COVID-19 short-term forecasts Confirmed 2020-08-29 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 2020-08-29

ArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameVenezuela
Peak date -- --07-1707-2906-0608-23 --07-2704-2408-0407-1806-06 -- --07-2905-2607-1308-2608-2608-1608-13
Peak daily increment 1578 46690 7363 16773 1432 7757 421 2699 179 6809 145 1089 575 8588 88 1011
Days from 100 to peak 107 135 83 157 127 37 117 99 31 132 7 115 143 162 70 139
Days from peak/2 to peak 83 107 64 110 120 18 106 65 33 111 9 109 118 139 75 89
Last total 401239 2135 115354 3846153 408009 599884 39699 93732 112906 25537 73679 8174 59645 2113 591712 4494 91337 16474 629961 3954 44946
Last daily increment 9230 78 945 41350 2037 9392 1214 342 765 122 758 13 835 102 5974 0 713 601 7964 106 1067
Last week 59085 370 6205 240370 10344 58745 5879 2571 5137 915 5491 92 5134 584 31548 183 4437 3241 44725 347 5382
Days since peak 43 31 84 6 33 127 25 42 84 31 95 47 3 3 13 16

Confirmed count forecast Latin America (bold red line in graphs) 2020-08-30 to 2020-09-05

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-08-29 401239 2135 115354 3846153 408009 599884 39699 93732 112906 25537 73679 59645 2113 591712 91337 16474 629961 3954 44946
2020-08-30 409200 2245 116500 3885000 409900 608900 40840 94320 113800 25670 74160 60860 2184 597700 92100 16830 637000 4028 45880
2020-08-31 418100 2332 117600 3900000 411600 617800 42000 94930 114800 25800 74500 62130 2253 600900 92870 17230 644300 4100 46800
2020-09-01 427500 2427 118700 3946000 412900 626600 43180 95520 115700 25930 75540 63420 2327 605700 93620 17820 651500 4173 47710
2020-09-02 436900 2511 119800 3992000 414100 635400 44400 96120 116600 26060 76580 64780 2397 611000 94370 18420 658600 4246 48610
2020-09-03 445000 2594 120900 4034000 415800 644300 45650 96730 117500 26190 77640 66170 2470 616800 95120 18900 665800 4319 49510
2020-09-04 455700 2676 122000 4073000 417500 653200 46940 97340 118400 26320 78680 67590 2545 622100 95880 19510 673000 4393 50430
2020-09-05 464600 2759 123100 4114000 419400 662200 48260 97940 119400 26460 79420 69060 2621 627800 96640 20070 680200 4467 51350

Confirmed count average forecast Latin America (bold black line in graphs) 2020-08-30 to 2020-09-05

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-08-29 401239 2135 115354 3846153 408009 599884 39699 93732 112906 25537 73679 59645 2113 591712 91337 16474 629961 3954 44946
2020-08-30 406900 2176 116300 3862000 409500 608600 40630 93930 113600 25660 74170 60780 2183 595100 92020 16870 637700 3979 45560
2020-08-31 414700 2215 117400 3875000 411200 618500 41740 94310 114400 25800 74780 62070 2257 599100 92720 17380 645500 4024 46420
2020-09-01 423600 2260 118500 3918000 412500 628600 42890 94710 115200 25940 75740 63340 2346 603800 93440 18000 652900 4086 47300
2020-09-02 432800 2306 119800 3964000 413700 638800 44100 95130 115900 26070 76710 64680 2420 608800 94150 18670 660400 4134 48180
2020-09-03 441100 2357 121100 4007000 415400 649100 45320 95650 116700 26210 77690 66080 2508 614500 94870 19370 668200 4204 49080
2020-09-04 450500 2462 122300 4044000 417200 659700 46610 96280 117500 26360 78650 67500 2588 619900 95590 20120 676300 4281 50010
2020-09-05 459700 2549 123500 4088000 418900 670500 47920 96890 118200 26500 79550 68910 2677 625600 96340 20750 684300 4373 50940

Confirmed count scenario forecast (bold purple line in graphs) 2020-08-30 to 2020-09-07

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameVenezuela
2020-08-29 401239 2135 115354 3846153 408009 599884 39699 93732 112906 25537 73679 59645 2113 591712 91337 16474 629961 3954 44946
2020-08-30 409000 2175 116200 3873000 409800 615000 40310 94320 113900 25640 74080 60520 2186 595600 91910 17050 645400 4004 45740
2020-08-31 416700 2232 116900 3903000 411500 626000 41240 94730 114700 25740 74550 61150 2286 600000 92510 17530 651600 4052 46390
2020-09-01 427100 2279 117600 3933000 413100 639000 42130 95060 115500 25820 75010 61900 2408 604500 93080 18050 660900 4087 47240
2020-09-02 434900 2328 118200 3958000 414600 649400 42950 95380 116300 25900 75530 62640 2525 608500 93570 18660 667800 4122 48110
2020-09-03 442400 2370 118800 3982000 416200 661300 43680 95720 117000 25980 75910 62920 2637 612300 93950 19020 667800 4154 48660
2020-09-04 450500 2404 119300 4004000 417700 670700 44340 95990 117700 26070 76420 62920 2780 616100 94310 19600 668400 4193 49460
2020-09-05 457100 2433 119800 4032000 419100 680200 45110 96190 118400 26140 76730 62920 2873 619400 94580 20000 668400 4233 49990
2020-09-06 464800 2472 120200 4045000 420400 688400 45670 96430 118900 26200 76970 63420 2962 622200 94880 20390 673600 4251 50650
2020-09-07 471600 2509 120500 4053000 421800 695600 46330 96610 119500 26270 77340 63430 3077 624900 95070 20680 674100 4286 51160

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