COVID-19 short-term forecasts Deaths 2021-09-06 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 2021-09-06

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-10 --12-032021-06-122021-04-062021-03-292021-06-252021-05-2409-032021-07-202021-07-31 --2021-05-262021-06-282021-05-12 --2021-06-0105-262021-01-122021-06-082021-04-112021-06-102021-05-282021-04-172021-05-16
Peak daily increment 569 6 82 2996 118 654 31 22 7806 12 7 5 45 3098 7 46 129 813 8 15 64 25
Days since peak 88 277 86 153 161 73 105 368 48 37 103 70 117 97 468 237 90 148 88 101 142 113
Last total 112673 439 364 18512 583810 37108 125331 5642 4012 32353 2969 12315 641 588 9081 1637 263470 200 7086 15889 198523 740 1336 6034 4098
Last daily increment 162 27 1 0 182 18 53 74 0 2 8 50 0 2 58 18 330 0 0 0 35 2 6 0 12
Last week 861 85 2 60 3397 171 386 136 4 109 51 389 19 2 231 119 4144 0 25 122 260 19 45 2 88
Previous peak date10-01 -- --09-0707-2107-172021-01-2209-2804-1209-072021-01-032021-07-01 --07-1107-302021-05-1910-05 --07-23 --07-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 389 18 22 3440 25 60 3 35 9 1724 28 918 9
Low between peaks 108 3 371 37 96 4 4 -17 3 0 5 175 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-09-07 to 2021-09-13

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-09-06 112673 439 18512 583810 37108 125331 5642 32353 2969 12315 9081 1637 263470 7086 15889 198523 1336 4098
2021-09-07 112900 451 18550 585000 37160 125400 5642 32390 2977 12370 9083 1654 264200 7092 15890 198600 1344 4112
2021-09-08 113200 453 18580 586000 37190 125500 5652 32430 2984 12410 9156 1668 265200 7103 15900 198700 1351 4125
2021-09-09 113400 455 18600 586900 37250 125600 5666 32470 2991 12460 9194 1683 266000 7110 15900 198700 1357 4138
2021-09-10 113500 463 18620 587700 37300 125600 5686 32500 2998 12500 9203 1699 266700 7117 15910 198800 1364 4150
2021-09-11 113600 468 18640 588500 37330 125700 5686 32530 3005 12540 9222 1714 267300 7123 15910 198800 1371 4162
2021-09-12 113700 472 18650 588700 37360 125800 5686 32570 3013 12580 9222 1731 267400 7128 15920 198900 1377 4175
2021-09-13 113900 472 18670 588900 37390 125900 5751 32600 3020 12620 9267 1747 267800 7133 15920 198900 1384 4187

Deaths count average forecast Latin America (bold black line in graphs) 2021-09-07 to 2021-09-13

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-09-06 112673 439 18512 583810 37108 125331 5642 32353 2969 12315 9081 1637 263470 7086 15889 198523 1336 4098
2021-09-07 112800 448 18520 584500 37120 125400 5660 32370 2977 12360 9100 1650 264300 7090 15900 198600 1343 4111
2021-09-08 113000 453 18540 585200 37140 125400 5674 32380 2983 12410 9146 1670 265300 7096 15910 198600 1350 4123
2021-09-09 113200 458 18550 585900 37170 125500 5689 32400 2990 12460 9175 1685 266100 7102 15930 198700 1356 4136
2021-09-10 113300 462 18550 586500 37200 125600 5707 32420 2997 12500 9188 1700 266800 7107 15950 198700 1363 4147
2021-09-11 113500 465 18560 587100 37220 125600 5715 32430 3003 12540 9207 1714 267400 7112 15970 198700 1370 4159
2021-09-12 113600 468 18570 587500 37240 125700 5723 32450 3010 12580 9216 1732 267600 7116 15990 198800 1378 4171
2021-09-13 113800 472 18580 587900 37270 125700 5755 32470 3017 12620 9242 1747 267900 7121 16010 198800 1385 4183

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