COVID-19 short-term forecasts Confirmed 2021-09-06 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 Confirmed in Latin America 2021-09-06

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-05-272021-07-262021-02-182021-08-232021-06-012021-03-242021-06-042021-06-26 --2021-01-182021-07-292021-04-11 --2021-06-242021-06-082021-08-10 --2021-08-09 --2021-07-032021-06-022021-04-10 --2021-05-242021-04-092021-05-16
Peak daily increment 32516 172 105 88 2893 74830 7273 29826 1589 3070 674 193 179 1453 18023 1075 2953 8699 529 5270 1699
Days since peak 102 42 200 14 97 166 94 72 231 39 148 74 90 27 28 65 96 149 105 150 113
Last total 5207695 18853 5417 16950 492680 20899933 1641526 4919773 478144 351894 503883 96067 488538 26510 21069 346134 71987 3433511 11735 459844 458844 2155508 31012 45824 385780 340187
Last daily increment 3893 159 68 200 0 9154 435 1124 5829 156 116 0 640 0 92 2327 643 5127 0 0 0 474 157 110 120 932
Last week 22075 714 385 597 1801 123063 2851 10687 14418 1721 2682 2007 18261 962 173 7377 3856 81101 0 2357 316 5917 1719 977 846 5844
Previous peak date10-1910-17 --12-032021-01-2208-0406-062021-01-162021-05-1707-2604-2408-0507-1809-2106-042021-02-032021-03-1510-05 --2021-01-07 --08-022021-06-0409-19 --09-08
Previous peak daily increment 14376 104 1113 2113 45351 7360 17013 2571 1408 7756 420 2699 66 177 1356 750 23278 3350 8364 281 119 1086
Low between peaks 5479 7 2 704 19228 1343 3453 400 -4346 71 13 5 553 2152 294 1487 4 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-09-07 to 2021-09-13

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-09-06 5207695 18853 5417 16950 492680 20899933 1641526 4919773 478144 351894 503883 96067 488538 26510 346134 71987 3433511 459844 458844 2155508 31012 45824 385780 340187
2021-09-07 5220000 18990 5438 17010 493800 20939000 1642000 4922000 480900 352000 503900 96070 494000 26580 347400 73050 3454000 460800 458900 2156000 31170 46060 385800 341200
2021-09-08 5228000 19000 5438 17100 494600 20974000 1643000 4923000 483700 352300 504000 97980 499300 26730 349100 73370 3477000 461800 459000 2157000 31240 46200 385900 342200
2021-09-09 5235000 19330 5438 17190 495100 21003000 1644000 4925000 485900 352600 505000 97980 503500 26840 350800 74000 3498000 462400 459000 2159000 31350 46320 386000 343100
2021-09-10 5240000 19460 5438 17300 495200 21030000 1644000 4927000 487900 352900 505000 97980 508000 26960 351400 74740 3515000 463000 459100 2160000 31480 46560 386100 344000
2021-09-11 5243000 19460 5438 17300 496000 21048000 1645000 4928000 489700 353200 505300 97980 510800 27070 352500 75580 3531000 463500 459200 2161000 31610 46730 386200 344900
2021-09-12 5244000 19460 5438 17300 496200 21057000 1645000 4930000 491400 353400 506300 97980 511400 27280 352500 76270 3535000 463800 459200 2162000 31750 46900 386300 345800
2021-09-13 5247000 19670 5438 17460 496300 21067000 1646000 4931000 493100 353600 506700 97980 512100 27280 354800 76980 3541000 463900 459200 2162000 31890 47000 386400 346700

Confirmed count average forecast Latin America (bold black line in graphs) 2021-09-07 to 2021-09-13

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-09-06 5207695 18853 5417 16950 492680 20899933 1641526 4919773 478144 351894 503883 96067 488538 26510 346134 71987 3433511 459844 458844 2155508 31012 45824 385780 340187
2021-09-07 5213000 18890 5484 17040 492900 20928000 1642000 4921000 480700 352100 504200 96160 491900 26630 346900 72730 3444000 460100 458900 2156000 31300 46010 385900 341000
2021-09-08 5218000 18930 5523 17130 493300 20959000 1642000 4923000 482700 352400 504300 97240 496600 26790 348200 73070 3462000 460600 458900 2157000 31540 46150 386000 341700
2021-09-09 5223000 19150 5563 17220 493500 20987000 1643000 4924000 484600 352600 504800 97390 500500 26910 349400 73550 3479000 460900 459000 2158000 31790 46290 386100 342400
2021-09-10 5227000 19270 5603 17320 493600 21012000 1643000 4926000 486400 353000 504800 97530 504400 27050 350100 74110 3494000 461200 459000 2159000 31980 46470 386200 343000
2021-09-11 5231000 19300 5643 17370 494100 21033000 1644000 4928000 487900 353200 505100 97670 507600 27160 351100 74770 3510000 461600 459100 2159000 32200 46640 386200 343600
2021-09-12 5235000 19340 5684 17430 494200 21045000 1645000 4929000 489300 353500 505400 97820 509900 27310 351600 75340 3519000 461900 459100 2160000 32380 46790 386300 344200
2021-09-13 5239000 19540 5727 17520 494500 21057000 1645000 4931000 490900 353700 505500 97960 512000 27390 353300 76020 3530000 462100 459200 2161000 32520 46910 386300 344800

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 Confirmed