COVID-19 short-term forecasts Deaths 2020-05-30


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 The documentation that is provided is still in progress and not peer reviewed.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

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) 2020-05-31 to 2020-06-06

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorHondurasMexicoPanamaPeru
2020-05-30 528 310 28834 997 891 498 3334 201 9779 330 4371
2020-05-31 540 320 30100 1030 920 500 3360 200 10200 330 4470
2020-06-01 550 330 31400 1080 950 500 3390 210 10800 340 4580
2020-06-02 560 340 32900 1130 980 510 3420 210 11300 340 4690
2020-06-03 570 350 34400 1180 1020 510 3460 220 11900 340 4820
2020-06-04 590 360 36000 1240 1050 520 3500 220 12500 350 4950
2020-06-05 600 370 37700 1300 1090 520 3530 220 13200 350 5100
2020-06-06 610 380 39400 1360 1130 530 3570 230 13900 350 5250

Deaths count forecast Latin America (bold red line in graphs) 2020-05-31 to 2020-06-06

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorHondurasMexicoPanamaPeru
2020-05-30 528 310 28834 997 891 498 3334 201 9779 330 4371
2020-05-31 540 320 30300 1040 920 500 3360 200 10400 330 4480
2020-06-01 540 330 31900 1090 950 510 3370 210 11100 340 4630
2020-06-02 550 330 33600 1140 990 520 3390 210 11800 340 4770
2020-06-03 560 340 35300 1190 1020 520 3410 210 12500 350 4930
2020-06-04 570 350 37200 1240 1060 530 3430 210 13300 350 5090
2020-06-05 570 360 39100 1290 1100 530 3440 220 14100 350 5250
2020-06-06 580 370 41100 1350 1140 540 3460 220 15000 360 5420

Deaths count scenario forecast (bold purple line in graphs) 2020-05-31 to 2020-06-08

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorHondurasMexicoPanamaPeru
2020-05-30 528 310 28834 997 891 498 3334 201 9779 330 4371
2020-05-31 540 320 30000 1050 920 500 3370 200 10300 330 4450
2020-06-01 550 320 31100 1100 950 510 3400 210 10700 340 4570
2020-06-02 560 330 32200 1160 980 510 3430 210 11100 340 4710
2020-06-03 570 340 33300 1210 1010 520 3440 210 11600 340 4830
2020-06-04 580 340 34300 1260 1030 520 3460 220 12000 340 4970
2020-06-05 590 350 35700 1320 1060 520 3490 220 12400 340 5120
2020-06-06 600 360 36800 1380 1080 520 3510 220 12700 350 5260
2020-06-07 610 360 37800 1440 1100 520 3530 220 13100 350 5330
2020-06-08 620 360 38800 1500 1120 530 3550 220 13500 350 5420

Peak increase in estimated trend of Deaths in Latin America 2020-05-30

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorHondurasMexicoPanamaPeru
Peak date05-2605-21 -- -- --04-1205-0205-24 --05-06 --
Peak daily increment 15 13 16 171 7 7
Days from 100 to peak 42 13 4 30 17 20
Days from peak/2 to peak 62 49 18 31 55 44
Days since peak 4 9 48 28 6 24

Initial visual evaluation of forecasts of Deaths