COVID-19 short-term forecasts Deaths 2020-09-05 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:
    [2020-07-01] 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.

Peak increase in estimated trend of Deaths in Latin America 2020-09-05

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date --08-0107-2107-1708-23 -- --05-1008-0706-0507-1007-2906-2407-23 --07-2308-13
Peak daily increment 80 1065 785 563 167 11 43 6 35 683 28 2947 8
Days from 100 to peak 85 115 93 133 39 46 5 12 82 79 98 107 28
Days from peak/2 to peak 100 106 63 116 40 116 29 83 113 72 116 78 133
Last total 9739 5398 126203 11551 20886 478 1840 6724 752 2845 212 2006 67326 2075 412 29554 420
Last daily increment 116 55 701 57 268 9 39 50 8 20 0 22 475 12 14 149 8
Last week 1282 432 5375 307 1523 60 159 169 39 105 11 148 3168 80 104 947 39
Days since peak 35 46 50 13 118 29 92 57 38 73 44 44 23

Deaths count forecast Latin America (bold red line in graphs) 2020-09-06 to 2020-09-12

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-05 9739 5398 126203 11551 20886 478 1840 6724 752 2845 2006 67326 2075 412 29554 420
2020-09-06 9830 5464 127100 11620 21190 485 1878 6757 759 2887 2028 67750 2089 424 29570 427
2020-09-07 10060 5529 127600 11670 21400 501 1917 6788 765 2901 2049 67990 2102 437 29610 433
2020-09-08 10320 5592 128800 11710 21670 512 1954 6819 772 2916 2070 68730 2115 450 29690 440
2020-09-09 10550 5656 129800 11740 21910 523 1992 6849 779 2936 2091 69270 2128 464 29780 447
2020-09-10 10760 5720 130700 11820 22110 531 2030 6879 785 2954 2111 69760 2141 477 29860 453
2020-09-11 11000 5784 131500 11890 22170 541 2069 6909 792 2972 2132 70220 2154 491 29950 460
2020-09-12 11110 5849 132100 11950 22440 554 2109 6938 798 2993 2153 70710 2168 506 30030 466

Deaths count average forecast Latin America (bold black line in graphs) 2020-09-06 to 2020-09-12

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-05 9739 5398 126203 11551 20886 478 1840 6724 752 2845 2006 67326 2075 412 29554 420
2020-09-06 9870 5457 126800 11600 21190 485 1858 6743 758 2859 2029 67510 2085 419 29650 424
2020-09-07 10090 5528 127400 11660 21470 499 1887 6766 765 2876 2053 67770 2098 432 29790 431
2020-09-08 10320 5601 128400 11710 21790 511 1916 6794 772 2894 2077 68530 2111 446 29950 438
2020-09-09 10550 5678 129400 11760 22080 524 1947 6828 779 2915 2102 69060 2123 459 30110 445
2020-09-10 10770 5755 130200 11840 22400 534 1976 6859 787 2937 2126 69510 2136 474 30270 451
2020-09-11 11000 5830 131100 11900 22630 547 2008 6895 794 2962 2152 69960 2149 489 30440 459
2020-09-12 11170 5911 131800 11970 22950 561 2039 6930 802 2987 2179 70510 2162 508 30620 466

Deaths count scenario forecast (bold purple line in graphs) 2020-09-06 to 2020-09-14

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-05 9739 5398 126203 11551 20886 478 1840 6724 752 2845 2006 67326 2075 412 29554 420
2020-09-06 10020 5450 127000 11600 21150 490 1860 6748 758 2862 2024 67710 2086 435 29680 423
2020-09-07 10210 5502 127600 11640 21410 505 1893 6769 764 2879 2039 68180 2097 451 29810 428
2020-09-08 10500 5548 128300 11690 21650 518 1919 6791 770 2895 2052 68600 2107 470 29910 433
2020-09-09 10760 5590 128900 11730 21890 536 1956 6811 775 2909 2062 69040 2115 489 30020 438
2020-09-10 11010 5621 129400 11760 22120 549 1998 6828 780 2924 2083 69440 2124 505 30120 442
2020-09-11 11280 5655 130000 11790 22320 566 2036 6841 786 2935 2100 69860 2131 523 30210 446
2020-09-12 11510 5684 130400 11820 22520 580 2072 6854 789 2944 2104 70240 2138 539 30280 449
2020-09-13 11770 5709 130800 11850 22700 596 2100 6864 793 2952 2108 70650 2145 556 30330 453
2020-09-14 12030 5733 131200 11870 22860 611 2130 6875 797 2962 2122 70980 2151 574 30370 456

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

[2020-07-01] 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 Deaths