COVID-19 short-term forecasts Confirmed 2022-03-21 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 2022-03-21

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-102022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-132022-01-192021-09-282022-01-142022-01-242022-01-192022-01-182021-12-092022-01-202022-01-25
Peak daily increment 112477 929 826 763 10699 182433 36048 30553 6209 6246 8554 7430 3374 916 482 8136 1338 43483 137 10293 8637 47146 911 788 11003 2156
Days since peak 67 71 61 70 66 52 41 65 55 66 65 35 41 64 63 33 67 61 174 66 56 61 62 102 60 55
Last total 9011367 33230 57800 57168 899999 29650082 3401145 6081639 831152 577354 854252 161052 816653 63195 30509 418300 128565 5634829 18290 761508 647268 3541994 79025 135328 876400 519724
Last daily increment 3614 9 67 25 302 12268 8167 508 1752 37 402 0 517 1 31 0 26 901 0 0 169 597 79 231 1087 54
Last week 30212 32 570 72 2029 209043 77821 3778 4901 566 4553 4688 9200 38 36 1099 147 26984 0 1403 985 4506 197 1935 9118 1264
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-1806-012021-08-132021-08-232021-08-1105-262021-06-292021-06-082021-06-052021-09-212021-06-052021-06-062021-10-05
Previous peak daily increment 25322 182 347 1122 2614 92852 2476 29569 2470 1203 1229 1526 3774 229 178 1515 747 18310 213 1107 2669 3719 478 365 3221 1476
Low between peaks 898 -11 41 2 287 2340 968 1351 -225 163 197 5 203 31 5 5 26 617 4 129 -154 60 12 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-03-22 to 2022-03-28

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-21 9011367 57800 899999 29650082 3401145 6081639 831152 577354 854252 161052 816653 418300 5634829 761508 647268 3541994 135328 876400 519724
2022-03-22 9016000 57940 900400 29687000 3407000 6082000 833200 577600 856200 161100 819500 419300 5645000 761600 647600 3543000 135800 877800 519900
2022-03-23 9022000 58070 900600 29720000 3420000 6083000 835300 577800 857700 161300 822300 419400 5658000 761900 648000 3544000 136300 880000 520400
2022-03-24 9027000 58170 900900 29747000 3430000 6084000 836700 578000 858300 163900 824800 419800 5671000 762000 648200 3545000 136800 881800 520800
2022-03-25 9038000 58260 901200 29785000 3438000 6084000 837800 578200 858300 164300 826800 420300 5674000 762200 648300 3546000 137100 883400 521100
2022-03-26 9040000 58390 901400 29819000 3445000 6085000 838200 578300 858600 164400 828500 420600 5681000 762300 648400 3546000 137500 884900 521300
2022-03-27 9041000 58450 901700 29819000 3452000 6085000 838200 578300 861000 164500 828500 420600 5684000 762400 648600 3547000 137700 886300 521600
2022-03-28 9044000 58520 902000 29821000 3458000 6085000 839700 578300 862300 164500 829000 420600 5685000 762500 648800 3547000 137900 887700 521800

Confirmed count average forecast Latin America (bold black line in graphs) 2022-03-22 to 2022-03-28

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-21 9011367 57800 899999 29650082 3401145 6081639 831152 577354 854252 161052 816653 418300 5634829 761508 647268 3541994 135328 876400 519724
2022-03-22 9016000 57910 900300 29689000 3414000 6082000 832200 577400 854200 161100 819100 418800 5636000 761900 647600 3543000 135600 877700 519900
2022-03-23 9020000 58020 900500 29723000 3434000 6082000 833600 577500 855400 161100 821500 419000 5643000 762200 648000 3543000 136000 879100 520100
2022-03-24 9024000 58120 900800 29763000 3453000 6083000 834800 577600 855900 162800 823900 419100 5653000 762600 648300 3544000 136400 880600 520300
2022-03-25 9034000 58200 901100 29810000 3472000 6083000 835600 577700 856100 163000 825800 419600 5654000 762800 648500 3545000 136700 882000 520500
2022-03-26 9037000 58310 901400 29856000 3487000 6084000 835900 577800 856800 163100 827700 419800 5665000 763000 648700 3546000 137100 883100 520700
2022-03-27 9038000 58390 901600 29866000 3504000 6084000 836500 577800 857800 163100 828000 420000 5670000 763200 649000 3546000 137300 884000 520900
2022-03-28 9041000 58450 901900 29879000 3515000 6084000 838500 577800 860500 163100 828400 420100 5672000 763400 649400 3547000 137500 884900 521000

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