COVID-19 short-term forecasts Confirmed 2022-01-01 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 2022-01-01

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --2021-12-292021-10-262021-10-14 --2021-09-18 --2021-06-262021-09-062021-11-132021-12-202021-11-062021-08-242021-09-182021-06-082021-12-032021-08-232021-08-112021-10-19 --2021-11-15 --2021-09-152021-12-042021-10-282021-10-05
Peak daily increment 554 347 370 92852 29570 2470 1083 655 1526 3774 232 179 100 759 18310 141 163 485 746 232 1476
Days since peak 3 67 79 105 189 117 49 12 56 130 105 207 29 131 143 74 47 108 28 65 88
Last total 5674428 24476 28810 32840 599753 22295621 1806494 5169855 570556 419927 549418 121945 628589 39779 25985 379402 94649 3979723 17487 497808 466101 2296831 52612 92201 414294 444828
Last daily increment 20020 0 245 0 0 3782 0 12415 0 1149 0 0 230 206 0 0 729 0 0 1888 0 4577 166 302 911 193
Last week 214386 937 1137 1337 23151 52355 6214 45165 1696 5701 9296 204 3423 672 35 329 1944 29523 45 10041 1073 18024 1034 2372 6913 1496
Previous peak date2021-06-252021-07-262021-07-11 --2021-06-122021-06-16 -- -- --2021-06-052021-07-292021-09-09 --2021-06-2406-042021-08-13 -- --05-262021-07-062021-06-082021-07-042021-06-132021-06-052021-06-06 --
Previous peak daily increment 20799 172 36 3394 72652 1203 3070 454 196 177 1515 145 1068 2669 2786 257 365 3221
Low between peaks 7 7 17910 245 175 124 58 5 44 4 22 77 170 95

Confirmed count forecast Latin America (bold red line in graphs) 2022-01-02 to 2022-01-08

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-01 5674428 24476 28810 32840 599753 22295621 1806494 5169855 570556 419927 549418 121945 628589 39779 379402 94649 3979723 497808 466101 2296831 52612 92201 414294 444828
2022-01-02 5674000 24660 28990 32840 603100 22296000 1808000 5180000 570600 420900 549400 122000 628800 39900 379500 95180 3980000 499500 466200 2302000 52760 92730 415400 445100
2022-01-03 5690000 24740 29200 32860 605100 22297000 1809000 5189000 570600 421700 550200 122000 629100 39990 379700 95560 3980000 500600 466200 2304000 52900 93790 416200 445800
2022-01-04 5716000 24790 29450 32890 609200 22302000 1811000 5198000 571000 422500 550500 122100 629700 40090 379800 96010 3980000 502400 466300 2306000 53030 94580 417200 446300
2022-01-05 5734000 24790 29730 32950 611600 22307000 1812000 5207000 571000 423300 550500 122100 630300 40200 379800 96380 3980000 504400 466400 2307000 53160 95230 418100 446600
2022-01-06 5768000 24790 30010 33040 616000 22314000 1813000 5215000 571000 424100 552600 122300 630800 40300 379900 96730 3982000 505900 466500 2310000 53290 95860 419000 447000
2022-01-07 5802000 24910 30290 33110 622100 22322000 1814000 5224000 571000 424900 554400 122300 631300 40410 379900 97130 3989000 507600 466600 2311000 53410 96410 419900 447300
2022-01-08 5818000 24930 30440 33140 622200 22325000 1815000 5232000 571000 425700 554400 122300 631400 40540 379900 97520 3990000 508400 466700 2313000 53540 96940 420900 447600

Confirmed count average forecast Latin America (bold black line in graphs) 2022-01-02 to 2022-01-08

DateArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2022-01-01 5674428 24476 28810 32840 599753 22295621 1806494 5169855 570556 419927 549418 121945 628589 39779 379402 94649 3979723 497808 466101 2296831 52612 92201 414294 444828
2022-01-02 5701000 24550 28960 32950 603000 22300000 1807000 5179000 570600 420800 550100 122100 628900 39900 379400 95080 3983000 499400 466100 2299000 52740 92480 415200 445100
2022-01-03 5724000 24660 29010 33020 604900 22304000 1808000 5186000 570600 421400 551400 122100 629100 39930 379500 95230 3984000 500000 466300 2300000 52830 92760 415800 445400
2022-01-04 5751000 24750 29110 33150 608500 22310000 1809000 5192000 571300 422000 551900 122100 629500 39990 379600 95450 3985000 501100 466300 2301000 52930 93290 416600 445700
2022-01-05 5779000 25040 29280 33330 611800 22316000 1810000 5199000 571400 422700 552000 122200 630100 40070 379600 95600 3989000 502500 466400 2302000 53070 93740 417400 446000
2022-01-06 5809000 25110 29440 33520 615200 22322000 1812000 5205000 571400 423300 553800 122300 630500 40160 379600 95830 3994000 503200 466600 2306000 53180 94390 418200 446300
2022-01-07 5834000 25230 29550 33650 619000 22326000 1813000 5211000 571500 424000 555000 122400 630900 40240 379600 96020 4000000 504900 466600 2307000 53270 95160 419000 446500
2022-01-08 5855000 25290 29610 33710 620900 22327000 1814000 5217000 571500 424600 555000 122400 631100 40300 379600 96250 4002000 505800 466700 2310000 53340 95730 419600 446800

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