COVID-19 short-term forecasts Confirmed 2022-02-13 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 2022-02-13

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-172022-01-142022-02-08 --2022-01-152022-02-072022-01-142022-01-152022-01-292022-01-072022-01-142022-01-172021-08-132022-01-142022-01-152021-08-192022-01-142022-02-082022-01-212022-01-172021-12-092022-01-202022-01-25
Peak daily increment 114966 997 859 779 11796 183168 30802 5846 6323 8603 4157 2267 935 464 1499 1380 41110 167 10537 8314 43061 939 788 10838 2079
Days since peak 31 35 25 27 30 5 29 6 30 29 15 37 30 27 184 30 29 178 30 5 23 27 66 24 19
Last total 8734551 32931 51624 55183 883382 27492904 2621427 6020095 757093 569446 781470 135109 735099 62156 29969 391874 127054 5283852 17791 742854 624475 3445680 77036 120385 777490 504719
Last daily increment 6289 6 313 0 733 58618 38493 5532 0 422 0 0 1139 0 30 0 168 0 0 1203 3767 9927 61 382 4733 1557
Last week 119266 129 2966 1004 8476 876890 215755 44309 23385 5789 12321 0 23042 540 137 0 832 123085 82 15441 14268 82191 1147 4542 44480 8436
Previous peak date2021-06-052021-10-182021-10-262021-10-142021-06-102021-09-182021-06-122021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-182021-06-082021-02-032021-08-232021-08-1105-262021-06-292021-06-082021-06-052021-09-152021-06-052021-06-062021-10-05
Previous peak daily increment 25322 184 347 370 2614 92851 6996 29569 2470 1203 1229 1386 3774 232 180 1308 759 18310 176 1107 2668 3718 485 365 3221 1476
Low between peaks 898 -2 41 -8 287 2340 1351 -225 163 197 21 203 31 4 551 27 617 -35 129 -154 60 16 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-02-14 to 2022-02-20

DateArgentinaBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-02-13 8734551 51624 55183 883382 27492904 2621427 6020095 757093 569446 781470 735099 62156 127054 5283852 742854 624475 3445680 77036 120385 777490 504719
2022-02-14 8788000 52460 56320 885500 27682000 2662000 6027000 768700 570200 789300 735100 62340 127300 5313000 746400 637500 3464000 77330 120700 785400 506300
2022-02-15 8829000 53300 57350 896900 27899000 2684000 6039000 776200 571400 807800 738300 62920 127700 5373000 761000 643500 3535000 78210 121500 800200 509100
2022-02-16 8860000 53960 57880 903800 28098000 2712000 6049000 781700 572400 821200 742200 63310 128000 5416000 770200 648000 3581000 78810 122200 811800 511400
2022-02-17 8884000 54510 58250 908300 28292000 2751000 6057000 787200 573300 831800 746100 63600 128300 5451000 776700 652000 3614000 79260 122900 821600 513200
2022-02-18 8907000 54980 58600 912300 28465000 2759000 6065000 792100 574100 841700 749400 63870 128600 5483000 782600 654300 3643000 79670 123700 831100 515000
2022-02-19 8916000 55390 58600 915100 28598000 2780000 6072000 792100 574900 850100 753100 64090 128800 5511000 786900 654700 3665000 80000 124500 839600 516600
2022-02-20 8922000 55740 58600 917400 28654000 2825000 6078000 792100 575600 858000 754000 64280 129000 5538000 790700 657100 3685000 80300 124800 847800 518100

Confirmed count average forecast Latin America (bold black line in graphs) 2022-02-14 to 2022-02-20

DateArgentinaBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-02-13 8734551 51624 55183 883382 27492904 2621427 6020095 757093 569446 781470 735099 62156 127054 5283852 742854 624475 3445680 77036 120385 777490 504719
2022-02-14 8747000 52010 55820 885400 27562000 2661000 6024000 766600 570000 780400 735700 62110 127200 5289000 745700 634500 3453000 77250 120800 784100 506000
2022-02-15 8767000 52720 56710 891300 27752000 2691000 6032000 774500 570700 784500 739200 62260 127400 5310000 751900 638500 3487000 77590 121600 793500 507800
2022-02-16 8785000 53350 57210 895900 27939000 2727000 6039000 780600 571300 787300 743300 62380 127500 5330000 757000 641900 3509000 77880 122300 802200 509400
2022-02-17 8801000 53880 57560 899800 28133000 2770000 6046000 786500 571900 795600 747500 62490 127600 5342000 761300 644900 3528000 78130 122900 810800 510900
2022-02-18 8821000 54430 57990 903300 28354000 2798000 6054000 793000 572400 800300 751000 62610 127700 5399000 765300 648400 3573000 78340 123700 819300 512400
2022-02-19 8840000 54980 58140 906700 28542000 2822000 6063000 793800 572900 805600 754500 62800 127800 5444000 768700 653400 3607000 78520 124500 827000 513800
2022-02-20 8848000 55410 58440 910000 28639000 2863000 6070000 794300 573400 808100 755400 62950 127900 5458000 772200 656200 3629000 78660 124800 833600 515200

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