COVID-19 short-term forecasts Confirmed 2021-01-11 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:
    [2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.

Peak increase in estimated trend of Confirmed in Latin America 2021-01-11

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-03 -- --06-06 --09-142021-01-0409-1612-2407-1809-2112-2706-2809-2212-1105-26 --12-1108-0212-2311-22 --09-08
Peak daily increment 14378 104 1122 7350 1226 1022 1216 249 2590 66 39 795 160 10680 177 875 8380 61 55 1085
Days since peak 84 86 39 219 119 7 117 18 177 112 15 197 111 31 230 31 162 19 50 125
Last total 1730921 8004 11332 175288 8131612 645892 1801903 180061 183282 221506 48905 143243 6588 10386 128701 13637 1541633 6097 281353 116535 1035184 7064 7273 26901 116983
Last daily increment 8704 35 29 1392 25822 3969 15003 2447 1179 436 0 0 14 114 756 89 7594 0 2157 802 9004 56 8 715 373
Last week 68191 76 314 10020 321212 22791 98937 6470 7433 5423 1550 3824 181 259 4879 391 75143 0 21583 5739 15709 478 87 4797 2321
Previous peak date -- -- --07-1708-04 -- -- --07-2604-2408-05 -- --06-06 -- --10-05 -- -- -- --08-1409-19 -- --
Previous peak daily increment 1578 45272 1405 7778 420 179 22834 89 119
Low between peaks 400 -4305 90 6 4599 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-12 to 2021-01-18

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-11 1730921 11332 175288 8131612 645892 1801903 180061 183282 221506 48905 143243 10386 128701 13637 1541633 281353 116535 1035184 7064 26901 116983
2021-01-12 1732000 11400 176000 8139000 649700 1816000 180500 183400 221600 49180 143400 10390 129300 13700 1545000 283400 117300 1042000 7117 27160 117300
2021-01-13 1745000 11460 177400 8198000 653400 1828000 181900 184200 222800 49410 144100 10410 129800 13760 1558000 288100 118000 1047000 7171 27470 117600
2021-01-14 1757000 11520 178900 8273000 657200 1840000 183200 185300 223900 49660 145000 10440 130300 13820 1571000 292100 118700 1052000 7224 27800 117900
2021-01-15 1767000 11580 180300 8310000 661000 1851000 183900 186400 224600 49900 145600 10470 130800 13880 1583000 295400 119400 1057000 7277 28130 118200
2021-01-16 1774000 11640 181300 8352000 664000 1861000 184000 187900 225600 50140 146200 10500 131300 13940 1594000 298200 120100 1061000 7330 28460 118500
2021-01-17 1781000 11700 182000 8375000 667300 1871000 184000 189000 226000 50380 146300 10530 131800 14000 1602000 300700 120800 1066000 7383 28810 118700
2021-01-18 1789000 11760 183300 8397000 670500 1881000 186500 190200 226300 50620 146400 10550 132300 14060 1609000 302800 121500 1070000 7437 29150 119000

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-12 to 2021-01-18

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-11 1730921 11332 175288 8131612 645892 1801903 180061 183282 221506 48905 143243 10386 128701 13637 1541633 281353 116535 1035184 7064 26901 116983
2021-01-12 1742000 11370 176700 8187000 649600 1818000 181000 184300 222000 49130 143700 10420 129500 13700 1553000 284700 117500 1038000 7134 27680 117300
2021-01-13 1753000 11410 178200 8247000 652500 1832000 182000 185200 222600 49370 144300 10450 130000 13760 1564000 288800 118300 1040000 7206 28370 117600
2021-01-14 1764000 11460 179700 8322000 656000 1846000 183000 186200 223100 49650 144900 10480 130500 13810 1575000 292400 119200 1042000 7277 29060 117800
2021-01-15 1774000 11500 181100 8363000 659900 1860000 183800 187300 223600 49900 145400 10500 131000 13860 1587000 295600 120000 1044000 7351 29730 118000
2021-01-16 1782000 11560 182300 8393000 663200 1874000 184200 188500 224100 50130 145800 10520 131500 13900 1597000 298200 120600 1045000 7426 30410 118300
2021-01-17 1789000 11620 183100 8406000 665600 1886000 184600 189400 224400 50400 146000 10550 132000 13970 1605000 300200 121100 1047000 7493 31110 118500
2021-01-18 1797000 11680 184300 8421000 668400 1898000 186100 190500 224600 50680 146300 10570 132400 14040 1612000 302200 121600 1049000 7559 31840 118700

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

[2021-01-07]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