COVID-19 short-term forecasts Deaths 2021-09-01 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 2021-09-01

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-10 --12-032021-06-122021-04-062021-03-292021-06-252021-05-2409-032021-07-202021-08-012021-06-292021-05-262021-06-282021-05-12 --2021-06-0105-262021-01-122021-06-082021-04-142021-06-102021-05-282021-04-172021-05-16
Peak daily increment 568 6 82 2995 116 654 31 22 7798 12 57 7 5 45 2955 8 46 129 814 8 15 64 25
Days since peak 83 272 81 148 156 68 100 363 43 31 64 98 65 112 92 463 232 85 140 83 96 137 108
Last total 112005 381 362 18473 581150 36945 125016 5523 4008 32244 2926 12007 624 586 8960 1549 260503 200 7066 15785 198295 725 1302 6034 4026
Last daily increment 193 27 0 21 737 8 71 17 0 0 8 81 2 0 110 31 1177 0 5 18 32 4 11 2 16
Last week 888 38 6 102 3585 167 449 106 1 78 54 313 18 2 158 96 4216 1 35 132 231 18 44 9 71
Previous peak date10-01 -- --09-0707-2107-172021-01-2209-2804-1209-072021-01-0306-05 --07-1207-302021-05-1910-05 --07-23 --07-18 -- -- --09-20
Previous peak daily increment 2487 1439 1066 787 389 18 23 3440 25 43 3 35 9 1833 28 917 9
Low between peaks 108 3 371 37 96 4 4 -17 3 11 0 5 171 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-09-02 to 2021-09-08

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-09-01 112005 381 18473 581150 36945 125016 5523 32244 2926 12007 8960 1549 260503 7066 15785 198295 1302 4026
2021-09-02 112300 381 18510 581600 36990 125000 5533 32280 2934 12060 8998 1579 260900 7072 15810 198300 1307 4041
2021-09-03 112500 386 18530 582100 37020 125100 5542 32320 2942 12120 9024 1598 261500 7082 15820 198400 1313 4056
2021-09-04 112600 390 18560 582500 37050 125100 5542 32350 2950 12170 9044 1621 262100 7089 15840 198500 1320 4069
2021-09-05 112700 395 18580 582600 37090 125200 5542 32380 2958 12210 9047 1645 262200 7096 15850 198600 1326 4081
2021-09-06 113000 400 18590 582700 37130 125200 5590 32420 2965 12230 9082 1662 262700 7102 15870 198600 1333 4094
2021-09-07 113200 405 18620 583300 37140 125200 5602 32450 2973 12260 9096 1677 263500 7108 15890 198700 1339 4106
2021-09-08 113400 409 18640 584000 37160 125300 5617 32480 2980 12330 9167 1697 264500 7114 15910 198700 1346 4119

Deaths count average forecast Latin America (bold black line in graphs) 2021-09-02 to 2021-09-08

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-09-01 112005 381 18473 581150 36945 125016 5523 32244 2926 12007 8960 1549 260503 7066 15785 198295 1302 4026
2021-09-02 112200 389 18490 581800 36980 125100 5541 32270 2933 12070 9005 1568 261300 7072 15800 198300 1309 4037
2021-09-03 112300 393 18500 582500 37000 125200 5552 32280 2939 12130 9028 1581 262000 7079 15820 198400 1316 4050
2021-09-04 112500 395 18510 583000 37020 125200 5558 32300 2946 12180 9047 1596 262700 7085 15840 198400 1323 4062
2021-09-05 112600 398 18520 583300 37050 125300 5564 32310 2952 12230 9057 1612 262800 7090 15860 198500 1330 4074
2021-09-06 112800 401 18530 583700 37080 125400 5593 32330 2958 12260 9085 1627 263200 7096 15880 198500 1336 4086
2021-09-07 113000 404 18540 584500 37090 125400 5604 32350 2964 12300 9109 1639 264000 7101 15900 198600 1343 4098
2021-09-08 113100 408 18550 585300 37110 125500 5615 32370 2970 12360 9137 1654 264900 7107 15920 198600 1350 4110

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