COVID-19 short-term forecasts Deaths 2021-08-12 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 2021-08-12

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-07-1612-032021-06-122021-04-062021-03-292021-06-252021-05-2409-032021-07-202021-07-31 --2021-05-262021-06-282021-05-10 --2021-06-012021-01-122021-06-082021-04-112021-06-102021-05-282021-04-172021-05-16
Peak daily increment 569 10 6 82 2996 118 654 31 22 7907 12 7 5 44 3098 46 129 813 8 15 64 25
Days since peak 63 27 252 61 128 136 48 80 343 23 12 78 45 94 72 212 65 123 63 76 117 88
Last total 108569 311 344 18109 566896 36243 123097 5199 3975 31870 2759 10960 569 576 8310 1268 246811 6932 15383 197209 672 1159 5996 3764
Last daily increment 181 3 0 0 1148 86 144 14 0 82 10 62 2 7 0 14 608 8 20 63 1 8 5 31
Last week 1356 16 5 174 5134 363 1010 75 2 96 68 316 15 13 190 54 3078 57 145 391 13 42 10 82
Previous peak date10-01 -- --09-0707-2107-172021-01-2209-2804-1209-072021-01-0307-15 --07-1107-292021-05-1810-0507-23 --07-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 389 18 22 3440 25 41 3 35 8 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) 2021-08-13 to 2021-08-19

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-08-12 108569 18109 566896 36243 123097 5199 31870 2759 10960 8310 1268 246811 6932 15383 197209 1159 3764
2021-08-13 109000 18120 568200 36340 123300 5214 31910 2770 11060 8354 1273 246900 6941 15410 197300 1165 3778
2021-08-14 109200 18150 569300 36430 123800 5214 31910 2779 11130 8354 1273 247200 6952 15440 197400 1172 3794
2021-08-15 109400 18170 569700 36500 124200 5214 31910 2789 11190 8354 1273 247200 6962 15470 197500 1178 3810
2021-08-16 109800 18190 570200 36590 124500 5233 31910 2799 11220 8363 1273 247400 6971 15500 197600 1184 3824
2021-08-17 110100 18210 571400 36630 124800 5246 31910 2810 11290 8415 1273 248000 6980 15520 197700 1190 3839
2021-08-18 110300 18280 572300 36640 125000 5260 31910 2820 11340 8515 1273 248700 6988 15550 197700 1196 3853
2021-08-19 110600 18290 573400 36750 125200 5275 31910 2830 11360 8528 1273 249300 6997 15580 197800 1202 3867

Deaths count average forecast Latin America (bold black line in graphs) 2021-08-13 to 2021-08-19

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2021-08-12 108569 18109 566896 36243 123097 5199 31870 2759 10960 8310 1268 246811 6932 15383 197209 1159 3764
2021-08-13 108800 18130 567800 36300 123300 5213 32000 2769 11010 8355 1276 247300 6940 15410 197300 1166 3781
2021-08-14 109000 18140 568700 36360 123600 5219 32160 2779 11050 8375 1278 247800 6949 15440 197400 1173 3795
2021-08-15 109100 18150 569200 36410 123800 5226 32250 2789 11090 8394 1280 248000 6957 15470 197400 1180 3808
2021-08-16 109500 18170 569700 36460 124000 5243 32350 2799 11130 8417 1283 248200 6966 15510 197500 1187 3821
2021-08-17 109700 18180 570700 36490 124100 5255 32420 2809 11170 8452 1287 248800 6974 15530 197600 1193 3834
2021-08-18 110000 18200 571600 36510 124300 5268 32620 2818 11210 8504 1290 249400 6982 15560 197600 1200 3846
2021-08-19 110200 18210 572500 36590 124500 5282 32710 2828 11240 8527 1294 249900 6991 15590 197700 1207 3858

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