COVID-19 short-term forecasts Confirmed 2022-05-31 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 2022-05-31

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-05-052022-01-172022-01-142022-01-282022-02-082022-01-152022-05-102022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-142022-04-072021-08-192022-05-182022-01-242022-01-192022-01-182022-05-142022-05-242022-01-25
Peak daily increment 112477 942 646 814 10699 182433 36050 30553 2696 6246 8554 7430 3374 925 494 8075 1352 21585 166 3616 8637 47146 913 491 3458 2156
Days since peak 138 142 26 134 137 123 112 136 21 137 136 106 112 135 134 104 137 54 285 13 127 132 133 17 7 126
Last total 9230573 34759 80654 59365 908862 31019038 3702941 6103455 891038 584029 877282 162755 861002 64763 30818 425371 137266 5775977 18491 858268 650661 3580960 80547 161584 925777 523654
Last daily increment 0 46 330 122 0 41377 4274 0 0 307 0 0 833 135 18 258 190 2981 0 3819 0 613 131 149 9389 36
Last week 51778 404 1026 492 1095 167847 45753 4344 0 2160 2532 666 2987 328 37 284 1804 16204 0 18522 378 3719 131 1865 9389 243
Previous peak date2021-06-052021-07-262022-01-192021-10-142021-06-102021-09-182021-11-132021-06-262022-01-252021-06-052021-06-292021-11-062021-08-242021-09-1806-042021-08-132021-08-232022-01-1905-262022-01-142021-06-082021-06-052021-09-212021-12-092022-01-202021-10-05
Previous peak daily increment 25322 172 826 370 2614 92852 2476 29569 7160 1203 1229 1526 3774 232 180 1515 759 43483 175 10293 2669 3719 480 788 11003 1476
Low between peaks 898 -2 84 -8 287 2340 968 1351 184 163 197 5 203 31 4 5 27 2342 -26 231 -154 60 16 234 57 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-06-01 to 2022-06-07

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-05-31 9230573 34759 80654 59365 908862 31019038 3702941 6103455 584029 877282 162755 861002 64763 425371 137266 5775977 858268 650661 3580960 161584 925777 523654
2022-06-01 9231000 34810 80890 59400 909000 31019000 3703000 6104000 584200 877300 162800 861800 64810 425400 137500 5777000 861200 650900 3581000 162500 925800 523700
2022-06-02 9231000 34810 81590 59440 909100 31027000 3704000 6104000 584200 878200 162800 862500 64820 425400 137500 5779000 863800 651100 3582000 163100 925800 523800
2022-06-03 9231000 34830 82060 59510 909200 31059000 3707000 6104000 584200 878300 162800 863300 64850 425400 137700 5781000 866500 651600 3582000 163400 925800 523800
2022-06-04 9231000 34860 82420 59510 909300 31072000 3709000 6105000 584200 878600 162800 863900 64890 425400 137900 5782000 869200 651700 3583000 163900 925800 523800
2022-06-05 9259000 34900 82750 59510 909400 31079000 3712000 6105000 584200 878700 162800 864000 64930 425500 138100 5783000 872000 652000 3583000 164300 927000 523900
2022-06-06 9259000 34940 83020 59650 909500 31099000 3714000 6105000 584200 879100 162800 864100 64980 425500 138400 5795000 874800 652200 3584000 164500 927000 523900
2022-06-07 9259000 34980 83270 59770 909700 31129000 3718000 6106000 584400 879100 162800 864800 65020 425600 138600 5796000 877700 652200 3584000 164800 936900 523900

Confirmed count average forecast Latin America (bold black line in graphs) 2022-06-01 to 2022-06-07

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-05-31 9230573 34759 80654 59365 908862 31019038 3702941 6103455 584029 877282 162755 861002 64763 425371 137266 5775977 858268 650661 3580960 161584 925777 523654
2022-06-01 9233000 34790 81030 59470 908900 31037000 3709000 6104000 584300 877400 162800 861700 64850 425500 137500 5778000 861800 650700 3582000 161900 927600 523700
2022-06-02 9237000 34840 81460 59540 909000 31055000 3716000 6106000 584400 878000 163000 862400 64890 425500 137700 5779000 864800 650700 3582000 162400 927800 523700
2022-06-03 9241000 34880 81760 59630 909100 31091000 3724000 6106000 584500 878200 163000 863000 64930 425500 137800 5779000 867800 651100 3582000 162700 928000 523800
2022-06-04 9244000 34910 81960 59640 909300 31106000 3730000 6106000 584600 878500 163000 863600 64960 425500 138000 5780000 870700 651100 3583000 163200 928300 523800
2022-06-05 9281000 34960 82250 59650 909400 31118000 3737000 6106000 584800 878700 163000 863700 65010 425500 138300 5780000 873100 651200 3583000 163600 928600 523800
2022-06-06 9282000 35000 82420 59740 909500 31130000 3742000 6106000 585000 878900 163000 863800 65040 425500 138600 5788000 875600 651300 3584000 163900 928700 523900
2022-06-07 9283000 35040 82660 59850 909700 31152000 3746000 6107000 585200 879100 163000 864500 65090 425600 139000 5789000 878500 651300 3584000 164500 932600 523900

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