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

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --2022-01-09 -- -- -- -- -- -- -- -- --2021-11-062022-01-072022-01-16 --2022-01-08 -- --2021-10-192022-01-14 -- -- --2021-12-09 -- --
Peak daily increment 805 1526 2518 1082 1148 168 14441 788
Days since peak 10 74 12 3 11 92 5 41
Last total 7446626 31549 37063 44145 774227 23425392 1902657 5624520 631311 517611 629507 123577 659655 53921 28030 385964 116084 4495310 17604 602606 509795 2723166 67768 102942 543166 461059
Last daily increment 128321 444 1329 1030 10835 195541 9542 27603 5359 7153 0 0 3199 743 211 0 1098 109895 0 10763 5415 54942 1135 918 11714 4418
Last week 653507 1641 3175 4255 62928 603215 53192 183539 26653 35154 69557 552 13336 5730 948 3524 8363 237534 41 62534 34109 249457 6060 4015 59346 11329
Previous peak date2021-06-252021-07-262021-10-292021-10-142021-06-112021-06-182021-06-122021-06-262021-09-062021-11-132021-07-292021-09-092021-08-242021-09-182021-06-082021-08-162021-08-292021-08-122021-08-192021-06-292021-06-082021-07-042021-09-152021-06-052021-06-082021-10-05
Previous peak daily increment 20798 172 347 318 2509 79238 6997 29569 2553 1150 3070 454 3774 232 142 1508 722 18191 147 1107 2705 2786 520 365 3390 1476
Low between peaks -9 124 151 20 -17 56 -277 170

Confirmed count forecast Latin America (bold red line in graphs) 2022-01-20 to 2022-01-26

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-19 7446626 31549 37063 44145 774227 23425392 1902657 5624520 631311 517611 629507 123577 659655 53921 28030 385964 116084 4495310 602606 509795 2723166 67768 102942 543166 461059
2022-01-20 7594000 31870 38360 46140 783400 23592000 1912000 5651000 638400 526800 638700 123700 661800 54780 28240 387500 116700 4550000 606800 513600 2727000 69510 103100 543200 463700
2022-01-21 7731000 32290 39350 47340 785400 23728000 1921000 5683000 644000 535600 647100 123800 664500 55450 28250 388000 117300 4590000 619900 515800 2755000 70750 103700 552900 466100
2022-01-22 7814000 32650 40350 47770 790500 23861000 1930000 5714000 645600 540900 651000 123800 666900 56210 28270 388800 118000 4635000 626800 518100 2788000 71640 104300 562600 468500
2022-01-23 7895000 32980 41190 47770 797200 23967000 1939000 5742000 647800 549100 651000 123800 668300 57010 28350 389500 119200 4679000 633200 520400 2812000 72170 104600 572200 470800
2022-01-24 7996000 33300 42020 49430 804300 24070000 1946000 5763000 654600 555200 654600 123800 669000 57820 28470 390200 119700 4722000 639000 524300 2838000 73670 104900 581900 473100
2022-01-25 8124000 33600 42770 50910 812400 24194000 1954000 5787000 659400 559500 656200 123800 671500 58670 28560 391300 120500 4766000 647400 526700 2865000 75120 105500 591800 475300
2022-01-26 8254000 33910 43490 51880 820800 24317000 1961000 5814000 664300 567000 660000 123900 674300 59530 28620 391300 121700 4809000 656900 527800 2891000 76190 106200 601800 477500

Confirmed count average forecast Latin America (bold black line in graphs) 2022-01-20 to 2022-01-26

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-19 7446626 31549 37063 44145 774227 23425392 1902657 5624520 631311 517611 629507 123577 659655 53921 28030 385964 116084 4495310 602606 509795 2723166 67768 102942 543166 461059
2022-01-20 7582000 31840 37650 45350 780200 23549000 1912000 5655000 637100 523800 636800 123600 662400 55140 28320 386600 117500 4543000 611500 515200 2755000 68920 103600 553100 462900
2022-01-21 7711000 32100 38070 46220 789600 23621000 1920000 5689000 642500 530100 653200 123700 665000 56110 28390 386800 118900 4561000 621200 517000 2771000 69990 104400 561500 463600
2022-01-22 7807000 32460 38500 46450 797300 23691000 1928000 5722000 644000 535700 654900 123700 667400 57220 28480 387200 120300 4608000 627300 518700 2793000 70870 105000 570300 464400
2022-01-23 7877000 32950 38840 46540 808500 23756000 1936000 5752000 645100 541700 655900 123800 668800 58230 28580 387500 122100 4638000 632400 520300 2814000 71430 105400 580500 465200
2022-01-24 7971000 33190 39200 47610 818700 23815000 1943000 5779000 652100 546600 657200 123800 669300 59120 28720 388200 123500 4664000 637900 526300 2830000 72610 105700 588000 466100
2022-01-25 8112000 33620 39680 48970 828600 23888000 1950000 5807000 657300 552200 658300 123800 671700 60120 28840 388800 125200 4681000 645100 528000 2847000 74020 106200 596400 467000
2022-01-26 8268000 34050 40180 50130 840600 23965000 1957000 5839000 662200 559600 660000 124000 674200 61240 28970 389000 126800 4704000 654000 528900 2860000 75310 106800 606300 467900

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