COVID-19 short-term forecasts Confirmed 2022-03-13 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:
    [2021-04-29]The `legacy' download for areas of England is stuck at April 26, so we switched to the newer downloads. The results now include Scotland, Wales, and Northern Ireland. The map, however, only shows England.

Peak increase in estimated trend of Confirmed in Latin America 2022-03-13

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-102022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-102022-01-152022-01-172022-02-162022-01-132022-01-192021-08-192022-01-142022-01-242022-01-192022-01-182021-12-092022-01-202022-01-25
Peak daily increment 112477 997 826 759 10699 182434 37316 30553 6209 6246 8554 8389 3472 919 482 7990 1343 43484 167 10293 8637 47146 924 788 11003 2156
Days since peak 59 63 53 62 58 44 33 57 47 58 57 27 31 57 55 25 59 53 206 58 48 53 54 94 52 47
Last total 8971432 33194 57023 57034 897489 29374833 3301747 6076819 822935 576641 843760 156364 805030 63144 30461 416729 128379 5606827 18203 759636 645883 3536496 78763 132933 864376 518410
Last daily increment 1236 0 101 0 138 13809 51857 486 0 274 0 0 321 17 15 0 25 1191 0 412 115 770 9 225 1011 411
Last week 28544 18 803 158 2391 297002 99185 4163 5397 638 2185 0 12069 82 76 3030 166 40158 98 1706 1684 8458 184 2401 8493 1593
Previous peak date2021-06-052021-10-182021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-09-062021-06-052021-06-292021-11-062021-08-242021-09-152021-06-082021-08-132021-08-232021-08-1105-262021-06-292021-06-082021-06-052021-09-162021-06-052021-06-062021-10-05
Previous peak daily increment 25322 184 347 1136 2614 92852 2476 29569 2470 1203 1229 1386 3774 229 180 1515 731 18310 176 1107 2669 3719 480 365 3221 1476
Low between peaks 898 -2 41 2 287 2340 968 1351 -225 163 197 21 203 28 4 5 17 617 -35 129 -154 60 17 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-03-14 to 2022-03-20

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-13 8971432 57023 897489 29374833 3301747 6076819 822935 576641 843760 805030 416729 5606827 759636 645883 3536496 132933 864376 518410
2022-03-14 8972000 57220 897800 29453000 3320000 6078000 827900 576800 849300 807800 416800 5622000 760100 645900 3537000 133500 865700 518900
2022-03-15 8977000 57430 898400 29557000 3355000 6080000 830900 577400 850100 811800 417700 5639000 761200 646200 3539000 134000 868200 519800
2022-03-16 8980000 57620 898900 29630000 3383000 6081000 832900 577800 852400 815200 418000 5654000 761900 646500 3541000 134500 870100 520500
2022-03-17 8986000 57760 899200 29668000 3405000 6082000 834600 578000 853800 817800 418900 5666000 762400 646900 3542000 135000 871600 521000
2022-03-18 8994000 57900 899500 29737000 3427000 6083000 835900 578300 853800 819500 418900 5672000 762900 647600 3543000 135500 873100 521500
2022-03-19 8994000 58000 899800 29781000 3446000 6084000 835900 578500 853800 821100 419600 5682000 763200 647600 3543000 135800 874500 522000
2022-03-20 8995000 58110 900100 29804000 3464000 6084000 835900 578700 853800 821500 419600 5686000 763500 647700 3543000 136100 875800 522400

Confirmed count average forecast Latin America (bold black line in graphs) 2022-03-14 to 2022-03-20

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-03-13 8971432 57023 897489 29374833 3301747 6076819 822935 576641 843760 805030 416729 5606827 759636 645883 3536496 132933 864376 518410
2022-03-14 8974000 57130 897700 29395000 3321000 6077000 826500 576900 846400 805600 417200 5607000 760000 646000 3538000 133100 865600 518800
2022-03-15 8979000 57270 897900 29456000 3341000 6078000 829000 577100 848300 808600 418200 5614000 760600 646200 3539000 133500 866900 519200
2022-03-16 8984000 57410 898200 29507000 3358000 6078000 830800 577300 850300 811400 418800 5625000 761000 646600 3540000 133900 868200 519500
2022-03-17 8989000 57530 898400 29547000 3377000 6079000 832300 577500 852100 813600 419700 5634000 761300 647000 3541000 134300 869500 519800
2022-03-18 8996000 57650 898600 29608000 3398000 6080000 834000 577700 853200 815600 420600 5638000 761600 647400 3541000 134800 870700 520100
2022-03-19 8999000 57780 898800 29668000 3415000 6080000 834500 577800 854300 817900 421000 5652000 761700 647600 3542000 135200 871800 520400
2022-03-20 8999000 57860 899000 29688000 3439000 6081000 835300 578000 855400 818600 421300 5657000 762000 647800 3542000 135600 872700 520700

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

[2021-04-29]The `legacy' download for areas of England is stuck at April 26, so we switched to the newer downloads. The results now include Scotland, Wales, and Northern Ireland. The map, however, only shows England.
[2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.
[2020-10-27]Statistical short-term forecasting of the COVID-19 Pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now published at the Journal of Clinical Immunology and Immunotherapy. open access
[2020-10-11]Short-term forecasting of the coronavirus pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now in press at the International Journal of Forecasting. open access
[2020-10-10]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