COVID-19 short-term forecasts Confirmed 2021-01-28 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 2021-01-28

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-1712-03 -- --06-062021-01-1609-142021-01-232021-01-162021-01-162021-01-1909-2112-22 --09-2212-1105-2612-312021-01-172021-01-152021-01-1811-222021-01-1409-08
Peak daily increment 11060 104 1122 7349 16924 1226 1613 2028 312 791 66 29 160 10409 177 3385 897 4741 80 55 856 1085
Days since peak 14 103 56 236 12 136 5 12 12 9 129 37 128 48 247 28 11 13 10 67 14 142
Last total 1905524 8161 11845 210726 9058687 714143 2067575 192637 208610 246000 53989 157595 7470 11286 144992 15435 1825519 6253 316808 130917 1113970 8293 7520 39887 125364
Last daily increment 9471 0 29 2652 61811 4255 12270 571 1155 2465 0 1098 90 0 985 194 18670 0 1408 754 6731 50 11 559 406
Last week 51694 60 145 12469 304767 24077 80157 3329 7465 8842 1317 4639 327 323 5810 777 93229 49 9015 4547 31063 348 64 3717 2569
Previous peak date10-19 -- --07-1708-04 -- -- --07-2604-2408-0507-18 --06-0406-28 --10-05 -- -- --08-0208-1409-19 -- --
Previous peak daily increment 14378 1578 45272 1405 7778 420 2590 177 795 22834 8380 89 119
Low between peaks 5479 400 -4305 90 393 6 4599 1264 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-29 to 2021-02-04

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-28 1905524 210726 9058687 714143 2067575 192637 208610 246000 53989 157595 7470 11286 144992 15435 1825519 316808 130917 1113970 8293 39887 125364
2021-01-29 1923000 212800 9126000 718200 2091000 194500 210000 246200 54450 158400 7518 11340 145900 15540 1841000 318700 132200 1118000 8362 41270 125800
2021-01-30 1930000 214800 9189000 722500 2106000 194500 211400 247600 54740 159300 7562 11400 146800 15630 1861000 320600 132900 1122000 8430 41970 126300
2021-01-31 1935000 216700 9217000 726600 2118000 194500 212800 249600 55020 159600 7608 11440 147700 15730 1872000 322500 133400 1125000 8496 42690 126700
2021-02-01 1942000 218600 9242000 730200 2130000 195700 214200 250200 55300 159700 7651 11490 148500 15820 1880000 324400 134000 1129000 8563 43120 127200
2021-02-02 1952000 220500 9302000 733200 2144000 196400 215500 250700 55570 160800 7694 11540 149400 15910 1895000 326300 134900 1133000 8630 43750 127600
2021-02-03 1963000 222400 9365000 736300 2158000 197000 216900 252000 55850 161900 7738 11590 150200 16010 1913000 328200 135700 1137000 8698 44420 128100
2021-02-04 1973000 224400 9423000 740300 2170000 197700 218300 254000 56120 162800 7781 11640 151100 16100 1931000 330100 136600 1141000 8765 45110 128500

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-29 to 2021-02-04

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-28 1905524 210726 9058687 714143 2067575 192637 208610 246000 53989 157595 7470 11286 144992 15435 1825519 316808 130917 1113970 8293 39887 125364
2021-01-29 1915000 212900 9115000 718800 2082000 193300 209900 247100 54200 158400 7525 11330 146000 15560 1844000 318200 131700 1119000 8355 40640 125800
2021-01-30 1923000 214600 9174000 723200 2096000 193500 211300 248100 54440 159100 7569 11380 146900 15650 1862000 319600 132400 1123000 8421 41330 126200
2021-01-31 1930000 216100 9200000 727300 2110000 193700 212800 249400 54780 159600 7614 11430 147800 15740 1877000 320800 133000 1127000 8486 42030 126600
2021-02-01 1938000 217600 9222000 731100 2124000 194500 214200 250100 55060 160000 7658 11480 148700 15840 1889000 321900 133700 1130000 8551 42630 127000
2021-02-02 1948000 219200 9283000 734300 2138000 195200 215700 250700 55290 160800 7703 11540 149600 15950 1904000 323200 134500 1135000 8617 43310 127400
2021-02-03 1959000 221200 9342000 737700 2153000 195800 217100 251700 55520 161600 7748 11590 150500 16030 1920000 324800 135300 1138000 8683 44030 127800
2021-02-04 1969000 223200 9405000 741900 2168000 196500 218600 252700 55970 162400 7792 11640 151400 16120 1937000 326000 136200 1142000 8750 44840 128200

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