COVID-19 short-term forecasts Deaths 2021-04-15 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 2021-04-15

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 60 115 389 18 22 3440 25 34 2 3 21 1259 46 204
Days since peak 87 133 71 17 83 199 224 220 102 64 11 277 65 79 93 51
Last total 58925 318 12603 365444 24766 67199 3065 3405 17489 2064 7120 263 252 4929 697 211213 6183 5110 55812 1726 1853
Last daily increment 383 0 84 3560 218 380 21 3 89 4 31 3 1 24 5 401 6 70 0 79 19
Last week 1575 0 175 16726 658 1916 47 23 331 20 143 11 0 163 36 4193 27 412 1527 363 114
Previous peak date10-01 --09-0707-2307-1708-23 --04-1205-1008-0706-05 -- --07-30 --10-0507-23 --07-232021-01-2509-18
Previous peak daily increment 2487 1439 1103 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) 2021-04-16 to 2021-04-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-15 58925 12603 365444 24766 67199 3065 3405 17489 2064 7120 4929 697 211213 6183 5110 55812 1726 1853
2021-04-16 59110 12640 370400 24830 67520 3069 3410 17530 2067 7151 4949 704 211400 6187 5178 56050 1796 1871
2021-04-17 59360 12670 373000 24910 67830 3069 3417 17530 2070 7177 4949 709 212100 6195 5232 56180 1867 1888
2021-04-18 59520 12700 374600 25020 68130 3069 3423 17550 2073 7205 4949 715 212100 6201 5292 56340 1936 1905
2021-04-19 59710 12730 375500 25140 68430 3069 3428 17580 2076 7232 4958 721 212200 6206 5354 56530 2005 1921
2021-04-20 59890 12750 379900 25170 68730 3087 3433 17610 2079 7259 4970 727 212900 6211 5416 56720 2077 1938
2021-04-21 60190 12780 383300 25200 69030 3087 3438 17650 2082 7287 4987 733 213400 6215 5481 56930 2149 1954
2021-04-22 60480 12810 386800 25390 69330 3098 3443 17680 2085 7314 5005 740 213900 6220 5548 57140 2225 1971

Deaths count average forecast Latin America (bold black line in graphs) 2021-04-16 to 2021-04-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-15 58925 12603 365444 24766 67199 3065 3405 17489 2064 7120 4929 697 211213 6183 5110 55812 1726 1853
2021-04-16 59190 12640 369100 24880 67510 3077 3410 17540 2067 7143 4945 704 211700 6188 5173 55950 1802 1871
2021-04-17 59400 12650 371500 24960 67770 3081 3416 17570 2071 7155 4952 710 212700 6193 5231 56100 1865 1888
2021-04-18 59570 12660 373000 25050 68020 3085 3421 17590 2074 7169 4963 717 212700 6198 5294 56260 1928 1904
2021-04-19 59770 12680 374300 25150 68270 3088 3427 17610 2077 7185 4977 724 212900 6202 5356 56430 1997 1920
2021-04-20 59960 12700 378100 25200 68530 3104 3432 17620 2080 7201 4993 731 213400 6206 5421 56580 2064 1937
2021-04-21 60190 12710 381600 25250 68780 3108 3437 17640 2083 7218 5009 738 213900 6211 5485 56750 2134 1954
2021-04-22 60420 12730 385500 25360 69030 3113 3442 17660 2086 7235 5026 745 214300 6215 5550 56950 2208 1970

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