COVID-19 short-term forecasts Confirmed 2020-09-21 Latin American Countries


General information

  • 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. 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.
  • A list of notes is below. The most recent note:
    [2020-07-01] Modified the short-term model to allow for (slowly changing) seasonality. Many countries show clear seasonality after the initial period, likely caused by institutional factors regarding data collection. This seasonality was also getting in the way of peak detection. As a consequence estimates of the peak date may have changed for countries with strong seasonality.
    Added forecasts of cumulative confirmed cases for lower tier local authorities of England. The data is available from 2020-07-02 including all tests (pillar one and two). Only authorities with more than 5 cases in the previous week are included.

Peak increase in estimated trend of Confirmed in Latin America 2020-09-21

ArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoVenezuela
Peak date --08-1907-1708-0406-0608-13 --07-2604-2408-0507-18 --06-0606-28 --07-3105-2607-1309-1608-2108-13 --09-09
Peak daily increment 69 1578 45356 7362 11282 1408 7757 420 2699 179 795 6738 145 1089 821 8492 89 1058
Days from 100 to peak 85 107 141 83 146 126 38 118 99 31 91 135 7 116 164 155 67 166
Days from peak/2 to peak 133 83 113 64 107 119 18 107 65 33 75 113 9 109 135 134 72 116
Last total 640147 3418 130986 4558040 447468 770435 65602 108783 126711 27798 85681 2402 8624 72075 5143 700580 4961 106810 34260 768895 4740 3945 67443
Last daily increment 8782 103 310 13411 1194 5359 1890 494 292 245 237 133 5 459 155 2917 0 607 740 6030 17 44 787
Last week 62809 386 2700 175777 9485 41845 7465 3980 7158 710 2997 444 94 3455 1101 24093 0 3978 4962 35035 115 722 4788
Days since peak 33 66 48 107 39 57 150 47 65 107 85 52 118 70 5 31 39 12

Confirmed count forecast Latin America (bold red line in graphs) 2020-09-22 to 2020-09-28

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-09-21 640147 3418 130986 4558040 447468 770435 65602 108783 126711 27798 85681 2402 72075 5143 700580 106810 34260 768895 3945 67443
2020-09-22 651100 3465 131600 4594000 449600 777100 68130 109300 127700 27980 86390 2490 72560 5281 708000 107400 35040 771000 3989 68360
2020-09-23 662700 3517 132200 4627000 450800 784800 70230 110000 128600 28160 86750 2576 73040 5413 711900 108100 35810 773800 4126 69280
2020-09-24 674800 3572 132800 4660000 452500 792200 72250 110700 129600 28330 87400 2664 73510 5546 715100 108700 36580 777900 4225 70180
2020-09-25 686200 3626 133400 4697000 454100 798500 74360 111100 130600 28500 87970 2752 73980 5676 719900 109300 37340 783400 4394 71090
2020-09-26 695200 3681 134000 4726000 455900 805700 76510 112000 131600 28670 88620 2842 74450 5806 724800 109900 38120 788700 4576 72010
2020-09-27 703300 3736 134600 4737000 457500 812000 78720 112600 132500 28830 88830 2936 74920 5938 728000 110500 38900 793700 4627 72930
2020-09-28 711900 3792 135200 4750000 458700 816500 80980 113000 133500 29000 89030 3032 75400 6072 730800 111100 39700 799500 4684 73860

Confirmed count average forecast Latin America (bold black line in graphs) 2020-09-22 to 2020-09-28

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-09-21 640147 3418 130986 4558040 447468 770435 65602 108783 126711 27798 85681 2402 72075 5143 700580 106810 34260 768895 3945 67443
2020-09-22 650100 3431 131500 4579000 448700 775700 67030 109300 127300 27840 86170 2424 72480 5176 704800 107300 35020 773500 4032 68270
2020-09-23 661100 3470 132200 4614000 450000 782300 68740 110000 128100 27890 86660 2459 72940 5273 708800 107800 35860 778400 4165 69190
2020-09-24 672400 3526 132800 4648000 451600 788800 70490 110600 128800 27970 87270 2512 73390 5409 712500 108300 36660 783500 4284 70130
2020-09-25 683700 3575 133600 4687000 453300 795300 72310 111200 129600 28070 87840 2579 73840 5537 717000 108900 37510 789000 4435 71080
2020-09-26 693700 3641 134200 4717000 455000 801900 74150 111900 130300 28170 88510 2648 74300 5656 722100 109500 38380 794400 4593 72040
2020-09-27 703600 3690 134900 4728000 456800 808500 75890 112500 131100 28270 88950 2719 74760 5803 726300 110000 39220 799900 4695 73030
2020-09-28 714200 3749 135600 4742000 458400 814900 77910 113000 131800 28370 89360 2792 75220 5925 730000 110500 40060 805400 4806 74020

