COVID-19 short-term forecasts Deaths 2021-09-17 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 2021-09-17

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-09-0112-032021-06-122021-04-062021-03-292021-06-25 --09-032021-07-202021-07-312021-09-11 --2021-06-282021-05-122021-09-052021-06-0105-262021-01-122021-09-032021-04-112021-06-112021-05-282021-04-172021-05-16
Peak daily increment 569 13 6 82 2996 118 654 22 7806 12 62 5 45 17 3098 7 46 130 813 8 15 64 25
Days since peak 99 16 288 97 164 172 84 379 59 48 6 81 128 12 108 479 248 14 159 98 112 153 124
Last total 114286 504 389 18648 589573 37318 125826 5949 4027 32559 3078 12947 706 596 9452 1772 270538 202 7169 16120 198891 796 1405 6046 4275
Last daily increment 185 35 0 32 327 25 44 30 2 0 12 40 3 0 52 4 190 0 3 0 0 8 8 0 30
Last week 930 51 16 80 3015 117 234 195 13 115 65 282 32 8 133 68 3014 1 38 27 163 35 37 7 83
Previous peak date10-01 -- --09-0707-2107-172021-01-222021-05-2404-1209-072021-01-032021-06-292021-05-2607-1107-302021-05-1610-05 --07-232021-06-0807-18 -- -- --09-20
Previous peak daily increment 2439 1427 1065 785 389 31 22 3440 25 57 6 3 35 8 1724 28 129 918 9
Low between peaks 108 3 371 37 96 4 -17 3 27 0 5 2 175 10 17 95 3

Deaths count forecast Latin America (bold red line in graphs) 2021-09-18 to 2021-09-24

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoVenezuela
2021-09-17 114286 504 18648 589573 37318 125826 5949 32559 3078 12947 706 9452 1772 270538 7169 16120 198891 796 1405 4275
2021-09-18 114400 508 18660 590400 37360 125900 5949 32590 3087 13000 713 9512 1788 272000 7175 16130 199000 803 1412 4282
2021-09-19 114400 508 18680 590800 37410 125900 5949 32610 3092 13070 718 9520 1813 272300 7178 16130 199000 809 1419 4294
2021-09-20 114700 512 18680 591000 37440 126000 5955 32630 3099 13130 721 9570 1832 272500 7183 16130 199100 815 1426 4307
2021-09-21 114900 516 18700 591600 37450 126000 6001 32650 3106 13190 727 9591 1848 273500 7188 16140 199100 821 1432 4320
2021-09-22 115000 521 18710 592300 37470 126100 6040 32680 3114 13240 731 9632 1864 274400 7193 16140 199100 826 1439 4333
2021-09-23 115200 526 18720 592900 37500 126100 6064 32700 3122 13300 734 9641 1878 274900 7198 16140 199200 832 1445 4346
2021-09-24 115400 531 18740 593400 37540 126200 6087 32730 3131 13350 736 9688 1891 275100 7204 16150 199200 838 1451 4359

Deaths count average forecast Latin America (bold black line in graphs) 2021-09-18 to 2021-09-24

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoVenezuela
2021-09-17 114286 504 18648 589573 37318 125826 5949 32559 3078 12947 706 9452 1772 270538 7169 16120 198891 796 1405 4275
2021-09-18 114400 517 18660 590100 37340 125900 5970 32580 3087 12990 711 9469 1784 271500 7175 16130 198900 802 1412 4291
2021-09-19 114500 521 18670 590400 37360 125900 5985 32590 3094 13050 717 9484 1800 271800 7180 16140 199000 806 1418 4303
2021-09-20 114700 526 18670 590600 37380 125900 6004 32600 3101 13100 721 9519 1814 272000 7186 16150 199000 811 1424 4316
2021-09-21 114800 531 18680 591200 37390 126000 6035 32610 3109 13160 726 9539 1828 272900 7191 16170 199000 815 1430 4328
2021-09-22 115000 538 18690 591700 37400 126000 6069 32630 3116 13220 731 9572 1841 273800 7197 16180 199100 819 1436 4340
2021-09-23 115100 543 18690 592200 37420 126000 6092 32640 3124 13280 735 9592 1854 274400 7203 16190 199100 824 1442 4352
2021-09-24 115200 547 18700 592700 37440 126100 6116 32650 3131 13340 739 9633 1867 274700 7209 16200 199100 828 1449 4364

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