COVID-19 short-term forecasts Confirmed 2020-03-26


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, Magdalen College, or any other University of Oxford institute.
  • 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 The documentation that is provided is still in progress and not peer reviewed.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.

Moderation of forecast

[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. We have started to add forecasts that are based on what happened in China earlier this year, but only for Italy and Spain sofar.
[2020-03-26] We are in the process of updating our forecasts to reflect China's experience in a more coherent manner. This is shown for Italy only, and will require further development.
[2020-03-29] Now including some US States, based on New York Times data.

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 it seems that our forecasts need slightly less frequent updating.
    US state data as of 2020-03-28 is courtesy of the New York Times. This seems to be a day behind the Johns Hopkins data.
  • 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. Again: no other epidemiological data is used.
  • We will probably revise or 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.
  • We will probably revise or 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 forecasted, and combined with trend forecasts into an overall forecast.
  • EU-BS is Estonia, Latvia, and Lithuania together.
  • This paper describes the methodology and gives further references, but is still preliminary.

Confirmed count forecast average (bold black line in graphs) 2020-03-27 to 2020-03-31

DateUKEUATBEBGBSCZDEDKESFIFRGRHRHUIEITNLPLPTROSESINOCH
2020-03-26 11658 252770 6909 6235 264 2290 1925 43938 1877 57786 958 29155 892 495 261 1819 80589 7431 1221 3544 1029 2840 562 3369 11811
2020-03-27 13600 285000 7800 7100 280 2580 2150 49300 2020 66000 1050 33200 960 560 300 2050 87000 8400 1390 4090 1190 3110 600 3650 13000
2020-03-28 15500 322000 8700 7700 300 2850 2330 54900 2150 75000 1150 37900 1040 620 330 2280 95000 9300 1540 4590 1350 3360 650 3960 14400
2020-03-29 17600 363000 9700 8400 320 3140 2530 61200 2280 85000 1260 43300 1120 700 370 2550 104000 10300 1710 5160 1530 3630 690 4290 16000
2020-03-30 20100 410000 10900 9200 350 3470 2750 68300 2430 96000 1380 49400 1210 780 420 2840 114000 11400 1900 5810 1740 3920 740 4650 17800
2020-03-31 23000 462000 12200 10000 370 3840 2990 76200 2580 109000 1510 56500 1300 870 470 3170 124000 12600 2110 6550 1980 4240 790 5040 19700

Confirmed count forecast average (bold black line in graphs) 2020-03-27 to 2020-03-31

DateAustraliaBrazilCanadaIranMalaysiaPhilippinesSouth AfricaUS
2020-03-26 2810 2985 4042 29406 2031 707 927 83836
2020-03-27 3220 3400 4790 31800 2230 800 1110 100000
2020-03-28 3630 3920 5540 34100 2420 880 1270 118000
2020-03-29 4090 4510 6420 36500 2620 980 1450 140000
2020-03-30 4610 5200 7450 39200 2850 1090 1670 166000
2020-03-31 5190 6010 8650 42100 3090 1220 1920 198000

Confirmed count forecast (bold red line in graphs) 2020-03-27 to 2020-03-31

DateUKEUATBEBGBSCZDEDKESFIFRGRHRHUIEITNLPLPTROSESINOCH
2020-03-26 11658 252770 6909 6235 264 2290 1925 43938 1877 57786 958 29155 892 495 261 1819 80589 7431 1221 3544 1029 2840 562 3369 11811
2020-03-27 13600 283000 7500 6700 280 2560 2120 47700 2010 64000 1020 32200 950 550 290 2030 87000 8200 1380 4130 1160 3050 600 3640 12800
2020-03-28 15800 319000 8400 7500 300 2870 2350 53200 2150 72000 1100 36000 1020 610 330 2280 94000 9100 1560 4790 1320 3310 640 3950 14000
2020-03-29 18400 359000 9300 8400 310 3220 2610 59500 2300 82000 1190 40300 1100 670 360 2550 101000 10100 1770 5550 1500 3600 690 4290 15200
2020-03-30 21300 403000 10400 9300 330 3600 2900 66600 2460 93000 1280 45100 1180 740 410 2860 110000 11200 1990 6410 1690 3920 740 4650 16600
2020-03-31 24800 453000 11600 10400 350 4030 3210 74500 2630 106000 1380 50400 1280 810 450 3200 118000 12500 2240 7400 1910 4260 790 5050 18100

Confirmed count forecast (bold red line in graphs) 2020-03-27 to 2020-03-31

DateAustraliaBrazilCanadaIranMalaysiaPhilippinesSouth AfricaUS
2020-03-26 2810 2985 4042 29406 2031 707 927 83836
2020-03-27 3210 3310 4750 31700 2220 780 1060 97000
2020-03-28 3660 3680 5570 34200 2410 860 1240 116000
2020-03-29 4170 4090 6550 36900 2630 940 1450 138000
2020-03-30 4730 4530 7650 39900 2860 1030 1690 165000
2020-03-31 5360 5030 8940 43100 3120 1130 1960 196000

Initial visual evaluation of forecasts of Confirmed