COVID-19 short-term forecasts Confirmed 2020-10-03 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 2020-10-03

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) -- --09-3007-1708-0406-0608-1309-2107-2609-2408-0507-1809-2506-0606-2809-2907-3105-2607-13 --08-0208-1309-1807-2109-06
Peak daily increment 43 1578 45354 7363 11283 1214 1408 1311 420 2699 67 179 795 172 6738 145 1089 8364 89 110 18 1069
Days since peak 3 78 60 119 51 12 69 9 59 77 8 119 97 4 64 130 82 62 51 15 74 27
Last total 790818 4332 2080 136569 4906833 468471 848147 79182 113926 140351 29358 93748 2968 8811 78788 6795 757953 5170 114653 43452 821564 4924 4715 2122 77646
Last daily increment 11129 112 0 350 59741 1881 6615 1353 576 817 183 658 0 19 519 91 4863 0 691 768 3267 25 86 25 826
Last week 79493 494 255 2668 174524 10570 35091 7133 2540 5604 728 3656 196 71 4240 778 27636 97 4098 4768 21422 89 353 114 4955
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- -- -- -- -- -- -- --06-27 -- -- --03-24 --
Previous peak daily increment 7756 155 28
Low between peaks -4658 -1

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

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-10-03 790818 4332 2080 136569 4906833 468471 848147 79182 113926 140351 29358 93748 78788 6795 757953 114653 43452 821564 4715 77646
2020-10-04 802500 4344 2108 137000 4933000 470000 857100 79750 114400 141400 29470 94200 79350 6937 762100 115200 44460 831700 4792 78360
2020-10-05 814100 4382 2135 137400 4945000 471500 861700 81450 114900 142400 29590 94410 79890 7081 765200 115800 45440 836300 4867 79060
2020-10-06 826000 4439 2166 137800 4974000 472900 866900 82290 115300 143500 29700 95140 80430 7225 769500 116400 46420 837900 4942 79760
2020-10-07 837900 4615 2194 138200 5002000 474400 872800 83470 115800 144500 29810 95810 80950 7370 774100 117000 47400 841500 5016 80450
2020-10-08 849900 4641 2223 138600 5041000 476000 878700 84580 116200 145500 29920 96380 81480 7516 778700 117600 48390 845500 5091 81150
2020-10-09 862100 4727 2252 139000 5052000 477800 884600 85650 116700 146500 30030 97100 82000 7663 783200 118200 49410 850300 5167 81840
2020-10-10 874500 4804 2282 139400 5098000 479700 891000 86930 117100 147500 30150 97740 82530 7812 787800 118800 50440 853000 5243 82540

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

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-10-03 790818 4332 2080 136569 4906833 468471 848147 79182 113926 140351 29358 93748 78788 6795 757953 114653 43452 821564 4715 77646
2020-10-04 803200 4402 2106 136900 4920000 470300 854200 79710 114300 141200 29470 94270 79360 6931 762000 115300 44250 825300 4778 78450
2020-10-05 815400 4452 2141 137300 4931000 471800 859400 81040 114600 142000 29580 94690 79890 7078 765500 115800 45040 829700 4837 79240
2020-10-06 827600 4515 2171 137700 4961000 473100 865100 81980 115000 142800 29690 95370 80420 7241 769700 116400 45840 832600 4897 80030
2020-10-07 840000 4655 2208 138200 4986000 474600 871000 83090 115300 143500 29800 96050 80950 7371 774100 116900 46710 836300 4957 80830
2020-10-08 852600 4708 2235 138600 5027000 476100 877100 84240 115800 144300 29910 96640 81480 7549 778300 117500 47570 840500 5018 81640
2020-10-09 865500 4787 2279 139000 5050000 477800 883500 85450 116200 145000 30020 97330 82020 7700 782900 118000 48410 846400 5081 82470
2020-10-10 878500 4843 2313 139400 5088000 479600 890200 86710 116700 145800 30140 98040 82560 7871 787700 118600 49270 850100 5144 83310

Confirmed count scenario forecast (bold purple line in graphs) 2020-10-04 to 2020-10-12

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoVenezuela
2020-10-03 790818 4332 2080 136569 4906833 468471 848147 79182 113926 140351 29358 93748 78788 6795 757953 114653 43452 821564 4715 77646
2020-10-04 801800 4390 2113 136800 4917000 469900 853500 80070 114200 141500 29460 94020 79330 6967 760300 115200 43980 827200 4750 78200
2020-10-05 811500 4408 2135 137100 4937000 471600 858900 80890 114400 142100 29560 94410 79790 7071 763100 115900 44450 831200 4784 78870
2020-10-06 820300 4484 2165 137500 4949000 473300 864100 81710 114600 143000 29640 94760 80270 7214 765500 116500 45130 835000 4826 79440
2020-10-07 831900 4563 2201 137800 4959000 474600 868800 82580 114800 143400 29710 95070 80790 7329 767500 116900 45710 838100 4868 79920
2020-10-08 834300 4576 2218 138000 4973000 476000 873700 83360 114900 143700 29790 95330 81270 7386 769700 117400 46470 841500 4908 80480
2020-10-09 838400 4606 2233 138200 4986000 477500 878100 84040 115000 144200 29840 95620 81600 7428 771500 117800 46530 843700 4935 80960
2020-10-10 840600 4622 2249 138200 4991000 478400 882400 84660 115000 144400 29890 95790 81890 7491 772800 118200 46820 846400 4951 81380
2020-10-11 848300 4667 2258 138400 4997000 479300 886600 85360 115100 145100 29920 95990 82280 7586 773800 118500 47300 848000 4968 81880
2020-10-12 849700 4681 2268 138500 4999000 480200 890800 85780 115200 145600 29980 96070 82590 7662 774300 118700 47450 848200 4979 82220

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