COVID-19 short-term forecasts Deaths 2021-08-04 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 2021-08-04

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-07-1612-032021-06-122021-04-062021-07-042021-06-252021-05-2409-032021-07-202021-01-032021-06-292021-05-262021-06-282021-05-102021-05-162021-06-012021-01-122021-06-082021-04-112021-06-102021-05-282021-04-172021-05-16
Peak daily increment 569 10 6 82 2996 119 654 31 22 8103 25 57 7 5 44 8 3098 46 129 813 8 15 64 25
Days since peak 55 19 244 53 120 31 40 72 335 15 213 36 70 37 86 80 64 204 57 115 55 68 109 80
Last total 106747 292 338 17910 559607 35671 121695 5087 3971 31694 2672 10524 549 560 8014 1207 242547 6860 15172 196673 657 1110 5979 3649
Last daily increment 300 1 0 28 1175 31 479 17 3 50 9 41 0 0 112 4 611 9 37 75 3 9 3 12
Last week 1634 6 1 126 5110 376 1569 74 15 145 73 276 14 15 221 25 2550 62 296 459 14 53 26 91
Previous peak date10-01 -- --09-0707-2107-172021-01-2209-2804-1209-0708-0706-05 --07-1107-29 --10-0507-23 --07-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 389 18 22 3440 11 43 3 35 1724 28 918 9
Low between peaks 108 3 371 37 96 4 4 -90 2 11 0 5 175 10 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-08-05 to 2021-08-11

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-08-04 106747 17910 559607 35671 121695 5087 31694 2672 10524 8014 1207 242547 6860 15172 196673 1110 5979 3649
2021-08-05 107200 17970 561300 35820 122000 5096 31780 2681 10560 8029 1210 242500 6869 15220 196800 1116 5985 3664
2021-08-06 107700 17990 562600 35900 122400 5111 31860 2688 10560 8029 1213 242600 6876 15280 196900 1120 5995 3677
2021-08-07 107900 18030 563600 35980 122700 5111 31940 2695 10580 8044 1216 243000 6884 15330 197000 1126 6002 3691
2021-08-08 108000 18050 564100 36060 123000 5111 32000 2704 10600 8067 1219 243100 6893 15380 197100 1132 6008 3705
2021-08-09 108400 18060 564600 36140 123300 5113 32070 2712 10630 8091 1221 243200 6901 15430 197200 1138 6014 3719
2021-08-10 108700 18090 565700 36150 123600 5152 32130 2720 10660 8118 1224 243800 6909 15480 197300 1144 6020 3733
2021-08-11 109100 18120 566800 36180 123900 5166 32190 2729 10690 8146 1227 244300 6918 15530 197300 1151 6025 3747

Deaths count average forecast Latin America (bold black line in graphs) 2021-08-05 to 2021-08-11

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-08-04 106747 17910 559607 35671 121695 5087 31694 2672 10524 8014 1207 242547 6860 15172 196673 1110 5979 3649
2021-08-05 107000 17940 560700 35770 122000 5102 31750 2682 10560 8055 1210 243000 6869 15220 196800 1117 5984 3663
2021-08-06 107300 17950 561700 35830 122300 5114 31810 2693 10590 8075 1213 243300 6877 15270 196800 1123 5991 3676
2021-08-07 107500 17970 562600 35900 122500 5119 31870 2703 10620 8097 1216 243700 6885 15320 196900 1129 5999 3690
2021-08-08 107700 17980 563200 35950 122800 5124 31920 2713 10660 8119 1218 243800 6894 15370 197000 1135 6006 3704
2021-08-09 108000 17990 563900 36010 123000 5130 31960 2723 10680 8141 1221 244000 6903 15420 197100 1142 6014 3718
2021-08-10 108300 18010 565000 36040 123200 5152 32000 2733 10710 8163 1224 244400 6911 15480 197200 1148 6022 3733
2021-08-11 108600 18030 566100 36060 123400 5162 32050 2742 10750 8185 1226 244900 6920 15530 197300 1155 6031 3747

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