COVID-19 short-term forecasts Confirmed 2022-05-13 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 2022-05-13

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-04-302022-01-172022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-04-292022-01-142022-04-072021-09-272022-01-142022-01-172022-01-192022-01-182021-12-092022-01-202022-01-25
Peak daily increment 112477 1008 426 814 10699 182433 36050 30553 6209 6246 8554 8671 3374 925 525 1854 1352 21585 140 10293 8807 47146 944 788 11003 2156
Days since peak 120 124 13 116 119 105 94 118 108 119 118 88 94 117 116 14 119 36 228 119 116 114 115 155 113 108
Last total 9101319 33830 75845 57896 905994 30664739 3595968 6095316 866164 580336 872268 162089 851877 63772 30713 423775 131980 5745652 18491 803223 649718 3571516 80008 153471 902540 522921
Last daily increment 0 0 407 60 174 25609 4871 0 0 135 1222 0 662 36 0 0 276 0 0 3248 0 355 0 0 0 81
Last week 17646 135 2647 284 566 106209 19829 1671 8874 593 1742 0 2460 202 10 0 1193 5972 0 16367 0 2190 615 2236 2817 249
Previous peak date2021-06-052021-07-262022-01-192021-10-142021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-1806-042022-02-162021-08-232022-01-1905-262021-06-292021-06-042021-06-052021-09-152021-06-052021-06-062021-10-05
Previous peak daily increment 25322 188 826 370 2614 92852 2476 29569 2470 1203 1229 1336 3774 232 187 8637 759 43483 182 1107 2942 3719 485 365 3221 1476
Low between peaks 898 -33 84 -8 287 2340 968 1351 -225 163 197 9 203 31 -3 -14 27 2342 2 129 -743 60 16 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-05-14 to 2022-05-20

DateArgentinaBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-05-13 9101319 75845 57896 905994 30664739 3595968 6095316 866164 580336 872268 851877 63772 131980 5745652 803223 3571516 80008 153471 902540 522921
2022-05-14 9103000 77030 57950 906000 30672000 3601000 6095000 866200 580300 872300 852800 63820 132100 5746000 806100 3572000 80050 154300 902800 523000
2022-05-15 9103000 77630 57980 906000 30674000 3604000 6095000 866200 580300 872300 853200 63870 132100 5746000 809300 3573000 80070 154700 902800 523000
2022-05-16 9103000 78130 58030 906100 30681000 3610000 6095000 866200 580300 872700 853300 63910 132100 5747000 812200 3574000 80080 154900 902800 523000
2022-05-17 9103000 78780 58060 906200 30699000 3613000 6095000 866200 580400 872700 853900 63950 132100 5748000 815000 3575000 80080 155400 903000 523000
2022-05-18 9103000 79280 58100 906200 30719000 3616000 6095000 869800 580400 872700 854200 63980 132100 5748000 817700 3575000 80100 155900 903200 523000
2022-05-19 9103000 80050 58130 906300 30740000 3622000 6096000 870000 580500 873000 855100 64020 132200 5748000 820500 3576000 80120 156400 903400 523100
2022-05-20 9103000 80350 58180 906400 30759000 3625000 6096000 870900 580500 873700 855800 64050 132300 5748000 823200 3576000 80120 156500 903700 523100

Confirmed count average forecast Latin America (bold black line in graphs) 2022-05-14 to 2022-05-20

DateArgentinaBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaGuyanaJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-05-13 9101319 75845 57896 905994 30664739 3595968 6095316 866164 580336 872268 851877 63772 131980 5745652 803223 3571516 80008 153471 902540 522921
2022-05-14 9101000 76370 57930 906100 30681000 3600000 6096000 866200 580400 872600 852500 63830 132100 5746000 806700 3572000 80030 154000 902500 523000
2022-05-15 9108000 76750 57940 906100 30685000 3601000 6096000 866200 580500 872700 852600 63850 132200 5746000 809100 3573000 80080 154300 902900 523000
2022-05-16 9108000 77090 57990 906200 30692000 3605000 6096000 866500 580500 872900 852600 63880 132300 5747000 811300 3573000 80140 154600 903300 523000
2022-05-17 9108000 77580 58030 906200 30712000 3607000 6096000 869400 580600 873000 853100 63900 132300 5747000 815100 3574000 80430 155000 904200 523100
2022-05-18 9109000 77900 58060 906300 30731000 3609000 6096000 871400 580600 873100 853500 63950 132400 5748000 817000 3574000 80490 155600 904900 523100
2022-05-19 9110000 78560 58090 906300 30748000 3612000 6097000 871800 580700 873300 854300 63980 132500 5748000 819000 3575000 80530 156100 905400 523100
2022-05-20 9110000 78960 58130 906400 30763000 3615000 6097000 872300 580700 873600 854900 64000 132700 5748000 821600 3575000 80570 156500 905900 523100

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