COVID-19 short-term forecasts Confirmed 2021-08-26 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 Confirmed in Latin America 2021-08-26

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-05-272021-07-262021-02-1812-032021-06-012021-03-242021-06-042021-06-262021-05-172021-01-182021-07-29 -- --2021-06-242021-06-08 --2021-08-232021-08-1805-262021-07-032021-06-022021-04-102021-06-052021-05-242021-04-092021-05-16
Peak daily increment 32516 171 105 1113 2893 74829 7273 29826 2460 1589 3130 193 179 1098 18098 145 1075 2953 8699 261 529 5270 1699
Days since peak 91 31 189 266 86 155 83 61 101 220 28 63 79 3 8 457 54 85 138 82 94 139 102
Last total 5161926 17717 4741 15893 488933 20676561 1635958 4899085 452997 348915 500084 94060 455263 24864 20833 333890 64294 3291761 11167 454878 458207 2143691 28228 43750 384458 329736
Last daily increment 6847 102 21 100 530 31024 785 1935 2706 349 453 0 5113 151 87 2518 463 20633 0 548 91 0 230 212 66 1186
Last week 36963 755 113 478 3749 148462 3517 15153 12350 1545 4969 1374 21924 817 144 7060 3806 94653 495 3585 735 3629 1018 911 555 6979
Previous peak date10-1910-17 -- --2021-01-2208-0406-062021-01-1609-1407-2604-2408-0507-1809-2106-042021-02-032021-03-1810-05 --2021-01-07 --08-0208-1309-19 --09-08
Previous peak daily increment 14376 104 2113 45350 7360 17013 1225 1408 7756 420 2642 66 177 1356 660 23278 3350 8364 89 119 1086
Low between peaks 5479 7 704 19228 1343 3453 262 400 -4346 13 5 42 2152 294 1487 1 4 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-08-27 to 2021-09-02

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-08-26 5161926 17717 15893 488933 20676561 1635958 4899085 452997 348915 500084 94060 455263 24864 333890 64294 3291761 11167 454878 458207 2143691 28228 43750 384458 329736
2021-08-27 5185000 17790 15970 489500 20720000 1637000 4902000 454100 349200 500300 95220 459300 24860 337000 64910 3318000 11170 455900 458400 2147000 28390 44080 384600 330300
2021-08-28 5199000 17790 15970 490300 20753000 1638000 4904000 454100 349400 500600 95550 462700 24910 337000 65640 3337000 11170 457000 458500 2149000 28560 44300 384800 330900
2021-08-29 5207000 17800 15980 490700 20767000 1639000 4905000 454100 349600 501000 95860 466200 24960 337000 66310 3342000 11170 457700 458600 2150000 28710 44410 384800 331600
2021-08-30 5217000 18170 16020 491100 20780000 1640000 4907000 457400 349800 502000 96310 469800 24960 339000 66950 3349000 11170 458100 458700 2150000 28830 44510 384900 332400
2021-08-31 5228000 18280 16070 491700 20812000 1640000 4909000 459200 350000 503000 96820 473500 25030 340700 67580 3366000 11590 458700 458900 2152000 28980 44720 385000 333200
2021-09-01 5239000 18320 16130 492500 20842000 1640000 4910000 461600 350200 503600 98710 477200 25210 341500 68200 3388000 11590 459400 459000 2152000 29160 44910 385100 334100
2021-09-02 5245000 18450 16190 492800 20873000 1641000 4912000 463900 350500 504300 98710 481000 25300 343000 68810 3409000 11700 460000 459100 2153000 29320 45110 385200 334900

Confirmed count average forecast Latin America (bold black line in graphs) 2021-08-27 to 2021-09-02

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-08-26 5161926 17717 15893 488933 20676561 1635958 4899085 452997 348915 500084 94060 455263 24864 333890 64294 3291761 11167 454878 458207 2143691 28228 43750 384458 329736
2021-08-27 5170000 17830 15970 489500 20707000 1637000 4901000 455100 349200 500200 94390 459800 25020 336200 64790 3312000 11150 455400 458300 2144000 28410 43960 384600 330600
2021-08-28 5176000 17870 16020 490000 20737000 1638000 4903000 455900 349400 500300 94560 463700 25110 336900 65460 3330000 11150 456000 458400 2145000 28530 44150 384700 331200
2021-08-29 5181000 17920 16070 490200 20747000 1638000 4905000 456700 349600 500500 94730 467100 25200 337600 65910 3339000 11150 456400 458500 2146000 28630 44280 384700 331800
2021-08-30 5186000 18200 16140 490500 20757000 1639000 4907000 459300 349800 501100 94920 470400 25240 339400 66680 3349000 11150 456700 458500 2147000 28730 44410 384800 332400
2021-08-31 5192000 18300 16210 490800 20788000 1639000 4909000 460900 350000 501500 95120 474200 25320 340800 67290 3366000 11350 457100 458600 2148000 28840 44600 384900 332900
2021-09-01 5199000 18360 16290 491300 20820000 1640000 4912000 462500 350200 501800 96030 478200 25420 342200 67790 3386000 11350 457600 458800 2149000 28970 44780 385000 333500
2021-09-02 5205000 18490 16360 491500 20853000 1641000 4914000 464200 350500 502200 96230 482200 25480 343200 68360 3406000 11450 458100 458900 2150000 29100 44960 385100 334000

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 Confirmed