COVID-19 short-term forecasts Deaths 2021-04-09 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:
    [2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.

Peak increase in estimated trend of Deaths in Latin America 2021-04-09

ArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
Peak date (mm-dd)2021-01-1812-032021-02-03 --2021-03-292021-01-2209-2809-0309-072021-01-032021-02-102021-04-0407-122021-02-09 --2021-01-262021-01-12 --2021-02-23 -- --
Peak daily increment 249 6 60 119 389 18 22 3440 25 34 3 3 21 1259 46 204
Days since peak 81 127 65 11 77 193 218 214 96 58 5 271 59 73 87 45
Last total 57350 318 12428 348718 24108 65283 3018 3382 17158 2044 6977 252 252 4766 661 207020 6156 4698 54285 1363 1739
Last daily increment 228 0 16 3693 129 269 18 4 43 4 22 0 0 51 15 874 4 54 307 88 19
Last week 1244 1 112 18525 584 1351 61 41 229 23 93 15 0 133 48 3009 25 354 1660 292 92
Previous peak date10-01 --09-0707-2207-1708-23 --04-1205-1008-0706-05 -- --07-30 --10-0507-23 --07-232021-01-2509-18
Previous peak daily increment 2487 1439 1063 787 318 23 166 11 43 35 1833 28 2944 11 9
Low between peaks 120 3 37 125 4 14 2 11 5 343 10 48

Deaths count forecast Latin America (bold red line in graphs) 2021-04-10 to 2021-04-16

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-09 57350 12428 348718 24108 65283 3018 3382 17158 2044 6977 4766 661 207020 6156 4698 54285 1363 1739
2021-04-10 57560 12440 350300 24180 65540 3018 3388 17190 2047 6992 4778 662 207400 6161 4742 54300 1420 1761
2021-04-11 57740 12450 351000 24260 65780 3018 3391 17220 2049 7003 4781 664 207600 6172 4761 54360 1467 1780
2021-04-12 58050 12470 352000 24330 66020 3018 3396 17250 2052 7015 4792 668 207800 6180 4791 54490 1519 1798
2021-04-13 58250 12490 355700 24380 66260 3046 3400 17280 2055 7028 4804 671 208400 6186 4826 54640 1572 1815
2021-04-14 58450 12510 358800 24420 66490 3046 3405 17310 2058 7040 4817 679 209000 6192 4863 54810 1628 1833
2021-04-15 58720 12540 362800 24580 66720 3046 3411 17350 2061 7054 4831 688 209500 6198 4903 54990 1686 1850
2021-04-16 58840 12550 365900 24680 66950 3058 3416 17380 2064 7067 4846 702 210200 6203 4945 55170 1746 1867

Deaths count average forecast Latin America (bold black line in graphs) 2021-04-10 to 2021-04-16

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-09 57350 12428 348718 24108 65283 3018 3382 17158 2044 6977 4766 661 207020 6156 4698 54285 1363 1739
2021-04-10 57530 12440 351500 24210 65490 3023 3387 17190 2047 6994 4788 670 207400 6161 4751 54520 1415 1754
2021-04-11 57630 12450 353100 24300 65660 3026 3391 17210 2050 7004 4798 674 207600 6167 4798 54670 1453 1766
2021-04-12 57810 12470 354800 24380 65840 3029 3396 17220 2053 7015 4809 680 207800 6173 4847 54810 1493 1779
2021-04-13 57960 12480 358400 24440 66020 3047 3400 17240 2056 7026 4821 684 208300 6178 4897 54920 1536 1792
2021-04-14 58100 12490 361700 24490 66200 3051 3405 17260 2058 7038 4832 692 208900 6183 4948 55060 1579 1805
2021-04-15 58250 12510 365200 24600 66370 3055 3410 17280 2061 7049 4844 699 209500 6188 5000 55230 1624 1818
2021-04-16 58360 12520 368400 24670 66550 3060 3415 17300 2064 7061 4855 708 209900 6193 5053 55380 1673 1831

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

[2021-01-07]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