COVID-19 short-term forecasts Deaths 2020-07-01 Latin American Countries


Disclaimer

  • 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. The documentation that is provided is still in progress and not peer reviewed. 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.

Recent changes

[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.
[2020-03-26] Scenario forecasts that are based on what happened in China earlier this year, only for Italy.
[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).
[2020-04-02] Now including more US States, based on New York Times data.
[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.
[2020-04-08] Minor correction to peak estimates. Added table with scenario forecasts.
[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.
[2020-04-10] Updated documentation with better description of short-term estimates and peak determination.
[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).
[2020-04-17] Bird and Nielsen look into nowcasting death counts in England.
[2020-04-24] A summary of our work on short-term COVID-19 forecasting appeared as a voxeu.
[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.
[2020-04-29] 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.
[2020-05-06] 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.
[2020-05-13] 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.
[2020-05-18] Minor fixes to the improved version of scenario forecasting, backported to 2020-05-13.
[2020-05-20] Problem with UK confirmed cases: negative daily count. This makes the forecasts temporarily unreliable.
Updated the second paper.
[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.
[2020-06-06] Removed Brazil from yesterday's forecasts (only; last observation 2020-06-05).
[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.
[2020-06-29] 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.
[2020-07-01] 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.

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.

Deaths count average forecast Latin America (bold black line in graphs) 2020-07-02 to 2020-07-08

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-07-01 1351 1201 60632 5753 3488 754 4576 817 542 28510 645 9860
2020-07-02 1384 1223 61630 5897 3551 762 4607 832 550 29210 659 10030
2020-07-03 1424 1264 62590 6081 3681 772 4651 868 563 29920 675 10200
2020-07-04 1464 1310 63590 6293 3836 781 4683 908 580 30490 694 10400
2020-07-05 1506 1364 64210 6475 4106 790 4717 946 599 30980 711 10610
2020-07-06 1551 1420 64960 6603 4176 800 4756 984 620 31580 729 10810
2020-07-07 1597 1479 66250 6744 4282 810 4795 1029 641 32400 748 11030
2020-07-08 1645 1540 67410 6953 4369 819 4829 1075 663 33450 768 11240

Deaths count forecast Latin America (bold red line in graphs) 2020-07-02 to 2020-07-08

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-07-01 1351 1201 60632 5753 3488 754 4576 817 542 28510 645 9860
2020-07-02 1389 1260 61830 5986 3488 764 4616 843 565 29160 657 10060
2020-07-03 1428 1321 62790 6177 3590 773 4657 867 589 29790 670 10250
2020-07-04 1467 1385 63790 6420 3733 782 4697 892 614 30420 684 10450
2020-07-05 1508 1450 64320 6595 3971 791 4737 917 639 31040 697 10640
2020-07-06 1551 1519 64990 6646 4009 800 4777 942 666 31660 711 10830
2020-07-07 1594 1591 66250 6717 4119 809 4818 967 694 32300 726 11020
2020-07-08 1639 1667 67310 6837 4201 819 4860 992 723 32940 741 11210

Deaths count scenario forecast (bold purple line in graphs) 2020-07-02 to 2020-07-10

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-07-01 1351 1201 60632 5753 3488 754 4576 817 542 28510 645 9860
2020-07-02 1380 1234 61540 5881 3619 764 4607 821 544 29230 660 10050
2020-07-03 1413 1284 62440 5999 3757 773 4632 836 562 29930 676 10230
2020-07-04 1450 1334 63540 6116 3882 780 4658 879 577 30610 694 10440
2020-07-05 1485 1383 64780 6209 4073 788 4676 915 588 31340 712 10670
2020-07-06 1527 1425 65870 6287 4240 795 4695 965 609 32000 731 10870
2020-07-07 1565 1465 66960 6359 4373 801 4709 999 625 32790 750 11060
2020-07-08 1604 1518 68110 6425 4534 807 4726 1025 644 33460 768 11240
2020-07-09 1645 1561 69250 6488 4707 814 4741 1025 660 34140 787 11500
2020-07-10 1687 1622 70290 6553 4832 821 4760 1025 676 34870 804 11770

Peak increase in estimated trend of Deaths in Latin America 2020-07-01

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
Peak date -- --06-2606-08 --04-1205-10 -- -- -- --06-26
Peak daily increment 1032 484 22 170 206
Days from 100 to peak 90 54 5 39 80
Days from peak/2 to peak 81 37 17 40 80
Last total 1351 1201 60632 5753 3488 754 4576 817 542 28510 645 9860
Last daily increment 44 78 1038 65 112 7 49 44 45 741 14 183
Last week 201 288 5661 850 877 56 233 194 116 3450 81 1099
Days since peak 5 23 80 52 5

Initial visual evaluation of forecasts of Deaths