COVID-19 short-term forecasts Confirmed 2022-04-06 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 2022-04-06

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 36050 30553 6209 6246 8554 7430 3374 916 482 9050 1338 43483 137 10293 8637 47146 911 788 11003 2156
Days since peak 83 87 77 86 82 68 57 81 71 82 81 51 57 80 79 49 83 77 190 82 72 77 78 118 76 71
Last total 9047408 33315 60890 57318 902749 30069094 3495889 6086811 841343 578319 862321 161570 832956 63294 30575 421170 128924 5683288 18491 765930 648353 3549511 79241 139397 891800 521105
Last daily increment 2082 1 298 0 0 26822 3927 327 0 0 0 0 797 11 8 49 55 12144 0 253 0 483 0 283 820 75
Last week 9497 32 1618 38 806 117424 24953 1895 2806 299 2431 518 4082 41 28 108 167 23753 57 1394 403 2815 20 1537 3865 732
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-1806-012021-08-162021-08-232021-08-1105-262021-06-292021-06-082021-06-052021-09-212021-06-052021-06-062021-10-05
Previous peak daily increment 25322 182 347 1122 2614 92852 2476 29569 2470 1203 1229 1526 3774 229 178 1520 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 18 26 617 4 129 -154 60 12 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-04-07 to 2022-04-13

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-04-06 9047408 60890 902749 30069094 3495889 6086811 841343 578319 862321 161570 832956 5683288 765930 648353 3549511 139397 891800 521105
2022-04-07 9053000 61140 902900 30121000 3505000 6087000 842900 578400 863900 161700 833800 5683000 766200 648600 3550000 139600 892400 521200
2022-04-08 9057000 61410 903400 30165000 3513000 6088000 843900 578500 864700 161700 834500 5685000 766500 649000 3551000 140000 893100 521300
2022-04-09 9058000 61690 903700 30190000 3519000 6088000 844000 578600 864800 161700 834900 5688000 766800 649100 3552000 140100 893700 521500
2022-04-10 9058000 61950 903900 30200000 3523000 6088000 844000 578700 865400 161900 834900 5690000 767000 649200 3552000 140300 894300 521600
2022-04-11 9061000 62190 904100 30216000 3526000 6088000 844300 578800 867200 161900 834900 5690000 767200 649400 3553000 140600 894900 521600
2022-04-12 9064000 62490 904300 30237000 3527000 6089000 845500 578800 867400 162000 835000 5693000 767500 649400 3553000 140900 895400 521700
2022-04-13 9066000 62720 904500 30266000 3532000 6089000 846200 578900 867400 162000 836000 5701000 767700 649500 3554000 141100 895900 521800

Confirmed count average forecast Latin America (bold black line in graphs) 2022-04-07 to 2022-04-13

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-04-06 9047408 60890 902749 30069094 3495889 6086811 841343 578319 862321 161570 832956 5683288 765930 648353 3549511 139397 891800 521105
2022-04-07 9049000 61150 902900 30097000 3501000 6087000 841800 578400 862500 161600 833700 5690000 766200 648400 3550000 139600 892500 521200
2022-04-08 9051000 61370 903100 30126000 3507000 6087000 842500 578400 862900 161700 834700 5692000 766400 648500 3550000 139900 893100 521300
2022-04-09 9052000 61590 903300 30142000 3511000 6088000 842500 578500 863200 161800 835200 5695000 766600 648600 3551000 139900 893600 521400
2022-04-10 9052000 61800 903500 30146000 3515000 6088000 842500 578500 863600 161900 835300 5697000 766900 648600 3551000 140200 893900 521500
2022-04-11 9054000 61980 903700 30160000 3518000 6088000 842800 578600 864900 162000 835700 5697000 767000 648800 3552000 140500 894400 521500
2022-04-12 9057000 62280 903800 30192000 3520000 6088000 843500 578600 865500 162000 837000 5700000 767300 648900 3552000 140700 895200 521600
2022-04-13 9059000 62480 904000 30222000 3524000 6088000 844800 578700 865900 162100 838400 5702000 767600 649000 3552000 140900 895900 521700

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