COVID-19 short-term forecasts Confirmed 2020-10-11 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:
    [2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.

Peak increase in estimated trend of Confirmed in Latin America 2020-10-11

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) -- -- --07-1708-0406-0608-13 --07-2609-2908-0507-18 --06-0606-2809-2410-0505-2607-1309-1108-0208-1309-1107-2109-08
Peak daily increment 1578 45354 7362 11283 1408 1240 420 2699 179 795 158 23408 145 1089 800 8364 89 117 18 1109
Days since peak 86 68 127 59 77 12 67 85 127 105 17 6 138 90 30 70 59 30 82 33
Last total 894206 5078 2531 138574 5094979 481371 911316 87439 118477 147033 30196 97715 3469 8882 84081 7718 817503 5264 120313 49675 849371 5051 5043 2294 83137
Last daily increment 10324 55 104 111 39091 1776 8569 0 463 205 245 171 64 22 935 159 3175 0 647 697 3283 16 0 26 684
Last week 84478 626 335 1467 167744 9625 49158 6310 3423 5694 657 3533 281 55 4061 706 27723 94 4394 4960 21202 97 276 139 4020
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- -- -- -- -- -- -- -- -- -- -- --03-24 --
Previous peak daily increment 7756 28
Low between peaks -4346 -1

Confirmed count forecast Latin America (bold red line in graphs) 2020-10-12 to 2020-10-18

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-10-11 894206 5078 2531 138574 5094979 481371 911316 87439 118477 147033 30196 97715 3469 84081 7718 817503 120313 49675 849371 5043 83137
2020-10-12 907800 5121 2585 138900 5111000 483200 913800 89070 119000 147900 30300 98200 3522 84670 7845 819900 121000 50430 853000 5059 83860
2020-10-13 921800 5226 2650 139300 5147000 484600 920900 89940 119400 148800 30410 98800 3574 85240 7964 822400 121600 51170 856500 5077 84560
2020-10-14 935900 5422 2715 139700 5171000 485800 928100 91170 119900 149800 30530 99600 3626 85820 8088 824900 122300 51910 860100 5095 85260
2020-10-15 950100 5451 2781 140000 5203000 487300 935400 92330 120400 150700 30640 100200 3678 86380 8206 827400 122900 52650 863500 5112 85950
2020-10-16 964600 5481 2850 140400 5224000 489000 942400 93400 120800 151600 30750 100700 3731 86940 8325 830800 123500 53390 866900 5130 86650
2020-10-17 979200 5726 2921 140800 5238000 490800 950300 94660 121300 152500 30860 101300 3783 87500 8444 832600 124200 54140 870300 5149 87350
2020-10-18 994000 5796 2994 141200 5265000 492400 957800 94660 121700 153500 30970 101600 3837 88070 8565 835300 124800 54890 873800 5167 88050

Confirmed count average forecast Latin America (bold black line in graphs) 2020-10-12 to 2020-10-18

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-10-11 894206 5078 2531 138574 5094979 481371 911316 87439 118477 147033 30196 97715 3469 84081 7718 817503 120313 49675 849371 5043 83137
2020-10-12 906800 5169 2602 138800 5107000 482900 918500 89030 119000 147600 30310 98000 3524 84790 7847 823600 121000 50400 853200 5069 83850
2020-10-13 920100 5253 2654 139100 5141000 484300 925200 90080 119400 148300 30420 98700 3581 85380 7948 827900 121600 51170 856300 5109 84570
2020-10-14 933700 5395 2731 139500 5166000 485400 931800 91300 119900 149000 30520 99400 3639 85970 8033 832600 122200 51940 859400 5151 85290
2020-10-15 947400 5451 2790 139800 5197000 487000 938600 92490 120400 149700 30630 100000 3698 86560 8132 837300 122800 52750 862400 5192 86030
2020-10-16 961300 5509 2864 140100 5217000 488700 945300 93650 120800 150400 30730 100600 3757 87150 8230 842000 123300 53570 865500 5235 86770
2020-10-17 975500 5648 2913 140500 5244000 490500 952300 94900 121300 151100 30840 101200 3818 87740 8342 846900 123900 54340 868500 5279 87530
2020-10-18 989900 5735 2981 140800 5264000 492200 958600 95530 121700 151800 30940 101700 3879 88340 8440 851500 124500 55130 871600 5322 88290

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

[2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.
[2020-10-10]Temporarily removed forecasts from the Chinese scenarios, while investigating possibility to use own history from the first wave.
Added information on the previous peak (if present) to the peak tables.
Local forecasts for England: now dropping last four observations.
[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