COVID-19 short-term forecasts Deaths 2020-06-29 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 The documentation that is provided is still in progress and not peer reviewed.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 will be streamed here.

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) 2020-06-30 to 2020-07-06

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-29 1280 1071 58314 5575 3256 733 4502 746 485 27121 620 9504
2020-06-30 1300 1090 59000 5670 3370 740 4520 770 490 27500 630 9700
2020-07-01 1330 1120 59900 5820 3510 750 4550 800 510 28100 650 9900
2020-07-02 1360 1160 60700 5980 3680 760 4580 830 520 28600 670 10000
2020-07-03 1400 1200 61600 6130 3850 770 4610 870 540 29200 690 10200
2020-07-04 1430 1250 62400 6300 4020 770 4640 900 550 29800 700 10400
2020-07-05 1460 1290 63300 6460 4210 780 4670 940 570 30400 720 10600
2020-07-06 1500 1340 64200 6640 4410 790 4700 980 590 31000 740 10800

Deaths count forecast Latin America (bold red line in graphs) 2020-06-30 to 2020-07-06

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-29 1280 1071 58314 5575 3256 733 4502 746 485 27121 620 9504
2020-06-30 1310 1120 58900 5710 3430 740 4530 770 500 27500 640 9700
2020-07-01 1350 1160 59600 5850 3570 750 4560 800 500 28000 660 9900
2020-07-02 1380 1210 60200 5980 3720 760 4600 820 510 28400 680 10000
2020-07-03 1410 1260 60800 6120 3880 760 4630 850 520 28900 690 10200
2020-07-04 1450 1310 61400 6260 4040 770 4670 880 530 29300 710 10400
2020-07-05 1480 1370 62000 6400 4210 780 4700 910 540 29800 730 10600
2020-07-06 1520 1420 62700 6550 4390 790 4740 940 550 30200 750 10800

Deaths count scenario forecast (bold purple line in graphs) 2020-06-30 to 2020-07-08

DateArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
2020-06-29 1280 1071 58314 5575 3256 733 4502 746 485 27121 620 9504
2020-06-30 1290 1110 59000 5650 3450 740 4510 780 490 27600 630 9600
2020-07-01 1320 1150 59700 5740 3600 750 4540 800 500 28100 650 9800
2020-07-02 1350 1180 60400 5850 3720 760 4560 830 530 28800 660 10000
2020-07-03 1380 1220 60900 5970 3830 760 4580 850 550 29400 680 10100
2020-07-04 1410 1260 61500 6060 4110 770 4600 880 570 29900 700 10200
2020-07-05 1440 1300 62000 6120 4250 780 4620 910 590 30100 710 10300
2020-07-06 1460 1340 62500 6200 4380 780 4630 930 600 30500 730 10400
2020-07-07 1480 1370 63000 6260 4530 790 4660 950 610 30900 740 10500
2020-07-08 1500 1400 63500 6320 4680 790 4680 970 630 31300 750 10700

Peak increase in estimated trend of Deaths in Latin America 2020-06-29

ArgentinaBoliviaBrazilChileColombiaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaPeru
Peak date -- --06-0406-08 --04-1205-02 -- --06-24 --06-14
Peak daily increment 1097 309 15 166 836 245
Days from 100 to peak 68 53 4 30 79 68
Days from peak/2 to peak 59 47 18 31 70 67
Last total 1280 1071 58314 5575 3256 733 4502 746 485 27121 620 9504
Last daily increment 48 57 692 66 0 7 73 19 6 473 16 187
Last week 202 225 5669 1070 732 58 228 164 80 3744 84 1100
Days since peak 25 21 78 58 5 15

Initial visual evaluation of forecasts of Deaths