COVID-19 short-term forecasts Confirmed 2021-01-25 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 2021-01-25

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-1712-032021-01-212021-01-2206-062021-01-1909-142021-01-222021-01-162021-01-1607-18 --12-28 --09-2212-1105-2612-302021-01-172021-01-1512-2211-222021-01-1409-08
Peak daily increment 11138 104 1122 2139 63723 7349 17627 1226 1662 2742 310 2590 30 160 10409 177 3322 910 5065 64 55 861 1085
Days since peak 11 100 53 4 3 233 6 133 3 9 9 191 28 125 45 244 26 8 10 34 64 11 139
Last total 1874801 8133 11770 202818 8871393 703178 2027746 190745 205162 241567 53479 154430 7317 11099 141984 15012 1771740 6204 312158 128366 1093938 8112 7490 38041 124112
Last daily increment 7578 32 20 1781 26816 4068 12261 1437 1216 275 261 218 19 0 1055 133 8521 0 914 714 0 55 17 408 403
Last week 55232 65 155 11728 297529 26027 88675 3868 8571 8999 2042 4153 367 247 5916 593 103344 0 10624 5007 33371 403 75 4595 2995
Previous peak date10-19 -- --07-1708-04 -- -- --07-2604-2408-05 --09-2106-0406-28 --10-05 -- -- --08-0208-1409-19 -- --
Previous peak daily increment 14378 1578 45272 1405 7778 420 66 177 795 22834 8380 89 119
Low between peaks 5479 93 19229 400 -4305 90 6 4599 1163 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-26 to 2021-02-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-25 1874801 202818 8871393 703178 2027746 190745 205162 241567 53479 154430 7317 11099 141984 15012 1771740 312158 128366 1093938 8112 38041 124112
2021-01-26 1890000 203800 8928000 705900 2043000 192300 206600 241600 53670 155400 7336 11150 142800 15110 1776000 313100 129200 1097000 8184 38800 124500
2021-01-27 1902000 206200 8992000 709300 2058000 193100 208000 242900 53710 156400 7357 11200 143700 15190 1794000 314100 130000 1100000 8256 39570 125000
2021-01-28 1913000 208500 9053000 713300 2073000 193900 209400 244200 54480 157400 7379 11250 144500 15280 1814000 315100 130800 1104000 8327 40330 125400
2021-01-29 1923000 210400 9104000 717900 2088000 194600 210700 244800 54690 157900 7399 11290 145300 15360 1829000 316100 131600 1107000 8398 41090 125800
2021-01-30 1931000 212300 9161000 722000 2102000 194600 212100 245500 54840 158800 7420 11340 146100 15450 1851000 317100 132400 1110000 8469 41870 126200
2021-01-31 1936000 213500 9187000 726100 2117000 194600 213500 247300 55260 159000 7441 11380 146900 15530 1862000 318100 133200 1114000 8541 42650 126700
2021-02-01 1943000 215000 9208000 729700 2132000 196100 214800 247900 55540 159200 7461 11430 147700 15610 1869000 319100 134000 1117000 8613 43440 127100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-26 to 2021-02-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-25 1874801 202818 8871393 703178 2027746 190745 205162 241567 53479 154430 7317 11099 141984 15012 1771740 312158 128366 1093938 8112 38041 124112
2021-01-26 1885000 204500 8930000 706600 2042000 191400 206600 242700 53720 155300 7364 11140 142900 15130 1784000 313600 129100 1097000 8175 38670 124600
2021-01-27 1896000 206500 8991000 710100 2058000 192100 208100 243700 53880 156100 7413 11200 143800 15200 1801000 315400 129900 1102000 8249 39460 125000
2021-01-28 1906000 208500 9053000 714200 2073000 192800 209600 244800 54410 156900 7461 11250 144600 15280 1818000 317100 130700 1105000 8324 40260 125500
2021-01-29 1916000 210200 9104000 718900 2089000 193500 211100 245400 54660 157500 7509 11310 145500 15360 1834000 318700 131600 1110000 8398 41080 125900
2021-01-30 1925000 211700 9167000 723300 2105000 193900 212700 246100 54900 158200 7557 11360 146300 15440 1852000 320200 132400 1112000 8473 41870 126400
2021-01-31 1933000 213200 9202000 727500 2122000 194300 214200 247000 55210 158600 7606 11420 147200 15530 1867000 322000 133100 1115000 8549 42740 126800
2021-02-01 1942000 214500 9229000 731400 2138000 195600 215800 247600 55560 159000 7654 11470 148100 15620 1879000 323300 133900 1118000 8625 43520 127200

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