COVID-19 short-term forecasts Confirmed 2021-01-17 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:
    [2021-01-07]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 2021-01-17

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1610-1712-03 -- --06-06 --09-14 --09-1612-2007-1809-2112-14 --09-22 --05-262021-01-07 --08-2012-2211-222021-01-1409-08
Peak daily increment 14331 104 1122 7349 1226 1216 246 2590 66 36 160 177 3348 8111 64 55 1169 1085
Days since peak 93 92 45 225 125 123 28 183 118 34 117 236 10 150 26 56 3 131
Last total 1799243 8032 11580 187183 8488099 669832 1908413 184187 193118 231482 50784 148888 6908 10781 134111 14161 1641428 6152 298019 122160 1060567 7527 7393 32378 119803
Last daily increment 7264 0 51 1503 33040 4339 17379 0 1779 674 0 290 57 100 604 65 11170 0 1750 512 4544 58 23 709 497
Last week 68322 28 248 11895 356487 23940 106510 4126 9836 9976 1879 5645 320 395 5410 524 99795 55 16666 5625 25383 463 120 5477 2820
Previous peak date -- -- --07-1707-29 -- -- --07-2504-2408-05 -- --06-0606-28 --10-05 -- --06-27 --08-1409-19 -- --
Previous peak daily increment 1578 48670 1479 7778 420 179 795 21301 155 89 119
Low between peaks -4305 90 6 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-18 to 2021-01-24

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-17 1799243 11580 187183 8488099 669832 1908413 184187 193118 231482 50784 148888 6908 10781 134111 14161 1641428 298019 122160 1060567 7527 32378 119803
2021-01-18 1804000 11620 188500 8494000 674400 1922000 186600 194700 231500 51100 148900 6940 10820 134800 14230 1652000 301800 123800 1066000 7600 32750 120200
2021-01-19 1818000 11660 190100 8549000 677800 1937000 187600 196100 232400 51360 150400 6968 10860 135500 14300 1665000 305400 125100 1070000 7673 33160 120500
2021-01-20 1831000 11700 192100 8608000 681400 1952000 188600 197500 233900 51630 151300 6997 10900 136200 14370 1678000 309100 126200 1075000 7743 33580 120900
2021-01-21 1842000 11750 194000 8671000 685400 1968000 189600 198700 235100 51890 152200 7024 10930 136900 14440 1689000 312200 127400 1079000 7813 34000 121200
2021-01-22 1852000 11790 196100 8724000 689700 1986000 190400 199800 235500 52150 152700 7051 10970 137500 14500 1701000 314700 128900 1083000 7884 34430 121500
2021-01-23 1861000 11830 197900 8773000 694100 2003000 190400 200900 237100 52420 153600 7078 11010 138200 14570 1711000 317200 129800 1088000 7955 34860 121900
2021-01-24 1867000 11870 199300 8791000 697700 2019000 190400 201900 238100 52680 153800 7105 11050 138800 14640 1715000 318900 130100 1092000 8026 35300 122200

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-18 to 2021-01-24

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-17 1799243 11580 187183 8488099 669832 1908413 184187 193118 231482 50784 148888 6908 10781 134111 14161 1641428 298019 122160 1060567 7527 32378 119803
2021-01-18 1808000 11620 188900 8527000 673900 1925000 185700 194900 232600 51080 149300 6953 10840 134800 14250 1655000 299700 123100 1067000 7597 33180 120200
2021-01-19 1821000 11670 190400 8589000 677200 1941000 186700 196200 233300 51350 150300 6985 10880 135600 14320 1669000 302900 124100 1070000 7671 34140 120500
2021-01-20 1832000 11720 192200 8650000 680600 1957000 187600 197600 234200 51630 151000 7017 10920 136300 14390 1684000 306200 125000 1073000 7744 35050 120800
2021-01-21 1844000 11770 193900 8718000 684500 1973000 188600 198900 235000 51910 151700 7048 10960 137000 14450 1698000 309000 126000 1075000 7817 36040 121100
2021-01-22 1853000 11820 195800 8765000 688700 1991000 189400 200300 235500 52180 152200 7079 11000 137700 14510 1712000 311500 126800 1080000 7891 36910 121400
2021-01-23 1865000 11860 197300 8809000 693100 2007000 189900 201800 236000 52460 152800 7110 11040 138400 14570 1726000 314400 127600 1081000 7966 37780 121700
2021-01-24 1875000 11920 198600 8830000 697000 2023000 190300 203000 236500 52740 153200 7141 11080 139100 14650 1736000 316700 128200 1084000 8041 38830 122000

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