COVID-19 short-term forecasts Deaths 2021-07-08 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:
    [2021-04-29]The `legacy' download for areas of England is stuck at April 26, so we switched to the newer downloads. The results now include Scotland, Wales, and Northern Ireland. The map, however, only shows England.

Peak increase in estimated trend of Deaths in Latin America 2021-07-08

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-072021-05-0212-032021-05-252021-04-062021-03-292021-06-252021-05-2409-0309-072021-01-032021-07-032021-05-26 --2021-05-122021-05-182021-06-012021-01-122021-06-082021-04-142021-06-072021-05-282021-04-172021-05-16
Peak daily increment 580 6 6 80 2995 116 643 31 22 3440 25 59 7 45 8 2955 46 130 814 8 15 64 25
Days since peak 31 67 217 44 93 101 13 45 308 304 186 5 43 57 51 37 177 30 85 31 41 82 53
Last total 97904 252 330 17126 530179 33514 111155 4760 3870 21768 2428 9609 486 467 7192 1108 234458 6604 13729 193909 563 923 5772 3253
Last daily increment 465 0 0 59 1639 186 577 7 0 40 8 36 2 0 17 7 266 5 84 166 4 5 14 14
Last week 2522 6 0 247 8227 705 3432 74 24 145 35 259 9 31 133 26 1030 44 602 1222 24 57 130 99
Previous peak date10-0108-24 --09-0707-2107-172021-01-2209-2804-1205-1008-0706-05 --07-1207-30 --10-0507-23 --07-18 -- -- --09-20
Previous peak daily increment 2487 8 1439 1066 787 389 18 23 166 11 43 3 35 1833 28 917 9
Low between peaks 108 0 3 371 37 96 4 4 14 2 11 5 171 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-07-09 to 2021-07-15

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-07-08 97904 17126 530179 33514 111155 4760 3870 21768 2428 9609 467 7192 1108 234458 6604 13729 193909 563 923 5772 3253
2021-07-09 98600 17180 532900 33620 111800 4789 3876 21800 2433 9690 472 7213 1112 234600 6611 13830 194100 569 935 5782 3269
2021-07-10 99000 17290 535000 33750 112500 4796 3886 21840 2436 9751 474 7232 1114 234800 6614 13980 194400 579 946 5804 3285
2021-07-11 99200 17370 536000 33870 113200 4799 3895 21870 2440 9791 478 7253 1118 234900 6619 14100 194600 587 956 5820 3301
2021-07-12 99800 17440 536600 33990 113800 4842 3902 21900 2444 9825 482 7273 1121 235000 6624 14220 194700 594 966 5835 3316
2021-07-13 100300 17500 538500 34030 114400 4857 3909 21930 2449 9874 487 7294 1125 235300 6630 14330 194900 600 975 5849 3332
2021-07-14 100800 17560 540100 34070 115100 4867 3915 21960 2454 9907 492 7315 1129 235600 6636 14440 195100 606 985 5861 3347
2021-07-15 101200 17610 541800 34200 115700 4874 3921 21990 2459 9946 496 7336 1132 235900 6642 14540 195300 611 994 5873 3362

Deaths count average forecast Latin America (bold black line in graphs) 2021-07-09 to 2021-07-15

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-07-08 97904 17126 530179 33514 111155 4760 3870 21768 2428 9609 467 7192 1108 234458 6604 13729 193909 563 923 5772 3253
2021-07-09 98400 17180 531800 33660 111700 4772 3875 21790 2433 9662 470 7213 1113 234700 6610 13830 194100 568 933 5795 3268
2021-07-10 98700 17230 533400 33770 112300 4778 3882 21810 2437 9726 473 7233 1116 234800 6617 13960 194300 574 943 5824 3283
2021-07-11 99100 17280 534100 33870 112900 4783 3889 21830 2441 9781 477 7253 1120 234900 6623 14080 194500 580 953 5851 3298
2021-07-12 99500 17320 534700 33970 113400 4808 3895 21840 2446 9832 482 7272 1123 235000 6630 14190 194600 585 963 5878 3313
2021-07-13 100000 17350 536400 34010 114000 4821 3901 21860 2450 9885 487 7291 1127 235200 6637 14300 194800 590 972 5905 3328
2021-07-14 100500 17390 538000 34070 114500 4832 3908 21870 2454 9939 492 7310 1131 235500 6644 14410 195000 595 982 5932 3343
2021-07-15 100900 17430 539600 34140 115100 4841 3914 21890 2458 9993 497 7329 1134 235700 6650 14520 195200 600 992 5959 3359

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

[2021-04-29]The `legacy' download for areas of England is stuck at April 26, so we switched to the newer downloads. The results now include Scotland, Wales, and Northern Ireland. The map, however, only shows England.
[2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.
[2020-10-27]Statistical short-term forecasting of the COVID-19 Pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now published at the Journal of Clinical Immunology and Immunotherapy. open access
[2020-10-11]Short-term forecasting of the coronavirus pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now in press at the International Journal of Forecasting. open access
[2020-10-10]Removed forecasts from the Chinese scenarios, while investigating possibility to use own history from the first wave.
Added information on the previous peak (if present) to the peak tables.
Local forecasts for England: now dropping last four observations.
[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 Deaths