COVID-19 short-term forecasts Deaths 2021-07-25 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 2021-07-25

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-07-1612-032021-06-122021-04-062021-03-292021-06-242021-05-2409-032021-07-202021-01-032021-06-262021-05-262021-06-272021-07-202021-05-162021-06-012021-01-122021-06-082021-04-112021-06-102021-05-282021-04-172021-05-16
Peak daily increment 569 12 6 82 2996 118 662 31 22 8787 25 57 7 5 66 8 3098 46 129 813 8 15 64 25
Days since peak 45 9 234 43 110 118 31 62 325 5 203 29 60 28 5 70 54 194 47 105 45 58 99 70
Last total 103721 274 332 17651 549924 35026 118868 4941 3937 30797 2558 10100 527 523 7594 1176 238424 6759 14653 195243 631 1039 5931 3492
Last daily increment 137 0 0 14 476 68 330 0 0 0 11 7 4 0 0 8 108 9 60 0 3 7 4 17
Last week 1766 1 0 176 7168 487 2115 35 21 8839 59 173 15 15 203 19 1955 49 313 0 17 41 42 100
Previous peak date10-01 -- --09-0707-2107-172021-01-2209-2804-1209-0708-0707-14 --07-122021-05-12 --10-0507-23 --07-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 389 18 22 3440 11 39 3 45 1724 28 918 9
Low between peaks 108 3 371 37 96 4 4 5 2 11 0 20 175 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-07-26 to 2021-08-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayTrinidad and TobagoUruguayVenezuela
2021-07-25 103721 17651 549924 35026 118868 4941 3937 30797 2558 10100 7594 238424 6759 14653 1039 5931 3492
2021-07-26 104400 17690 551100 35180 119200 4970 3940 31120 2568 10130 7609 238400 6765 14700 1046 5939 3505
2021-07-27 104900 17740 552800 35240 119500 4990 3949 31350 2577 10180 7633 238500 6769 14730 1054 5954 3515
2021-07-28 105400 17780 554300 35300 119900 4999 3955 31590 2586 10220 7653 238600 6774 14770 1062 5965 3527
2021-07-29 105700 17820 555800 35470 120200 5006 3960 31800 2595 10250 7668 238900 6779 14810 1068 5974 3540
2021-07-30 106000 17850 557000 35560 120500 5011 3964 32000 2603 10280 7684 238900 6784 14850 1075 5982 3552
2021-07-31 106200 17890 557900 35640 120800 5014 3968 32210 2612 10310 7698 239100 6789 14890 1081 5990 3565
2021-08-01 106400 17920 558500 35720 121100 5014 3971 32410 2622 10340 7712 239300 6794 14930 1088 5997 3579

Deaths count average forecast Latin America (bold black line in graphs) 2021-07-26 to 2021-08-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayTrinidad and TobagoUruguayVenezuela
2021-07-25 103721 17651 549924 35026 118868 4941 3937 30797 2558 10100 7594 238424 6759 14653 1039 5931 3492
2021-07-26 104100 17680 550600 35080 119200 4957 3941 30930 2567 10130 7611 238600 6769 14700 1046 5939 3507
2021-07-27 104500 17700 552000 35120 119500 4973 3945 31130 2574 10170 7635 238800 6778 14740 1052 5947 3518
2021-07-28 104900 17730 553200 35160 119800 4983 3949 31330 2581 10200 7655 239200 6786 14770 1058 5954 3530
2021-07-29 105200 17750 554500 35260 120200 4993 3953 31520 2588 10240 7675 239500 6795 14810 1064 5963 3541
2021-07-30 105500 17770 555800 35330 120500 5002 3956 31710 2595 10280 7693 239600 6803 14860 1070 5972 3553
2021-07-31 105900 17800 556800 35390 120800 5010 3959 31950 2602 10310 7711 239900 6811 14900 1076 5981 3566
2021-08-01 106100 17810 557700 35460 121200 5016 3962 32130 2609 10340 7728 240100 6819 14940 1082 5990 3578

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