COVID-19 short-term forecasts Confirmed 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 The documentation that is provided is still in progress and not peer reviewed.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.

Confirmed count average forecast Latin America (bold black line in graphs) 2020-05-02 to 2020-05-08

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-01 4532 92202 17008 7006 7288 26336 20739 6720 40459
2020-05-02 4630 99000 17600 7300 7470 26900 22000 6840 43400
2020-05-03 4750 108000 18500 7700 7740 28000 23600 6980 47000
2020-05-04 4870 117000 19500 8100 8060 29100 25200 7120 50900
2020-05-05 5000 127000 20600 8600 8390 30200 27000 7270 55100
2020-05-06 5130 137000 21800 9100 8740 31400 29000 7440 59700
2020-05-07 5270 149000 23000 9600 9110 32800 31100 7630 64700
2020-05-08 5410 161000 24200 10100 9490 34300 33300 7820 70100

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

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-01 4532 92202 17008 7006 7288 26336 20739 6720 40459
2020-05-02 4630 100000 18000 7400 7600 26800 22300 6870 43600
2020-05-03 4740 107000 19000 7800 8000 27400 24000 7040 47200
2020-05-04 4850 116000 20100 8200 8300 27900 25900 7210 51000
2020-05-05 4950 124000 21400 8700 8700 28400 27800 7390 55200
2020-05-06 5070 134000 22600 9200 9100 28900 30000 7570 59600
2020-05-07 5180 144000 24000 9800 9600 29500 32300 7750 64500
2020-05-08 5300 155000 25400 10400 10000 30000 34700 7940 69700

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

DateArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
2020-05-01 4532 92202 17008 7006 7288 26336 20739 6720 40459
2020-05-02 4630 99000 17400 7200 7440 26700 21400 6790 42400
2020-05-03 4720 105000 18000 7450 7600 27100 22100 6900 44400
2020-05-04 4800 110000 18700 7690 7760 27200 22800 7040 46100
2020-05-05 4880 115000 19400 7880 7950 27700 23500 7120 47700
2020-05-06 4950 120000 19900 8100 8140 27700 24500 7230 49000
2020-05-07 5030 124000 20300 8250 8340 27700 25100 7300 50500
2020-05-08 5080 127000 20600 8460 8510 27700 25700 7360 51200
2020-05-09 5140 129000 21100 8520 8640 27700 26200 7430 51700
2020-05-10 5210 131000 21200 8680 8740 27700 26700 7480 52000

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

ArgentinaBrazilChileColombiaDominican RepublicEcuadorMexicoPanamaPeru
Peak date04-23 -- -- -- --04-24 --04-23 --
Peak daily increment 175 4481 228
Days from 100 to peak 35 37 35
Days from peak/2 to peak 35 24 35
Days since peak 8 7 8

Initial visual evaluation of forecasts of Confirmed