COVID-19 short-term forecasts Deaths 2020-06-20


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 The documentation that is provided is still in progress and not peer reviewed.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 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) 2020-06-21 to 2020-06-27

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-20 992 740 49976 4295 2148 655 4156 514 358 20781 493 7861
2020-06-21 1020 760 51400 4360 2190 660 4180 530 360 21500 500 7980
2020-06-22 1050 790 53000 4460 2270 670 4220 540 370 22300 510 8140
2020-06-23 1090 820 54700 4580 2360 680 4260 560 370 23200 530 8290
2020-06-24 1120 850 56500 4730 2450 690 4290 580 380 24000 540 8450
2020-06-25 1160 880 58400 4880 2540 690 4330 600 380 25000 550 8640
2020-06-26 1190 910 60300 5040 2630 700 4370 620 390 25900 560 8830
2020-06-27 1230 940 62200 5210 2730 710 4410 650 400 26900 570 9020

Deaths count forecast Latin America (bold red line in graphs) 2020-06-21 to 2020-06-27

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-20 992 740 49976 4295 2148 655 4156 514 358 20781 493 7861
2020-06-21 1030 770 51400 4450 2220 660 4180 540 360 21500 500 8010
2020-06-22 1060 800 52900 4600 2300 670 4210 560 370 22200 510 8220
2020-06-23 1090 840 54400 4770 2390 680 4230 590 370 23000 520 8430
2020-06-24 1120 870 56000 4930 2480 690 4260 610 380 23800 530 8640
2020-06-25 1150 910 57600 5100 2570 700 4280 640 390 24700 540 8850
2020-06-26 1190 940 59300 5280 2660 700 4300 670 390 25600 550 9080
2020-06-27 1220 980 61100 5460 2760 710 4330 710 400 26500 560 9300

Deaths count scenario forecast (bold purple line in graphs) 2020-06-21 to 2020-06-29

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-20 992 740 49976 4295 2148 655 4156 514 358 20781 493 7861
2020-06-21 1020 770 51400 4350 2200 660 4200 530 360 21700 500 7910
2020-06-22 1060 790 52600 4450 2270 670 4240 560 370 22600 510 8020
2020-06-23 1080 820 54000 4560 2340 670 4260 590 370 23300 520 8160
2020-06-24 1110 850 55500 4670 2420 680 4290 620 380 24100 530 8340
2020-06-25 1140 880 57000 4750 2520 690 4320 650 380 24900 540 8490
2020-06-26 1160 900 58400 4820 2610 690 4340 670 380 25700 550 8640
2020-06-27 1190 910 59900 4910 2700 700 4360 710 390 26600 560 8790
2020-06-28 1220 920 61300 4990 2780 700 4370 740 390 27500 570 8880
2020-06-29 1240 920 62700 5060 2860 700 4390 780 390 28400 580 8970

Peak increase in estimated trend of Deaths in Latin America 2020-06-20

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
Peak date -- -- --06-08 --04-1205-02 --06-10 -- -- --
Peak daily increment 294 15 166 9
Days from 100 to peak 53 4 30 34
Days from peak/2 to peak 48 18 31 72
Last total 992 740 49976 4295 2148 655 4156 514 358 20781 493 7861
Last daily increment 13 25 1022 202 102 8 0 31 9 387 8 400
Last week 159 129 6644 972 478 63 260 130 46 3640 56 1173
Days since peak 12 69 49 10

Initial visual evaluation of forecasts of Deaths