Confirmed count scenario forecast (bold purple line in graphs) 2020-09-22 to 2020-09-30

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-09-21 640147 3418 130986 4558040 447468 770435 65602 108783 126711 27798 85681 2402 72075 5143 700580 106810 34260 768895 3945 67443
2020-09-22 653500 3418 131600 4591000 449500 774600 67260 109400 129400 27800 86270 2417 72490 5161 704700 107100 35150 773800 4066 68420
2020-09-23 666100 3458 132000 4617000 451100 778900 68500 109800 132700 27830 86660 2476 72950 5287 707800 107400 35960 777600 4184 69230
2020-09-24 677300 3503 132400 4637000 452700 782500 70110 110300 136300 27890 87020 2546 73310 5432 710300 107700 36690 781700 4303 70100
2020-09-25 688200 3535 132800 4660000 454200 786000 71310 110800 140600 27950 87420 2597 73730 5486 712900 107800 37360 785300 4416 70800
2020-09-26 703000 3570 132900 4679000 455600 789700 72870 111100 144600 27990 87700 2649 74070 5606 715100 107900 38140 788800 4514 71400
2020-09-27 711800 3597 133000 4696000 456700 791300 73840 111400 147500 28010 87780 2695 74300 5701 717600 108100 38870 791900 4588 72040
2020-09-28 723100 3629 133200 4717000 457900 794000 75500 111700 152900 28040 88010 2766 74510 5838 718900 108200 39570 794400 4671 72730
2020-09-29 733700 3652 133200 4729000 459200 795900 76820 111800 156200 28060 88250 2803 74710 5951 720100 108300 40190 796800 4750 73410
2020-09-30 743400 3674 133300 4744000 460600 797600 77640 111900 161900 28090 88360 2847 74830 6033 720900 108300 40730 799100 4847 73710

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.

Recent changes and notes

[2020-07-01] Modified the short-term model to allow for (slowly changing) seasonality. Many countries show clear seasonality after the initial period, likely caused by institutional factors regarding data collection. This seasonality was also getting in the way of peak detection. As a consequence estimates of the peak date may have changed for countries with strong seasonality.
Added forecasts of cumulative confirmed cases for lower tier local authorities of England. The data is available from 2020-07-02 including all tests (pillar one and two). Only authorities with more than 5 cases in the previous week are included.
[2020-06-29] Tables in April included the world, but not the world as we know it (double counting China and the US). So removed the world from those old tables.
Why short-term forecasts can be better than models for predicting how pandemics evolve just appeared at The Conversation.
Thursday 2 July webinar at the FGV EESP - São Paolo School of Economics. This starts at 16:00 UK time (UTC+01:00) and streamed here.
[2020-06-24] Research presentation on short-term COVID-19 forecasting on 26 June (14:00 UK time) at the Quarterly Forecasting Forum of the IIF UK Chapter.
[2020-06-06] Removed Brazil from yesterday's forecasts (only; last observation 2020-06-05).
[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.
[2020-05-20] Problem with UK confirmed cases: negative daily count. This makes the forecasts temporarily unreliable.
Updated the second paper.
[2020-05-18] Minor fixes to the improved version of scenario forecasting, backported to 2020-05-13.
[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.
[2020-05-06] 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.
[2020-04-29] 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.
[2020-04-27] 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.
[2020-04-24] A summary of our work on short-term COVID-19 forecasting appeared as a voxeu.
[2020-04-17] Bird and Nielsen look into nowcasting death counts in England.
[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).
[2020-04-10] Updated documentation with better description of short-term estimates and peak determination.
[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.
[2020-04-08] Minor correction to peak estimates. Added table with scenario forecasts.
[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.
[2020-04-02] Now including more US States, based on New York Times data.
[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).
[2020-03-26] Scenario forecasts that are based on what happened in China earlier this year, only for Italy.
[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.

Initial visual evaluation of forecasts of Confirmed