COVID-19 short-term forecasts Confirmed 2022-01-05 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 2022-01-05

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) -- -- -- -- --2021-09-18 --2021-06-26 -- --2021-11-042021-11-062021-08-24 --2021-10-012021-08-132021-08-23 --2021-10-192021-12-312021-11-152021-12-30 --2021-12-09 --2021-10-05
Peak daily increment 92852 29569 514 1526 3774 97 1515 759 141 3933 163 7884 769 1476
Days since peak 109 193 62 60 134 96 145 135 78 5 51 6 27 92
Last total 5915695 26326 30177 34628 631554 22328252 1814188 5219633 578413 433710 553201 122063 631730 41418 26259 379402 97066 4029274 17526 503407 471434 2309856 55376 94213 425436 445680
Last daily increment 95159 315 479 541 9242 0 1840 16259 2542 5201 0 0 1919 779 46 0 735 20626 0 3259 974 0 916 579 5328 311
Last week 308950 2057 1867 2140 39781 46603 9506 72594 7857 16442 6015 118 4168 2023 274 0 3475 67612 39 12364 5333 17602 3107 2893 13778 1269
Previous peak date2021-06-252021-08-232021-10-272021-10-142021-06-122021-06-16 -- --2021-09-062021-06-182021-07-292021-04-11 --2021-09-162021-06-232021-02-03 --2021-08-1205-262021-06-292021-06-082021-06-052021-09-152021-06-05 -- --
Previous peak daily increment 20799 140 364 370 3394 72652 2553 1103 3070 674 245 153 1356 18191 145 1107 2669 3719 520 365
Low between peaks 17910 175 124 19 553 4 35 22 786 170

Confirmed count forecast Latin America (bold red line in graphs) 2022-01-06 to 2022-01-12

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaHaitiJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-05 5915695 26326 30177 34628 631554 22328252 1814188 5219633 578413 433710 553201 631730 41418 26259 97066 4029274 503407 471434 2309856 55376 94213 425436 445680
2022-01-06 5942000 26330 30470 35260 637700 22328000 1815000 5231000 580400 437200 554100 632800 41680 26260 97600 4043000 505200 472100 2310000 56080 94710 425400 445900
2022-01-07 5993000 26330 30790 35780 638700 22328000 1816000 5242000 582000 440800 554100 633900 42050 26260 98000 4057000 506600 472800 2310000 56630 95520 428500 446400
2022-01-08 6037000 26330 31130 36190 641700 22328000 1817000 5253000 583900 444400 554500 634600 42420 26260 98600 4069000 508200 473300 2310000 57220 96180 431600 446800
2022-01-09 6081000 26560 31380 36560 645500 22328000 1819000 5264000 585400 447800 555100 635400 42800 26260 99100 4077000 509400 473900 2311000 57780 96730 434500 447100
2022-01-10 6117000 26560 31610 36940 649600 22328000 1820000 5274000 586600 451500 555700 636300 43170 26260 99800 4086000 510900 474400 2312000 58360 97270 437400 447400
2022-01-11 6172000 26570 32070 37360 654200 22334000 1820000 5285000 590500 455200 556400 637400 43660 26300 100300 4099000 511700 474900 2313000 58950 97760 440400 447600
2022-01-12 6241000 26830 32450 37720 659000 22336000 1822000 5299000 591600 458700 557100 638500 44130 26330 100900 4111000 514100 475400 2314000 59550 98230 443300 447800

Confirmed count average forecast Latin America (bold black line in graphs) 2022-01-06 to 2022-01-12

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaHaitiJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-05 5915695 26326 30177 34628 631554 22328252 1814188 5219633 578413 433710 553201 631730 41418 26259 97066 4029274 503407 471434 2309856 55376 94213 425436 445680
2022-01-06 5980000 26530 30610 35240 636900 22335000 1816000 5231000 580800 437300 555300 633000 41890 26300 97700 4043000 504700 472400 2314000 56100 94830 428100 446000
2022-01-07 6023000 26710 30870 35580 641700 22339000 1817000 5240000 581700 439800 556800 633600 42090 26310 98000 4055000 506400 472900 2315000 56520 95470 430200 446300
2022-01-08 6062000 26770 31150 35800 644000 22341000 1819000 5249000 582600 442100 556900 634000 42330 26320 98600 4063000 507300 473300 2318000 56920 95940 432000 446600
2022-01-09 6101000 27350 31340 36000 650400 22343000 1820000 5258000 583300 444400 557100 634300 42590 26320 99000 4066000 508100 473700 2320000 57350 96410 433700 446900
2022-01-10 6141000 27440 31530 36270 653500 22347000 1821000 5267000 584100 446600 558500 634600 42790 26350 99400 4071000 508800 474800 2322000 57800 96760 435500 447100
2022-01-11 6182000 27560 31780 36560 656100 22353000 1822000 5277000 585800 448800 558800 635100 43020 26390 99700 4077000 509900 475400 2324000 58230 97250 437000 447400
2022-01-12 6227000 27750 32010 36760 659500 22355000 1823000 5285000 586100 451200 559000 635700 43220 26400 100100 4083000 511800 475800 2325000 58670 97660 439000 447600

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