COVID-19 short-term forecasts Deaths 2021-07-16 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 2021-07-16

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-10 --12-032021-06-152021-04-062021-03-292021-07-122021-05-2409-0309-072021-01-032021-07-012021-05-26 --2021-05-122021-05-162021-06-012021-01-122021-06-082021-04-112021-06-132021-05-282021-04-172021-05-16
Peak daily increment 569 6 87 2996 118 1410 31 22 3440 25 53 7 45 8 3098 46 129 813 8 15 64 25
Days since peak 36 225 31 101 109 4 53 316 312 194 15 51 65 61 45 185 38 96 33 49 90 61
Last total 101158 273 332 17375 540398 34309 115333 4874 3910 21899 2477 9871 507 487 7391 1140 236015 6688 14178 194935 607 982 5871 3350
Last daily increment 463 17 0 0 1456 102 500 17 3 27 7 37 1 0 0 4 275 14 58 90 5 13 6 11
Last week 2657 21 2 175 7505 542 3602 101 21 85 38 183 15 16 141 26 1108 61 311 1705 32 47 64 72
Previous peak date10-01 -- --09-0707-2107-172021-05-0309-2804-1205-1008-0706-05 --07-1207-30 --10-0507-23 --07-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 536 18 22 167 11 43 3 35 1724 28 918 9
Low between peaks 108 3 371 37 83 4 4 14 2 11 5 175 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-07-17 to 2021-07-23

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-07-16 101158 273 17375 540398 34309 115333 4874 3910 21899 2477 9871 7391 1140 236015 6688 14178 194935 607 982 5871 3350
2021-07-17 101600 280 17420 542400 34500 115700 4874 3915 21920 2483 9920 7420 1146 236200 6697 14220 195200 612 990 5881 3364
2021-07-18 101900 286 17510 543400 34620 116100 4877 3926 21970 2486 9940 7440 1150 236300 6698 14270 195400 618 1002 5899 3386
2021-07-19 102400 292 17570 544200 34730 116500 4890 3933 22000 2490 9950 7462 1154 236400 6703 14310 195600 623 1012 5913 3404
2021-07-20 102800 299 17620 545900 34760 116900 4927 3940 22020 2495 9990 7483 1158 236600 6709 14350 195700 629 1021 5925 3419
2021-07-21 103400 305 17670 547500 34800 117300 4938 3946 22050 2499 10040 7505 1163 236900 6716 14390 195900 633 1030 5936 3434
2021-07-22 103800 312 17710 548900 34930 117600 4950 3951 22070 2504 10070 7527 1167 237200 6723 14430 196000 638 1038 5946 3448
2021-07-23 104200 319 17750 550400 35040 118000 4966 3956 22090 2510 10100 7550 1171 237500 6731 14480 196100 643 1046 5955 3462

Deaths count average forecast Latin America (bold black line in graphs) 2021-07-17 to 2021-07-23

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-07-16 101158 273 17375 540398 34309 115333 4874 3910 21899 2477 9871 7391 1140 236015 6688 14178 194935 607 982 5871 3350
2021-07-17 101500 280 17430 541700 34420 115800 4882 3915 21920 2483 9900 7412 1144 236200 6698 14240 195100 612 991 5883 3363
2021-07-18 101800 281 17470 542400 34520 116100 4886 3921 21930 2487 9940 7432 1148 236300 6704 14300 195200 617 1000 5903 3379
2021-07-19 102200 283 17510 543100 34610 116600 4896 3926 21950 2492 9960 7451 1152 236400 6711 14350 195400 621 1009 5922 3393
2021-07-20 102600 285 17540 544500 34650 117000 4920 3931 21960 2496 9990 7470 1157 236600 6719 14410 195500 626 1017 5941 3406
2021-07-21 103100 287 17570 545900 34700 117400 4930 3936 21970 2501 10030 7489 1161 236900 6727 14460 195700 631 1024 5959 3419
2021-07-22 103500 289 17600 547200 34800 117800 4939 3940 21980 2505 10060 7508 1165 237100 6735 14510 195800 635 1032 5977 3432
2021-07-23 103900 291 17620 548500 34890 118200 4950 3945 22000 2510 10090 7526 1169 237300 6743 14570 196000 640 1040 5995 3445

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