 470 7290

Deaths count scenario forecast (bold purple line in graphs) 2020-06-11 to 2020-06-19

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-10 735 512 39680 2475 1439 550 3720 316 290 15357 413 5903
2020-06-11 760 520 40400 2630 1510 550 3740 350 300 15600 420 6050
2020-06-12 790 530 41700 2890 1570 560 3760 380 310 16000 430 6210
2020-06-13 820 550 43000 3170 1630 560 3780 400 320 16400 440 6370
2020-06-14 840 560 44200 3400 1700 560 3790 430 330 16800 450 6540
2020-06-15 870 570 45600 3650 1760 570 3810 450 340 17200 460 6710
2020-06-16 900 580 46800 3990 1820 570 3820 470 340 17600 470 6880
2020-06-17 930 590 48000 4530 1880 570 3830 490 350 18000 480 7060
2020-06-18 960 600 49300 4530 1920 570 3840 520 360 18400 490 7240
2020-06-19 980 610 50600 4530 1960 580 3840 540 370 18800 500 7430

Peak increase in estimated trend of Deaths in Latin America 2020-06-10

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
Peak date --06-0606-04 -- --04-1205-02 -- --06-03 -- --
Peak daily increment 23 1094 16 171 623
Days from 100 to peak 29 68 4 30 58
Days from peak/2 to peak 61 59 18 31 51
Last total 735 512 39680 2475 1439 550 3720 316 290 15357 413 5903
Last daily increment 18 25 1274 192 66 6 30 27 19 708 10 165
Last week 127 97 5659 1119 340 30 234 158 47 2812 50 872
Days since peak 4 6 59 39 7

Initial visual evaluation of forecasts of Deaths