COVID-19 short-term forecasts Confirmed 2022-04-27 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 2022-04-27

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-102022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-142022-04-072021-09-282022-01-142022-01-242022-01-192022-01-182021-12-092022-01-222022-01-25
Peak daily increment 112477 929 826 763 10699 182433 36050 30553 6209 6246 8554 8731 3374 916 482 8075 1352 21060 137 10293 8637 47145 911 788 11055 2156
Days since peak 104 108 98 107 103 89 78 102 92 103 102 72 78 101 100 70 103 20 211 103 93 98 99 139 95 92
Last total 9072230 33511 68913 57477 904758 30399004 3551383 6091959 852074 579248 868285 162089 844059 63447 30655 423101 129747 5736579 18491 773675 649034 3563151 79336 146140 895775 522263
Last daily increment 0 13 473 10 101 20943 2056 206 4290 134 0 0 790 27 0 556 66 1464 0 845 0 851 0 543 0 20
Last week 11307 81 2596 71 546 68375 12540 1439 4290 294 1115 0 3582 48 40 826 374 4944 0 3212 352 4538 34 2675 0 207
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-1806-012021-08-132021-08-232022-01-1905-262021-06-292021-06-082021-06-052021-09-212021-06-052021-04-092021-10-05
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 5284 1476
Low between peaks 898 -11 41 2 287 2340 968 1351 -225 163 197 9 203 31 5 5 27 1974 4 129 -154 60 12 170 97 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-04-28 to 2022-05-04

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2022-04-27 9072230 68913 904758 30399004 3551383 6091959 852074 579248 868285 844059 423101 129747 5736579 773675 649034 3563151 146140 522263
2022-04-28 9073000 69290 904800 30427000 3557000 6092000 852100 579200 869200 844300 423200 129800 5737000 774100 649000 3564000 146100 522300
2022-04-29 9074000 69430 904900 30444000 3562000 6092000 852100 579300 869400 844800 423300 129800 5738000 774100 649300 3564000 146300 522500
2022-04-30 9074000 69670 905000 30453000 3566000 6092000 852100 579300 869900 845500 423500 129800 5739000 774100 649300 3564000 146500 522600
2022-05-01 9074000 69970 905100 30458000 3569000 6093000 852200 579300 869900 845600 423500 129800 5741000 774100 649300 3565000 146600 522600
2022-05-02 9078000 70280 905100 30467000 3571000 6093000 852300 579400 870700 845700 423700 129800 5741000 774200 649300 3565000 146900 522700
2022-05-03 9078000 70620 905200 30485000 3573000 6093000 852900 579400 870700 846300 423800 129800 5741000 774500 649500 3566000 147300 522700
2022-05-04 9078000 70980 905300 30507000 3575000 6093000 856000 579500 870700 847000 424100 129900 5742000 774900 649500 3566000 147800 522800

Confirmed count average forecast Latin America (bold black line in graphs) 2022-04-28 to 2022-05-04

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2022-04-27 9072230 68913 904758 30399004 3551383 6091959 852074 579248 868285 844059 423101 129747 5736579 773675 649034 3563151 146140 522263
2022-04-28 9074000 69450 904800 30416000 3554000 6092000 852500 579300 868400 844700 423200 129800 5738000 774200 649000 3564000 146700 522300
2022-04-29 9075000 69770 904900 30427000 3557000 6092000 852500 579300 868500 845200 423300 129800 5738000 774500 649200 3564000 147000 522400
2022-04-30 9077000 70130 904900 30432000 3559000 6092000 852500 579400 868900 845800 423300 129900 5739000 774700 649300 3565000 147300 522500
2022-05-01 9078000 70410 905000 30435000 3561000 6093000 852600 579400 869000 845900 423300 129900 5739000 775000 649300 3565000 147500 522500
2022-05-02 9081000 70650 905000 30442000 3562000 6093000 852600 579400 869500 846000 423400 130000 5740000 775200 649300 3566000 147700 522500
2022-05-03 9083000 71050 905100 30460000 3563000 6093000 853000 579500 869700 846400 423500 130000 5740000 775600 649400 3567000 148000 522600
2022-05-04 9084000 71460 905200 30486000 3565000 6093000 855100 579500 869800 846900 423600 130000 5741000 776000 649400 3567000 148300 522600

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