COVID-19 short-term forecasts Deaths 2020-06-08


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 The documentation that is provided is still in progress and not peer reviewed.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) 2020-06-09 to 2020-06-15

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-08 693 475 37134 2264 1373 539 3642 267 262 14053 398 5571
2020-06-09 710 490 37800 2410 1410 540 3660 290 270 14400 410 5670
2020-06-10 740 510 38700 2580 1470 550 3690 320 270 14900 420 5790
2020-06-11 760 520 39600 2790 1540 550 3720 350 280 15400 430 5920
2020-06-12 790 540 40500 3030 1620 560 3750 390 280 15900 440 6060
2020-06-13 810 560 41400 3340 1700 560 3780 430 290 16400 450 6190
2020-06-14 840 580 42400 3790 1780 570 3810 470 290 17000 460 6330
2020-06-15 870 610 43400 4340 1860 570 3840 520 300 17600 470 6480

Deaths count forecast Latin America (bold red line in graphs) 2020-06-09 to 2020-06-15

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-08 693 475 37134 2264 1373 539 3642 267 262 14053 398 5571
2020-06-09 720 490 37900 2800 1440 540 3660 310 270 14400 410 5690
2020-06-10 750 500 38600 3500 1510 550 3680 340 270 14800 420 5820
2020-06-11 780 510 39300 4400 1590 550 3700 370 280 15200 430 5940
2020-06-12 800 530 40000 5400 1670 550 3730 400 280 15500 440 6070
2020-06-13 840 540 40800 6700 1760 560 3750 440 280 15900 450 6200
2020-06-14 870 560 41500 8200 1850 560 3770 470 290 16300 470 6330
2020-06-15 900 570 42300 10100 1950 560 3790 520 290 16700 480 6470

Deaths count scenario forecast (bold purple line in graphs) 2020-06-09 to 2020-06-17

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-08 693 475 37134 2264 1373 539 3642 267 262 14053 398 5571
2020-06-09 710 490 37900 2320 1400 540 3670 300 270 14500 410 5700
2020-06-10 730 490 38700 2540 1450 550 3700 330 270 15100 420 5830
2020-06-11 750 510 39400 2680 1500 550 3720 360 280 15500 430 5950
2020-06-12 780 540 40100 2830 1550 560 3740 410 300 15900 440 6050
2020-06-13 800 550 41000 2960 1620 560 3760 440 310 16100 450 6150
2020-06-14 830 570 41600 3090 1640 560 3770 490 310 16500 460 6260
2020-06-15 860 580 42100 3220 1680 560 3790 530 320 17000 470 6350
2020-06-16 880 600 42600 3350 1710 570 3800 580 330 17500 480 6430
2020-06-17 900 610 43100 3480 1740 570 3810 620 330 18000 480 6510

Peak increase in estimated trend of Deaths in Latin America 2020-06-08

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
Peak date --06-0306-03 -- --04-1205-02 --06-0406-03 --05-26
Peak daily increment 21 1080 16 171 8 630 151
Days from 100 to peak 26 67 4 30 28 58 49
Days from peak/2 to peak 59 58 18 31 66 51 51
Last total 693 475 37134 2264 1373 539 3642 267 262 14053 398 5571
Last daily increment 29 10 679 627 108 1 21 15 4 354 5 106
Last week 124 99 5935 1076 359 24 204 144 37 3416 46 937
Days since peak 5 5 57 37 4 5 13

Initial visual evaluation of forecasts of Deaths