COVID-19 short-term forecasts Deaths 2021-10-17 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 2021-10-17

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-06-102021-09-012021-10-132021-06-122021-04-062021-10-072021-06-252021-09-2009-032021-07-202021-10-052021-09-042021-09-152021-10-062021-05-122021-08-312021-06-0105-262021-01-122021-09-032021-04-112021-10-132021-05-282021-04-172021-05-16
Peak daily increment 569 12 11 82 2996 144 654 34 22 7806 15 61 6 4 45 17 3098 7 46 130 813 9 15 64 25
Days since peak 129 46 4 127 194 10 114 27 409 89 12 43 32 11 158 47 138 509 278 44 189 4 142 183 154
Last total 115666 590 461 18834 603282 37609 126865 6797 4087 32899 3477 14330 870 657 10096 2110 284381 206 7291 16209 199816 1022 1600 6069 4719
Last daily increment 3 0 0 10 130 12 35 0 3 0 17 64 4 0 0 7 60 0 3 0 41 8 7 2 26
Last week 175 0 34 31 2069 38 210 99 20 51 85 264 28 14 87 85 2154 1 20 2 113 54 45 5 85
Previous peak date10-01 --12-0309-0707-2107-172021-01-222021-05-2404-1209-072021-01-032021-06-292021-05-262021-06-2807-302021-05-1610-05 --07-232021-06-0807-182021-06-10 -- --09-20
Previous peak daily increment 2439 6 1427 1065 785 389 31 22 3440 25 57 7 5 35 8 1724 28 129 918 8 9
Low between peaks 108 0 3 371 7 96 11 4 -17 3 27 2 0 5 2 175 10 18 95 2 3

Deaths count forecast Latin America (bold red line in graphs) 2021-10-18 to 2021-10-24

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoVenezuela
2021-10-17 115666 461 18834 603282 37609 126865 6797 4087 32899 3477 14330 870 10096 2110 284381 7291 199816 1022 1600 4719
2021-10-18 115700 462 18840 603800 37620 126900 6881 4089 32920 3491 14370 876 10140 2124 285000 7295 199800 1029 1606 4732
2021-10-19 115800 465 18850 604400 37630 126900 6901 4090 32940 3502 14430 881 10170 2127 285600 7301 199900 1036 1611 4748
2021-10-20 115900 467 18860 604800 37640 127000 6924 4092 32950 3514 14490 887 10200 2135 286200 7306 199900 1043 1617 4763
2021-10-21 116000 469 18870 605300 37670 127000 6950 4094 32970 3527 14520 891 10210 2144 286800 7310 200000 1050 1623 4777
2021-10-22 116100 471 18880 605900 37670 127000 6975 4096 32980 3539 14550 896 10220 2155 287300 7314 200000 1058 1629 4791
2021-10-23 116100 472 18880 606300 37690 127100 6975 4098 32990 3552 14590 901 10220 2166 287500 7318 200000 1065 1636 4805
2021-10-24 116100 474 18890 606400 37700 127100 6975 4100 33010 3565 14640 905 10220 2179 287600 7321 200000 1073 1642 4818

Deaths count average forecast Latin America (bold black line in graphs) 2021-10-18 to 2021-10-24

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaPeruSurinameTrinidad and TobagoVenezuela
2021-10-17 115666 461 18834 603282 37609 126865 6797 4087 32899 3477 14330 870 10096 2110 284381 7291 199816 1022 1600 4719
2021-10-18 115700 463 18840 603500 37620 126900 6848 4089 32910 3491 14370 875 10120 2124 284500 7295 199800 1030 1607 4733
2021-10-19 115700 465 18840 603900 37620 126900 6875 4091 32910 3503 14420 880 10150 2135 285000 7299 199900 1037 1613 4746
2021-10-20 115800 467 18850 604200 37630 127000 6901 4093 32920 3515 14470 884 10170 2147 285500 7303 199900 1045 1620 4759
2021-10-21 115800 470 18850 604600 37640 127000 6927 4095 32930 3527 14500 889 10180 2159 286000 7307 199900 1053 1626 4772
2021-10-22 115900 473 18860 605200 37640 127000 6953 4097 32930 3539 14540 893 10190 2172 286500 7311 199900 1061 1633 4785
2021-10-23 115900 474 18860 605600 37650 127000 6966 4100 32940 3550 14580 898 10190 2186 286700 7315 200000 1068 1639 4798
2021-10-24 116000 477 18860 605800 37660 127100 6978 4102 32950 3562 14630 902 10190 2199 287100 7318 200000 1076 1646 4811

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