COVID-19 short-term forecasts Confirmed 2021-02-04 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 2021-02-04

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1210-1712-032021-01-302021-01-202021-01-232021-01-1509-142021-01-192021-01-162021-01-1607-182021-01-232021-01-23 --09-222021-01-2605-262021-01-072021-01-142021-01-152021-01-0711-222021-01-142021-01-18
Peak daily increment 10987 104 1122 2225 54305 4135 16730 1226 1550 1800 304 2590 53 63 160 16804 177 3390 888 4823 79 55 846 524
Days since peak 23 110 63 5 15 12 20 143 16 19 19 201 12 12 135 9 254 28 21 20 28 74 21 17
Last total 1961635 8247 12008 224234 9396293 740237 2135412 195992 218948 253339 55821 161665 7780 11692 152225 16250 1886245 6299 324489 136023 1158337 8595 7607 43804 128775
Last daily increment 8891 16 30 1787 56873 3592 9790 455 1671 949 198 699 66 20 1122 177 0 0 1107 794 0 39 21 589 460
Last week 46273 80 131 10842 277780 21488 57779 2716 8536 6652 1832 3329 252 290 6115 788 44352 46 6236 4137 38652 231 74 3275 2999
Previous peak date10-19 -- --07-1708-0406-06 -- --07-2604-2408-05 --09-2106-0407-03 --10-05 -- -- --08-0208-1409-19 --09-08
Previous peak daily increment 14378 1578 45271 7349 1405 7778 420 66 177 870 22833 8380 89 119 1085
Low between peaks 5479 93 19229 1343 400 -4305 90 12 6 4599 1306 1 23 242

Confirmed count forecast Latin America (bold red line in graphs) 2021-02-05 to 2021-02-11

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-02-04 1961635 224234 9396293 740237 2135412 195992 218948 253339 55821 161665 7780 11692 152225 16250 1886245 324489 136023 1158337 8595 43804 128775
2021-02-05 1976000 227000 9464000 745600 2145000 197500 220300 255600 56030 162900 7831 11750 153100 16360 1926000 328200 136800 1164000 8646 44410 129200
2021-02-06 1983000 228800 9522000 749500 2154000 197500 221700 258100 56100 163700 7880 11810 154100 16470 1940000 329700 137500 1168000 8697 45010 129700
2021-02-07 1987000 230100 9544000 753400 2163000 197500 223100 259400 56780 163900 7927 11860 155000 16580 1946000 330900 138200 1174000 8747 45600 130200
2021-02-08 1993000 231400 9565000 756900 2172000 198400 224400 259500 57030 164000 7973 11920 155400 16680 1950000 331700 138900 1179000 8797 46200 130600
2021-02-09 2001000 233600 9621000 759800 2181000 198900 225700 259900 57050 164800 8020 11970 156500 16780 1959000 333200 139600 1184000 8847 46790 131100
2021-02-10 2010000 235800 9673000 762300 2190000 199300 227100 261100 57370 165500 8067 12020 158200 16890 1970000 334600 140300 1189000 8897 47400 131500
2021-02-11 2019000 237700 9727000 765900 2199000 199800 228400 262400 57640 166200 8113 12080 159200 16990 1976000 335700 141000 1194000 8948 48000 132000

Confirmed count average forecast Latin America (bold black line in graphs) 2021-02-05 to 2021-02-11

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-02-04 1961635 224234 9396293 740237 2135412 195992 218948 253339 55821 161665 7780 11692 152225 16250 1886245 324489 136023 1158337 8595 43804 128775
2021-02-05 1971000 226300 9449000 744300 2145000 196500 220300 253900 56050 162200 7827 11740 153400 16360 1896000 325800 136800 1164000 8641 44320 129200
2021-02-06 1977000 227900 9506000 748200 2153000 196600 221600 255400 56260 162900 7872 11800 154100 16450 1909000 326800 137400 1169000 8687 44850 129600
2021-02-07 1983000 229100 9528000 752100 2162000 196700 222900 256300 56540 163300 7917 11860 154800 16550 1919000 327500 138000 1173000 8733 45390 130000
2021-02-08 1989000 230300 9543000 755600 2170000 197400 224200 256700 56770 163500 7962 11910 155400 16660 1926000 327900 138700 1178000 8779 45890 130400
2021-02-09 1997000 232200 9602000 758700 2178000 197800 225500 257100 56970 164200 8007 11970 156100 16780 1938000 328700 139300 1182000 8825 46420 130700
2021-02-10 2004000 234000 9658000 761700 2187000 198200 226800 258000 57210 164900 8052 12020 156900 16880 1952000 329500 140000 1186000 8872 46980 131100
2021-02-11 2012000 236000 9714000 766000 2196000 198600 228100 259200 57440 165600 8097 12080 157600 17000 1967000 330400 140700 1191000 8919 47550 131400

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