COVID-19 short-term forecasts Deaths 2021-05-17 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 Deaths in Latin America 2021-05-17

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-182021-05-0212-032021-02-032021-04-062021-03-29 -- --09-0309-072021-01-032021-02-102021-05-0807-122021-05-122021-04-092021-04-102021-01-122021-04-172021-04-1608-112021-05-132021-04-162021-04-20
Peak daily increment 249 6 6 60 2995 116 22 3440 25 34 3 3 49 8 1512 46 103 360 4 14 65 19
Days since peak 119 15 165 103 41 49 256 252 134 96 9 309 5 38 37 125 30 31 279 4 31 27
Last total 71027 221 323 13566 436537 27934 81809 3625 3589 19786 2191 7893 344 271 5970 863 220493 6297 7692 66220 230 294 3521 2411
Last daily increment 505 5 0 49 786 102 509 78 7 87 4 6 3 0 10 3 56 1 96 0 2 9 62 15
Last week 2716 7 0 308 10997 578 3038 169 39 500 27 117 13 7 181 49 1170 15 408 1847 10 70 313 91
Previous peak date10-0108-24 --09-0707-2107-172021-01-2209-2104-1205-1008-0706-05 -- --07-30 --10-0507-23 --07-23 -- -- --09-20
Previous peak daily increment 2487 8 1439 1066 787 389 20 23 166 11 43 35 1833 28 2944 9
Low between peaks 120 0 3 371 37 4 14 2 11 5 343 10 48 3

Deaths count forecast Latin America (bold red line in graphs) 2021-05-18 to 2021-05-24

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-05-17 71027 13566 436537 27934 81809 3625 3589 19786 2191 7893 5970 863 220493 7692 66220 294 3521 2411
2021-05-18 71340 13600 439700 28010 82280 3661 3595 19850 2195 7929 6009 866 220900 7766 66610 298 3570 2427
2021-05-19 71750 13600 442600 28040 82660 3695 3597 19910 2197 7963 6050 866 221300 7877 66910 308 3633 2445
2021-05-20 72130 13600 445000 28190 83070 3732 3601 19970 2201 7982 6088 866 221700 7970 67210 318 3689 2463
2021-05-21 72620 13610 447100 28310 83500 3761 3606 20040 2204 7996 6126 866 222100 8052 67490 327 3740 2479
2021-05-22 72880 13630 449200 28390 83930 3780 3610 20100 2207 8017 6164 866 222300 8132 67760 336 3790 2494
2021-05-23 73080 13660 449900 28480 84370 3784 3616 20160 2211 8027 6201 866 222400 8207 68030 345 3839 2509
2021-05-24 73560 13690 450700 28580 84810 3845 3621 20230 2215 8033 6238 866 222500 8281 68300 355 3887 2524

Deaths count average forecast Latin America (bold black line in graphs) 2021-05-18 to 2021-05-24

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-05-17 71027 13566 436537 27934 81809 3625 3589 19786 2191 7893 5970 863 220493 7692 66220 294 3521 2411
2021-05-18 71470 13600 438700 27990 82270 3659 3596 19840 2195 7912 5999 868 220800 7771 66450 304 3573 2428
2021-05-19 71900 13630 441100 28040 82700 3684 3601 19880 2198 7936 6043 871 221100 7849 66700 313 3622 2441
2021-05-20 72310 13650 443300 28130 83130 3712 3606 19940 2201 7957 6084 874 221400 7923 66870 323 3671 2453
2021-05-21 72770 13680 445300 28210 83560 3738 3612 19980 2204 7977 6124 878 221700 7993 67140 332 3720 2466
2021-05-22 73110 13700 447500 28280 83990 3755 3617 20030 2207 7998 6164 882 222000 8063 67330 342 3768 2478
2021-05-23 73420 13720 448700 28350 84440 3769 3623 20070 2210 8018 6204 886 222100 8132 67560 352 3817 2491
2021-05-24 73850 13740 449900 28430 84890 3802 3629 20100 2213 8037 6243 890 222200 8201 67720 362 3867 2503

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 Deaths