COVID-19 short-term forecasts Confirmed 2021-01-13 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 2021-01-13

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-03 -- --06-06 --09-1407-2609-1612-2407-1809-2112-27 --09-2212-1105-26 -- --08-0212-2211-222021-01-1009-08
Peak daily increment 14378 104 1122 7350 1226 1405 1216 249 2590 66 36 160 10566 177 8380 63 55 927 1085
Days since peak 86 88 41 221 121 171 119 20 179 114 17 113 33 232 164 22 52 3 127
Last total 1757429 8011 11419 178818 8256536 652525 1831980 182156 186383 224315 49539 145986 6696 10569 131009 13852 1571901 6152 288408 118592 1040231 7247 7305 28475 117811
Last daily increment 12725 7 53 2057 60899 3390 15898 1063 1595 1748 0 1004 43 154 1204 92 15873 0 3315 1002 2881 69 19 629 512
Last week 67423 52 267 9927 294863 23349 94633 5749 8890 5930 1591 4912 227 366 5891 484 78332 55 19317 5736 18213 497 95 4668 2489
Previous peak date -- -- --07-1708-04 -- -- -- --04-2408-05 -- --06-0607-03 --10-05 -- --06-27 --08-1409-19 -- --
Previous peak daily increment 1622 45272 7778 420 179 870 22834 155 89 119
Low between peaks -4305 90 6 4599 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-14 to 2021-01-20

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-13 1757429 11419 178818 8256536 652525 1831980 182156 186383 224315 49539 145986 6696 10569 131009 13852 1571901 288408 118592 1040231 7247 28475 117811
2021-01-14 1760000 11470 180700 8288000 656100 1854000 183300 186400 225200 49850 146000 6720 10570 131900 13920 1575000 292000 120000 1040000 7321 28920 118100
2021-01-15 1772000 11530 182500 8331000 660000 1875000 184100 187600 226000 50090 146700 6741 10600 132800 13990 1588000 295600 121000 1043000 7391 29370 118500
2021-01-16 1781000 11580 184100 8378000 664000 1892000 184100 189200 226700 50350 147300 6764 10630 133700 14060 1600000 298700 122000 1043000 7463 29830 118800
2021-01-17 1787000 11630 185600 8402000 666700 1908000 184100 190300 227400 50590 147700 6784 10660 134500 14120 1608000 300900 122800 1044000 7531 30280 119100
2021-01-18 1795000 11680 187100 8426000 670200 1924000 186600 191400 228100 50840 147800 6805 10700 135400 14180 1615000 302800 123800 1050000 7600 30730 119300
2021-01-19 1808000 11730 188600 8486000 673000 1940000 187700 192600 228900 51090 149200 6825 10730 136300 14250 1628000 306500 124700 1052000 7669 31190 119600
2021-01-20 1820000 11780 190000 8539000 676300 1955000 188700 193800 229600 51340 150000 6846 10760 137200 14310 1642000 310100 125600 1054000 7739 31650 119900

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-14 to 2021-01-20

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-13 1757429 11419 178818 8256536 652525 1831980 182156 186383 224315 49539 145986 6696 10569 131009 13852 1571901 288408 118592 1040231 7247 28475 117811
2021-01-14 1769000 11460 180600 8333000 656300 1848000 183300 187700 225300 49810 146900 6730 10630 131900 13930 1585000 291900 119600 1042000 7325 29220 118200
2021-01-15 1780000 11500 182200 8373000 660400 1863000 184100 188900 225900 50070 147300 6751 10660 132400 13990 1597000 295100 120500 1045000 7399 29940 118400
2021-01-16 1790000 11550 183600 8415000 664400 1877000 184500 190300 226400 50340 147800 6772 10690 133000 14050 1608000 298000 121300 1046000 7473 30660 118700
2021-01-17 1799000 11590 185000 8438000 667500 1891000 184900 191400 226800 50600 148200 6793 10720 133500 14110 1617000 300100 122000 1047000 7547 31460 118900
2021-01-18 1807000 11640 186300 8457000 670800 1904000 186500 192500 227200 50860 148400 6814 10750 134000 14180 1625000 302000 122700 1050000 7621 32210 119200
2021-01-19 1817000 11680 187600 8498000 673800 1918000 187300 193500 227600 51130 148900 6834 10770 134500 14240 1636000 305000 123400 1052000 7696 32930 119400
2021-01-20 1827000 11730 189100 8545000 677000 1932000 188300 194400 228100 51400 149400 6855 10800 135000 14300 1646000 308800 124200 1053000 7772 33750 119700

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