COVID-19 short-term forecasts Deaths 2020-05-01


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 The documentation that is provided is still in progress and not peer reviewed.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

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) 2020-05-02 to 2020-05-08

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPeru
2020-05-01 225 6412 234 314 313 1063 1972 1124
2020-05-02 230 6900 240 320 320 1130 2090 1180
2020-05-03 240 7400 250 340 320 1250 2240 1250
2020-05-04 240 8000 260 350 320 1400 2400 1330
2020-05-05 250 8600 270 360 320 1570 2570 1430
2020-05-06 260 9300 280 380 330 1760 2750 1530
2020-05-07 270 10000 290 400 330 1970 2950 1640
2020-05-08 270 10700 300 420 340 2220 3150 1750

Deaths count forecast Latin America (bold red line in graphs) 2020-05-02 to 2020-05-08

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPeru
2020-05-01 225 6412 234 314 313 1063 1972 1124
2020-05-02 230 7000 240 330 320 1200 2130 1200
2020-05-03 240 7500 250 340 330 1330 2300 1270
2020-05-04 240 8100 260 360 330 1470 2470 1360
2020-05-05 250 8800 270 380 340 1630 2650 1440
2020-05-06 250 9400 280 390 340 1800 2840 1530
2020-05-07 260 10200 280 410 350 1980 3050 1620
2020-05-08 260 11000 290 430 360 2190 3280 1730

Deaths count scenario forecast (bold purple line in graphs) 2020-05-02 to 2020-05-10

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPeru
2020-05-01 225 6412 234 314 313 1063 1972 1124
2020-05-02 230 7000 240 320 310 1140 2120 1200
2020-05-03 240 7600 250 330 320 1210 2250 1280
2020-05-04 240 8200 260 340 320 1290 2380 1370
2020-05-05 250 8900 270 360 330 1370 2500 1450
2020-05-06 250 9600 280 370 330 1450 2620 1520
2020-05-07 260 10100 290 380 330 1540 2740 1590
2020-05-08 260 10800 300 390 330 1600 2860 1690
2020-05-09 270 11500 310 400 340 1720 2980 1750
2020-05-10 270 12100 320 410 340 1780 3070 1840

Peak increase in estimated trend of Deaths in Latin America 2020-05-01

ArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPeru
Peak date04-23 --04-17 --04-12 -- -- --
Peak daily increment 10 9 16
Days from 100 to peak 9 1 4
Days from peak/2 to peak 30 21 18
Days since peak 8 14 19

Initial visual evaluation of forecasts of Deaths