COVID-19 short-term forecasts Confirmed 2022-06-16 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 2022-06-16

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-05-052022-01-172022-01-142022-01-282022-02-082022-01-152022-05-102022-01-142022-06-062022-02-142022-02-082022-01-162022-01-172022-02-162022-01-142022-04-0705-282022-05-182022-01-172022-01-192022-01-182022-05-142022-01-202022-01-25
Peak daily increment 112477 942 628 814 10699 182433 36050 30553 2623 6246 4728 8311 3374 925 494 8075 1352 21585 194 4062 9174 47146 913 521 11003 2156
Days since peak 154 158 42 150 153 139 128 152 37 153 10 122 128 151 150 120 153 70 749 29 150 148 149 33 147 142
Last total 9313453 35516 82643 61150 914252 31611769 3857643 6131657 904934 594964 892176 164134 876499 66300 31054 425930 141026 5852596 14619 898882 652044 3592765 80766 165468 943877 524488
Last daily increment 0 52 98 0 378 0 12975 13810 0 1640 0 0 1573 171 0 275 152 9406 0 1958 0 0 0 324 0 118
Last week 36835 298 572 287 2617 194428 60734 13810 0 5443 4698 0 8139 501 50 366 1108 37730 42 11245 0 4068 93 1153 8916 336
Previous peak date2021-06-052021-07-262022-01-192021-10-142021-06-102021-09-182021-11-132021-06-262022-01-252021-06-052022-01-152021-11-062021-08-242021-09-1806-042021-08-132021-08-232022-01-19 --2022-01-142021-06-042021-06-052021-09-212021-12-092021-06-062021-10-05
Previous peak daily increment 25322 172 826 370 2614 92852 2476 29569 7160 1203 8554 1196 3774 232 180 1515 759 43483 10293 2935 3719 480 788 3221 1476
Low between peaks 898 -2 84 -8 287 2340 968 1351 361 163 70 8 203 31 4 5 27 2342 231 -277 60 16 234 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-06-17 to 2022-06-23

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruTrinidad and TobagoUruguayVenezuela
2022-06-16 9313453 35516 82643 61150 914252 31611769 3857643 6131657 594964 892176 876499 66300 425930 141026 5852596 898882 3592765 165468 943877 524488
2022-06-17 9313000 35560 82760 61470 914500 31657000 3862000 6149000 595500 892900 877700 66390 425900 141500 5853000 901200 3593000 166100 943900 524500
2022-06-18 9315000 35610 83030 61500 914500 31665000 3869000 6160000 595500 892900 879400 66420 425900 141800 5853000 905700 3594000 166500 943900 524500
2022-06-19 9325000 35660 83220 61510 914500 31668000 3876000 6171000 595500 892900 880000 66470 426000 142100 5853000 909100 3595000 166800 943900 524500
2022-06-20 9325000 35710 83370 61770 914700 31701000 3884000 6182000 595700 893300 880500 66530 426000 142400 5853000 912000 3596000 167100 946800 524600
2022-06-21 9371000 35750 83510 61820 914900 31742000 3886000 6193000 596100 893300 881900 66600 426000 142600 5858000 914700 3596000 167300 954600 524600
2022-06-22 9374000 35790 83630 61880 915100 31799000 3896000 6204000 596600 893600 883200 66680 426000 142800 5866000 917100 3597000 167600 954600 524700
2022-06-23 9374000 35830 83740 61950 915400 31810000 3907000 6208000 597200 893900 884500 66760 426200 143100 5871000 919500 3598000 167900 958000 524700

Confirmed count average forecast Latin America (bold black line in graphs) 2022-06-17 to 2022-06-23

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruTrinidad and TobagoUruguayVenezuela
2022-06-16 9313453 35516 82643 61150 914252 31611769 3857643 6131657 594964 892176 876499 66300 425930 141026 5852596 898882 3592765 165468 943877 524488
2022-06-17 9315000 35580 82760 61240 914600 31652000 3869000 6136000 595600 892400 877500 66400 426000 141300 5862000 901300 3593000 165800 943400 524500
2022-06-18 9322000 35630 82880 61260 914800 31665000 3879000 6138000 596000 893100 879100 66470 426000 141500 5866000 904000 3594000 166000 944300 524600
2022-06-19 9337000 35680 83000 61270 915000 31672000 3888000 6139000 596500 893800 879600 66530 426000 141800 5868000 906100 3595000 166100 945500 524600
2022-06-20 9338000 35710 83080 61480 915200 31704000 3896000 6141000 597000 894800 879900 66570 426000 142100 5870000 907800 3595000 166200 949900 524700
2022-06-21 9366000 35750 83190 61550 915500 31746000 3899000 6143000 597600 895500 881200 66660 426000 142300 5876000 910400 3596000 166300 955200 524700
2022-06-22 9370000 35800 83310 61690 915800 31791000 3906000 6145000 598200 896600 882400 66770 426000 142500 5881000 913000 3597000 166500 955900 524700
2022-06-23 9373000 35830 83420 61860 916100 31821000 3916000 6150000 598900 897900 883400 66890 426100 142700 5884000 915500 3597000 166700 957100 524800

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