COVID-19 short-term forecasts Confirmed 2020-10-16 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:
    [2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.

Peak increase in estimated trend of Confirmed in Latin America 2020-10-16

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) -- -- --07-1708-0406-0608-1309-1407-2609-1508-0507-1809-2306-0606-2809-2410-0505-2607-13 --08-0208-1309-15 --09-08
Peak daily increment 1578 45353 7362 11286 1251 1408 1252 420 2699 63 179 795 156 24260 145 1089 8364 89 122 1086
Days since peak 91 73 132 64 32 82 31 72 90 23 132 110 22 11 143 95 75 64 31 38
Last total 965609 5517 2728 139562 5200300 488190 945354 94348 120450 151659 31265 100431 3672 8925 86691 8132 841661 5353 123498 53482 859740 5113 5241 2450 85758
Last daily increment 16546 132 46 243 30914 1694 8372 1196 384 1299 204 666 52 0 602 65 6751 0 615 886 0 19 47 33 289
Last week 81727 494 301 1099 117663 8595 42607 6909 2436 4831 1314 2887 267 65 3545 573 27333 89 3832 4504 13652 78 198 182 3305
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- -- -- -- -- -- -- --06-27 -- -- -- -- --
Previous peak daily increment 7756 155
Low between peaks -4346

Confirmed count forecast Latin America (bold red line in graphs) 2020-10-17 to 2020-10-23

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruVenezuela
2020-10-16 965609 5517 2728 139562 5200300 488190 945354 94348 120450 151659 31265 100431 3672 86691 8132 841661 123498 53482 859740 85758
2020-10-17 980000 5556 2774 139800 5246000 490600 952400 95500 120900 152600 31270 101300 3716 87290 8268 850100 124100 54880 862800 86260
2020-10-18 994000 5635 2819 140100 5256000 492100 959200 95500 121300 153500 31470 101400 3760 87880 8398 854400 124800 55790 865600 86760
2020-10-19 1008000 5720 2864 140300 5263000 493500 965900 97300 121800 154300 31540 101500 3803 88450 8530 859800 125400 56720 868500 87250
2020-10-20 1021000 5802 2908 140600 5282000 494800 972500 98300 122200 155200 31760 102100 3846 89020 8661 865100 126000 57710 871200 87750
2020-10-21 1035000 5885 2952 140800 5308000 495900 979100 99700 122600 156000 31960 102800 3889 89590 8793 870200 126700 58560 874000 88240
2020-10-22 1050000 5968 2998 141100 5336000 497000 985800 101000 123100 156900 32180 103400 3933 90170 8925 876300 127300 59540 876700 88740
2020-10-23 1064000 6052 3043 141400 5360000 498600 992500 102100 123500 157700 32340 104000 3977 90740 9058 881500 127900 60440 879500 89240

Confirmed count average forecast Latin America (bold black line in graphs) 2020-10-17 to 2020-10-23

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruVenezuela
2020-10-16 965609 5517 2728 139562 5200300 488190 945354 94348 120450 151659 31265 100431 3672 86691 8132 841661 123498 53482 859740 85758
2020-10-17 981000 5681 2775 139700 5232000 489900 952600 95600 120900 152600 31440 101000 3713 87290 8237 848000 124200 54340 861900 86230
2020-10-18 994000 5753 2828 139900 5240000 491400 959500 96200 121400 153100 31580 101400 3753 87880 8347 852200 124700 55140 864300 86810
2020-10-19 1008000 5830 2878 140100 5247000 492800 966400 97600 121800 153700 31690 101700 3794 88470 8436 856700 125300 55910 866700 87400
2020-10-20 1021000 5891 2924 140400 5265000 494100 973300 98600 122200 154300 31830 102300 3835 89050 8531 861400 125900 56770 869100 87990
2020-10-21 1035000 5989 2977 140700 5292000 495200 980300 99800 122700 154900 31970 102900 3877 89640 8611 866100 126400 57560 871400 88590
2020-10-22 1050000 6077 3029 141000 5325000 496700 987300 101000 123200 155500 32110 103500 3919 90240 8689 871600 127000 58500 873800 89210
2020-10-23 1064000 6145 3085 141200 5349000 498400 994400 102100 123700 156200 32220 104100 3962 90830 8765 876900 127600 59470 876100 89830

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

[2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.
[2020-10-10]Temporarily 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