COVID-19 short-term forecasts Confirmed 2022-04-21 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 2022-04-21

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-102022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-142022-04-072021-09-282022-01-142022-01-242022-01-192022-01-182021-12-092022-01-202022-01-19
Peak daily increment 112477 929 826 763 10699 182433 36050 30553 6209 6246 8554 8731 3374 916 482 8075 1352 20368 137 10293 8637 47146 911 788 11003 2083
Days since peak 98 102 92 101 97 83 72 96 86 97 96 66 72 95 94 64 97 14 205 97 87 92 93 133 91 92
Last total 9060923 33430 66317 57406 904212 30330629 3538843 6090520 847784 578954 867170 162089 840477 63399 30615 422275 129373 5731635 18491 770463 648682 3558613 79302 143465 895775 522056
Last daily increment 0 9 547 12 86 18660 3309 274 0 165 245 0 1061 10 0 0 26 1075 0 487 0 744 0 546 0 0
Last week 979 42 2140 75 436 83327 12998 1139 2892 291 1585 0 3100 35 21 1007 205 6159 0 1993 236 4607 26 1612 324 110
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-1806-012021-08-132021-08-232022-01-1905-262021-06-292021-06-082021-06-052021-09-212021-06-052021-06-062021-05-16
Previous peak daily increment 25322 182 347 1122 2614 92852 2476 29569 2470 1203 1229 1490 3774 229 178 1515 759 43483 213 1107 2669 3719 478 365 3221 1834
Low between peaks 898 -11 41 2 287 2340 968 1351 -225 163 197 9 203 31 5 5 27 2595 4 129 -154 60 12 170 95 166

Confirmed count forecast Latin America (bold red line in graphs) 2022-04-22 to 2022-04-28

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguay
2022-04-21 9060923 66317 904212 30330629 3538843 6090520 847784 578954 867170 840477 422275 129373 5731635 770463 648682 3558613 143465 895775
2022-04-22 9062000 66650 904300 30361000 3543000 6091000 848100 579000 868000 841300 422300 129400 5734000 770700 648700 3559000 143700 895900
2022-04-23 9062000 66650 904500 30376000 3547000 6091000 848200 579000 868200 841900 422300 129400 5735000 770700 648700 3559000 143700 896100
2022-04-24 9062000 66810 904600 30383000 3549000 6091000 848200 579000 868200 842200 422300 129400 5735000 770800 648800 3559000 143900 896300
2022-04-25 9062000 67040 904700 30394000 3550000 6091000 848200 579100 869500 842600 422400 129500 5735000 771000 648800 3560000 144100 896400
2022-04-26 9063000 67290 904800 30414000 3552000 6091000 849000 579100 869700 842900 422600 129500 5738000 771200 648900 3560000 144300 896600
2022-04-27 9063000 67580 904900 30443000 3555000 6092000 851000 579100 869700 843600 422600 129500 5739000 771500 649000 3561000 144600 896600
2022-04-28 9063000 67890 905000 30461000 3558000 6092000 851100 579200 869900 844400 422600 129500 5740000 771700 649100 3561000 145100 896700

Confirmed count average forecast Latin America (bold black line in graphs) 2022-04-22 to 2022-04-28

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguay
2022-04-21 9060923 66317 904212 30330629 3538843 6090520 847784 578954 867170 840477 422275 129373 5731635 770463 648682 3558613 143465 895775
2022-04-22 9061000 66680 904300 30345000 3541000 6091000 848000 579000 867300 840900 422300 129400 5732000 770800 648800 3559000 143800 895800
2022-04-23 9061000 66930 904400 30353000 3544000 6091000 848000 579000 867500 841300 422400 129400 5733000 770900 648800 3560000 143900 895800
2022-04-24 9061000 67110 904500 30356000 3546000 6091000 848000 579100 867600 841500 422400 129400 5734000 771200 648900 3560000 144100 895900
2022-04-25 9061000 67420 904500 30363000 3546000 6091000 848100 579100 868500 841700 422600 129500 5735000 771300 649000 3561000 144300 895900
2022-04-26 9062000 67780 904600 30383000 3548000 6091000 848900 579100 868700 842200 422800 129500 5737000 771600 649000 3561000 144400 896100
2022-04-27 9063000 68080 904600 30411000 3550000 6091000 850300 579100 868900 843000 422900 129500 5740000 771900 649100 3562000 144800 896100
2022-04-28 9064000 68550 904700 30431000 3553000 6091000 850300 579200 869200 843500 422900 129600 5743000 772200 649200 3562000 145200 896200

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