COVID-19 short-term forecasts Confirmed 2022-06-09 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 2022-06-09

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-05-052022-01-172022-01-142022-01-282022-05-282022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-142022-04-07 --2022-05-182022-01-242022-01-192022-01-182022-05-092022-01-202022-01-25
Peak daily increment 112477 942 628 814 10699 182433 7408 30553 7160 6246 8554 7430 3374 925 494 8195 1352 21585 3556 8637 47146 913 528 11003 2156
Days since peak 147 151 35 143 146 132 12 145 135 146 145 115 121 144 143 113 146 63 22 136 141 142 31 140 135
Last total 9276618 35163 81929 60694 911635 31360850 3783945 6117847 904934 589521 887478 164134 868360 65714 30963 425564 139456 5802672 18491 885046 651268 3587906 80673 163885 934961 524085
Last daily increment 0 31 129 201 447 45073 12436 8742 0 543 1255 0 1059 154 0 93 0 0 3914 2793 0 764 0 378 0 116
Last week 46045 232 755 906 1716 223371 54039 8742 0 3954 9282 1379 4765 566 71 193 1346 20267 3953 16250 607 4503 126 1444 9184 316
Previous peak date2021-06-052021-07-262022-01-192021-10-142021-06-102021-09-182022-02-082021-06-262021-05-102021-06-052021-06-292021-11-062021-08-242021-09-1806-042021-08-162021-08-232022-01-192021-10-062022-01-142021-06-082021-06-052021-09-212021-12-092021-06-062021-10-05
Previous peak daily increment 25322 172 826 370 2614 92852 36050 29569 2427 1203 1229 1526 3774 232 180 1486 759 43483 145 10293 2669 3719 480 788 3221 1476
Low between peaks 898 -2 84 -8 287 2340 1924 1351 -324 163 197 5 203 31 4 3 27 2342 231 -154 60 16 234 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-06-10 to 2022-06-16

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaJamaicaMexicoNicaraguaPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-06-09 9276618 35163 81929 60694 911635 31360850 3783945 6117847 589521 887478 164134 868360 65714 139456 5802672 18491 885046 651268 3587906 163885 934961 524085
2022-06-10 9297000 35220 82090 60690 911800 31362000 3785000 6133000 589600 888100 164300 869300 65800 140000 5803000 18490 887800 651600 3588000 164500 935700 524100
2022-06-11 9297000 35300 82490 60690 911800 31362000 3789000 6140000 589600 888500 164300 870200 65800 140000 5803000 18650 890800 651600 3589000 165000 935700 524100
2022-06-12 9341000 35360 82780 60690 911800 31362000 3795000 6148000 589600 889000 164400 870500 65800 140200 5803000 18780 893600 651600 3589000 165300 935700 524200
2022-06-13 9341000 35410 83000 60690 911900 31374000 3801000 6156000 589600 889200 164500 870700 65840 141000 5809000 18970 896200 651600 3590000 165500 936600 524200
2022-06-14 9341000 35460 83200 60690 912100 31419000 3806000 6165000 589600 889700 164600 871500 65900 141100 5810000 19260 898900 651600 3590000 165800 940900 524200
2022-06-15 9341000 35510 83370 60770 912300 31461000 3815000 6173000 589600 890500 164600 872600 65960 141300 5810000 19500 901500 651600 3591000 166100 940900 524300
2022-06-16 9341000 35560 83520 60950 912500 31495000 3825000 6175000 589600 891400 164700 873400 66030 141400 5810000 20940 904100 651700 3591000 166400 940900 524300

Confirmed count average forecast Latin America (bold black line in graphs) 2022-06-10 to 2022-06-16

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaJamaicaMexicoNicaraguaPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-06-09 9276618 35163 81929 60694 911635 31360850 3783945 6117847 589521 887478 164134 868360 65714 139456 5802672 18491 885046 651268 3587906 163885 934961 524085
2022-06-10 9280000 35210 82060 60850 911900 31406000 3793000 6121000 590100 888300 164200 869400 65810 139700 5803000 19470 888000 651300 3589000 164100 935700 524100
2022-06-11 9288000 35260 82200 60880 912100 31415000 3801000 6122000 590400 889400 164300 870200 65860 139800 5806000 19500 890900 651500 3589000 164600 936500 524200
2022-06-12 9338000 35320 82400 60890 912200 31422000 3808000 6123000 590800 890600 164500 870300 65900 140000 5807000 19550 893100 651600 3590000 164700 937600 524200
2022-06-13 9339000 35360 82480 61090 912400 31456000 3814000 6124000 591100 892500 164600 870400 65940 140700 5812000 19600 895300 651600 3590000 164800 938800 524200
2022-06-14 9344000 35400 82710 61230 912600 31502000 3818000 6125000 591500 893800 164700 871200 66020 140900 5815000 19660 898200 651600 3591000 165100 944800 524300
2022-06-15 9347000 35450 82880 61360 912800 31534000 3825000 6126000 591900 894700 164800 872000 66130 141100 5817000 19730 901300 651700 3591000 165400 945200 524300
2022-06-16 9353000 35490 83030 61510 913000 31564000 3833000 6129000 592300 896100 164800 872800 66210 141300 5818000 19930 904400 651800 3592000 165700 945400 524300

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