COVID-19 short-term forecasts Confirmed 2022-03-17 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 2022-03-17

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-172022-01-142022-01-282022-02-142022-01-152022-01-252022-01-142022-01-152022-02-142022-02-142022-01-172022-01-172022-02-162022-01-132022-01-192021-10-192022-01-142022-01-242022-01-192022-01-172021-12-092022-01-202022-01-25
Peak daily increment 112477 985 826 785 10699 182434 36772 30553 6209 6246 8554 7231 3514 931 517 7668 1362 43483 139 10293 8637 47146 939 788 11003 2156
Days since peak 63 67 57 59 62 48 31 61 51 62 61 31 31 59 59 29 63 57 149 62 52 57 59 98 56 51
Last total 8990413 33206 57451 57131 898941 29532810 3353259 6079231 828442 577070 849699 160305 812125 63175 30478 417201 128456 5624954 18290 760735 646630 3539400 78893 134272 871081 519059
Last daily increment 4577 0 103 18 477 44033 16645 744 1096 166 0 3941 2539 6 0 0 30 11084 0 330 135 1912 33 484 2129 0
Last week 23203 15 646 97 1843 219582 84197 3575 5507 703 5939 3941 9381 68 40 2487 138 33083 87 1511 975 4713 153 1889 8928 1339
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-192021-06-082021-08-132021-08-232021-08-1105-262021-06-292021-06-082021-06-052021-09-152021-06-052021-06-062021-10-05
Previous peak daily increment 25322 175 347 1073 2614 92852 2476 29569 2470 1203 1229 1526 3774 233 185 1515 773 18310 216 1107 2669 3719 493 365 3221 1476
Low between peaks 898 -25 41 2 287 2340 968 1351 -225 163 197 5 203 27 -3 5 21 617 4 129 -154 60 14 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-03-18 to 2022-03-24

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-17 8990413 57451 898941 29532810 3353259 6079231 828442 577070 849699 160305 812125 417201 5624954 760735 646630 3539400 134272 871081 519059
2022-03-18 9008000 57560 899300 29626000 3369000 6080000 831700 577200 851900 160300 815800 417200 5637000 761200 647000 3541000 135000 873000 519300
2022-03-19 9017000 57640 899500 29695000 3404000 6081000 833000 577700 853200 160300 818700 418500 5658000 761900 647300 3545000 135500 876100 519900
2022-03-20 9023000 57700 899800 29717000 3429000 6082000 833500 578000 853500 160300 819300 418500 5663000 762500 647600 3548000 135800 878600 520400
2022-03-21 9029000 57750 900100 29747000 3449000 6083000 836300 578200 858800 160600 819900 418500 5664000 762900 647700 3550000 136000 880800 520700
2022-03-22 9037000 57860 900300 29766000 3469000 6083000 838000 578300 859100 160600 822500 419200 5669000 763300 648500 3552000 136300 882900 521100
2022-03-23 9044000 57930 900600 29803000 3485000 6084000 838800 578500 859900 160600 824500 419200 5674000 763700 648900 3553000 136700 884900 521300
2022-03-24 9049000 58040 900800 29840000 3501000 6084000 840100 578600 860300 162900 826800 419400 5686000 764000 649300 3555000 137100 886800 521600

Confirmed count average forecast Latin America (bold black line in graphs) 2022-03-18 to 2022-03-24

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-17 8990413 57451 898941 29532810 3353259 6079231 828442 577070 849699 160305 812125 417201 5624954 760735 646630 3539400 134272 871081 519059
2022-03-18 8997000 57560 899300 29594000 3372000 6080000 829300 577200 849400 161300 814100 417200 5626000 761100 646800 3541000 134700 872800 519300
2022-03-19 9001000 57660 899500 29643000 3398000 6081000 829700 577400 850100 161700 816000 418300 5639000 761400 647100 3543000 135100 874100 519500
2022-03-20 9002000 57740 899800 29657000 3420000 6081000 829900 577600 850500 162000 816300 418500 5642000 761900 647400 3545000 135400 875200 519800
2022-03-21 9005000 57800 900000 29673000 3436000 6081000 831700 577700 854100 162400 816500 418600 5642000 762100 647500 3546000 135500 876400 520000
2022-03-22 9009000 57930 900300 29704000 3449000 6082000 833100 577800 854400 162800 819100 419100 5645000 762500 648100 3547000 135800 877500 520200
2022-03-23 9013000 58050 900500 29730000 3468000 6082000 834000 577900 855500 163100 821400 419400 5653000 762700 648600 3548000 136200 878600 520400
2022-03-24 9018000 58160 900700 29771000 3493000 6083000 835100 578000 857000 163900 823400 419600 5662000 763200 649000 3549000 136600 879900 520600

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