COVID-19 short-term forecasts Deaths 2021-10-04 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 2021-10-04

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-09-0112-032021-06-122021-04-062021-03-292021-06-25 --09-032021-07-202021-07-312021-09-042021-09-182021-06-282021-05-122021-09-032021-06-0105-262021-01-122021-09-032021-04-14 --2021-05-282021-04-172021-05-16
Peak daily increment 568 13 6 82 2995 116 654 22 7798 12 62 6 5 45 18 2955 8 46 129 814 15 64 25
Days since peak 116 33 305 114 181 189 101 396 76 65 30 16 98 145 31 125 496 265 31 173 129 170 141
Last total 115283 557 422 18766 598152 37500 126425 6494 4055 32805 3293 13750 799 615 9878 1922 279106 204 7244 16203 199485 919 1508 6058 4510
Last daily increment 38 24 4 4 204 6 24 81 0 14 16 20 0 4 24 13 514 0 6 3 0 11 3 1 0
Last week 245 34 14 50 2706 51 206 178 14 58 82 297 21 5 123 63 2730 0 25 10 156 49 42 5 67
Previous peak date10-01 -- --09-0707-2107-172021-01-222021-05-2404-1209-072021-01-032021-06-292021-05-2607-1207-302021-05-1810-05 --07-232021-06-0807-182021-06-09 -- --09-20
Previous peak daily increment 2487 1439 1066 787 389 31 23 3440 25 57 7 3 35 8 1833 28 129 917 8 9
Low between peaks 108 3 371 37 96 4 -17 3 27 2 0 5 2 171 10 18 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-10-05 to 2021-10-11

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoVenezuela
2021-10-04 115283 557 18766 598152 37500 126425 6494 32805 3293 13750 799 9878 1922 279106 7244 199485 919 1508 4510
2021-10-05 115400 557 18780 599000 37530 126500 6538 32820 3305 13800 805 9920 1929 279900 7248 199500 925 1514 4529
2021-10-06 115500 563 18790 599700 37550 126500 6567 32840 3311 13860 814 9950 1932 280600 7255 199600 931 1519 4542
2021-10-07 115600 568 18810 600300 37580 126500 6600 32860 3319 13900 822 9950 1938 281200 7260 199600 937 1525 4556
2021-10-08 115600 572 18820 600800 37600 126600 6627 32870 3329 13950 828 10010 1944 281500 7265 199700 944 1530 4569
2021-10-09 115700 577 18830 601300 37620 126600 6627 32890 3339 14010 834 10020 1951 282000 7270 199700 951 1536 4582
2021-10-10 115700 581 18840 601500 37640 126600 6627 32900 3350 14040 839 10020 1959 282200 7274 199700 959 1541 4595
2021-10-11 115800 585 18850 601600 37650 126700 6702 32910 3361 14050 844 10030 1967 282600 7278 199700 966 1547 4607

Deaths count average forecast Latin America (bold black line in graphs) 2021-10-05 to 2021-10-11

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoVenezuela
2021-10-04 115283 557 18766 598152 37500 126425 6494 32805 3293 13750 799 9878 1922 279106 7244 199485 919 1508 4510
2021-10-05 115300 565 18770 598700 37510 126500 6533 32810 3306 13790 803 9910 1932 279800 7249 199500 927 1513 4523
2021-10-06 115400 570 18780 599400 37520 126500 6563 32830 3315 13850 810 9940 1938 280500 7253 199500 934 1519 4537
2021-10-07 115500 574 18780 600000 37530 126500 6592 32840 3325 13890 816 9950 1945 281000 7258 199600 940 1525 4550
2021-10-08 115500 577 18790 600500 37540 126500 6618 32850 3334 13930 821 9990 1951 281300 7262 199600 946 1531 4563
2021-10-09 115600 580 18790 601000 37550 126600 6631 32850 3344 13980 827 9990 1958 281700 7266 199600 955 1537 4575
2021-10-10 115600 582 18800 601300 37570 126600 6644 32870 3354 14020 832 10000 1965 282100 7270 199700 961 1543 4588
2021-10-11 115700 587 18810 601600 37580 126600 6692 32880 3364 14050 837 10010 1972 282400 7274 199700 968 1549 4601

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