COVID-19 short-term forecasts Deaths 2020-07-06 Latin American Countries


Gnereal 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 2020-07-06

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaPeru
Peak date -- -- --06-08 --04-1205-10 --06-05 --07-01 -- --
Peak daily increment 512 22 170 43 681
Days from 100 to peak 53 5 39 5 86
Days from peak/2 to peak 35 17 40 29 79
Last total 1582 1476 65487 6384 4305 804 4821 223 981 656 31119 770 10772
Last daily increment 75 42 620 76 127 10 40 6 34 17 480 23 183
Last week 275 353 5893 696 929 57 294 49 208 159 3350 139 1095
Days since peak 28 85 57 31 5

Deaths count forecast Latin America (bold red line in graphs) 2020-07-07 to 2020-07-13

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaPeru
2020-07-06 1582 1476 65487 6384 4305 804 4821 223 981 656 31119 770 10772
2020-07-07 1633 1538 66670 6578 4552 814 4862 238 1010 671 32050 791 11110
2020-07-08 1686 1625 67710 6680 4700 823 4902 253 1038 686 32800 813 11470
2020-07-09 1740 1712 68870 6841 4902 832 4942 270 1067 700 33420 836 11830
2020-07-10 1796 1780 69980 6983 5156 842 4981 287 1095 714 34030 859 12230
2020-07-11 1854 1860 70990 7145 5391 851 5021 305 1123 729 34530 883 12640
2020-07-12 1913 1944 71510 7265 5726 860 5061 323 1151 743 34870 908 13060
2020-07-13 1975 2010 72160 7346 5879 870 5101 344 1180 757 35370 933 13500

Deaths count average forecast Latin America (bold black line in graphs) 2020-07-07 to 2020-07-13

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaPeru
2020-07-06 1582 1476 65487 6384 4305 804 4821 223 981 656 31119 770 10772
2020-07-07 1615 1527 66600 6454 4444 812 4849 231 1008 672 31720 790 11010
2020-07-08 1666 1592 67660 6575 4590 821 4889 240 1042 695 32470 812 11330
2020-07-09 1718 1658 68810 6731 4784 831 4936 250 1076 718 33120 835 11660
2020-07-10 1772 1722 69950 6876 5039 840 4984 261 1111 739 33760 861 12000
2020-07-11 1826 1791 71030 7046 5267 849 5027 273 1147 760 34370 888 12370
2020-07-12 1882 1863 71720 7227 5656 858 5063 284 1184 784 34850 914 12740
2020-07-13 1942 1935 72520 7362 5817 868 5103 297 1223 809 35440 943 13170

Deaths count scenario forecast (bold purple line in graphs) 2020-07-07 to 2020-07-15

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaPeru
2020-07-06 1582 1476 65487 6384 4305 804 4821 223 981 656 31119 770 10772
2020-07-07 1607 1538 66660 6473 4494 810 4849 235 998 684 31860 791 11020
2020-07-08 1650 1595 67500 6551 4678 818 4883 243 1021 706 32450 814 11170
2020-07-09 1684 1652 68480 6592 4841 825 4911 255 1040 720 32890 833 11410
2020-07-10 1718 1708 69290 6611 4978 831 4935 268 1057 749 33440 853 11670
2020-07-11 1746 1767 70140 6681 5134 837 4954 276 1072 763 33970 872 11780
2020-07-12 1775 1822 70900 6713 5293 841 4970 291 1088 804 34400 892 11920
2020-07-13 1815 1883 71630 6760 5428 846 4987 301 1098 819 34890 903 12290
2020-07-14 1842 1936 72290 6812 5566 849 5004 309 1110 821 35310 915 12630
2020-07-15 1865 1987 72970 6863 5654 852 5018 318 1128 824 35710 933 12860

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