COVID-19 short-term forecasts Confirmed 2020-09-03 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 Confirmed in Latin America 2020-09-03

ArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameVenezuela
Peak date -- --07-1708-0406-0608-23 --07-2604-2408-0507-1806-06 -- --08-0105-2607-13 --08-0208-1508-13
Peak daily increment 1578 46393 7363 14671 1409 7757 421 2699 179 6791 145 1089 8342 87 1010
Days from 100 to peak 107 142 83 156 126 37 118 99 31 135 7 115 137 69 139
Days from peak/2 to peak 83 113 64 112 119 18 107 65 33 114 9 109 115 74 89
Last total 451198 2386 118781 4041638 416501 641574 44458 96629 116360 26000 76358 8301 63158 2896 616894 4668 94914 19959 657129 4215 49877
Last daily increment 12026 49 853 43773 1762 8253 1153 1002 903 96 714 43 632 213 5937 0 830 821 0 66 994
Last week 59189 329 4372 236835 10529 51082 5973 3239 4219 585 3437 140 4348 885 31156 174 4290 4086 35132 367 5998
Days since peak 48 30 89 11 39 132 29 47 89 33 100 52 32 19 21

Confirmed count forecast Latin America (bold red line in graphs) 2020-09-04 to 2020-09-10

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-09-03 451198 2386 118781 4041638 416501 641574 44458 96629 116360 26000 76358 63158 2896 616894 94914 19959 657129 4215 49877
2020-09-04 464700 2517 119100 4078000 418100 651000 45730 98100 117200 26110 77150 63820 3305 622500 95630 21290 674200 4277 50800
2020-09-05 478600 2651 119400 4118000 420000 660300 47030 98900 118000 26220 77610 64460 3461 628100 96340 22100 680200 4337 51690
2020-09-06 492900 2785 119700 4135000 421800 669600 48370 99700 118800 26320 77610 65100 3806 631700 97050 22870 689200 4398 52590
2020-09-07 507800 2921 120100 4169000 423400 678900 49750 100400 119600 26420 77610 65730 4028 635000 97740 23720 696600 4459 53470
2020-09-08 523200 3060 120400 4212000 424700 688300 51170 101000 120400 26530 78160 66360 4196 640900 98440 24660 701300 4519 54360
2020-09-09 539000 3205 120700 4256000 426000 697700 52630 101800 121200 26630 78880 66990 4513 645500 99150 25750 705900 4580 55250
2020-09-10 555300 3356 121100 4299000 427700 707200 54130 102500 122000 26740 79820 67630 4708 651300 99850 26690 708600 4642 56150

Confirmed count average forecast Latin America (bold black line in graphs) 2020-09-04 to 2020-09-10

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-09-03 451198 2386 118781 4041638 416501 641574 44458 96629 116360 26000 76358 63158 2896 616894 94914 19959 657129 4215 49877
2020-09-04 463800 2495 119500 4069000 418000 648500 45630 96910 116800 26080 77060 63760 3008 621200 95500 20750 663300 4257 50450
2020-09-05 477500 2568 120400 4107000 419800 656900 46940 97140 117400 26170 77750 64420 3095 626500 96200 21430 670500 4314 51310
2020-09-06 491600 2624 121200 4123000 421700 665500 48160 97470 118100 26260 78200 65080 3325 630500 96890 22110 678100 4372 52190
2020-09-07 506200 2674 122000 4159000 423200 673800 49510 97810 118700 26350 78670 65780 3436 634300 97570 22800 685500 4432 53070
2020-09-08 521200 2741 122900 4201000 424600 682600 50840 98220 119400 26450 79480 66490 3511 639800 98230 23670 692600 4493 53960
2020-09-09 536800 2793 123900 4243000 425900 691700 52260 98650 120000 26540 80350 67220 3713 644800 98890 24650 699800 4556 54880
2020-09-10 552700 2837 124800 4285000 427500 701600 53740 99020 120700 26640 81300 67950 3858 650100 99560 25540 706900 4620 55810

Confirmed count scenario forecast (bold purple line in graphs) 2020-09-04 to 2020-09-12

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-09-03 451198 2386 118781 4041638 416501 641574 44458 96629 116360 26000 76358 63158 2896 616894 94914 19959 657129 4215 49877
2020-09-04 460200 2452 119300 4063000 418200 648600 45400 96630 117200 26070 76840 64520 3041 620700 95510 20680 670400 4260 50630
2020-09-05 469800 2500 119900 4095000 419700 654600 46370 96960 117800 26140 77260 65360 3233 624600 96000 21310 675900 4303 51430
2020-09-06 480400 2542 120300 4124000 421200 659700 47320 97320 118400 26210 77660 66250 3367 628200 96470 21780 680000 4346 52120
2020-09-07 489300 2587 120800 4149000 422600 665200 48230 97550 118900 26260 77980 67770 3519 631300 96890 22430 685600 4382 52980
2020-09-08 498600 2644 120900 4173000 423900 669400 49160 97740 119400 26330 78200 68690 3695 634400 97310 23190 689400 4418 53970
2020-09-09 506400 2683 121200 4197000 425300 674500 50050 97970 119900 26390 78370 69710 3822 637000 97600 23720 693300 4448 54640
2020-09-10 516800 2728 121400 4215000 426900 679000 50970 98130 120300 26440 78620 70440 3999 639600 97890 24420 697300 4469 55470
2020-09-11 526000 2769 121700 4235000 428100 682800 51840 98350 120900 26500 78770 71690 4152 641900 98250 25020 701400 4500 56140
2020-09-12 531500 2805 122000 4251000 429500 687600 52730 98500 121500 26560 78820 72560 4289 644000 98530 25510 704900 4525 56750

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 Confirmed