COVID-19 short-term forecasts Confirmed 2022-04-11 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:
    [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 2022-04-11

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2022-01-132022-01-092022-01-192022-01-102022-01-142022-01-282022-02-082022-01-152022-01-252022-01-142022-01-152022-02-142022-02-082022-01-162022-01-172022-02-162022-01-132022-01-192021-09-282022-01-142022-01-172022-01-192022-01-182021-12-092022-01-202022-01-25
Peak daily increment 112477 929 826 763 10699 182433 36050 30553 6657 6246 8554 7430 3374 916 482 9050 1338 43484 137 10293 9248 47146 911 788 11003 2156
Days since peak 88 92 82 91 87 73 62 86 76 87 86 56 62 85 84 54 88 82 195 87 84 82 83 123 81 76
Last total 9054126 33360 62425 57318 903477 30161909 3514291 6088335 841343 578531 865263 162089 835425 63326 30586 421268 129050 5722933 18491 766931 648446 3551800 79241 140457 894267 521471
Last daily increment 1590 2 211 0 171 7930 2271 301 0 0 1865 0 87 0 1 0 78 0 0 0 0 260 0 125 358 0
Last week 8800 46 1833 0 728 119637 22329 1851 0 212 2942 519 3266 43 19 147 181 51789 0 1254 93 2772 0 1343 3287 441
Previous peak date2021-06-052021-07-262021-10-2612-032021-06-102021-09-182021-11-132021-06-262021-05-142021-06-052021-06-292021-11-062021-08-242021-09-1806-012021-08-162021-08-232021-08-1105-262021-06-292021-06-032021-06-052021-09-212021-06-052021-06-062021-10-05
Previous peak daily increment 25322 182 347 1122 2614 92852 2476 29569 2437 1203 1229 1526 3774 229 178 1520 747 18310 213 1107 2937 3719 478 365 3221 1476
Low between peaks 898 -11 41 2 287 2340 968 1351 -276 163 197 5 203 31 5 18 26 617 4 129 -166 60 12 170 95 69

Confirmed count forecast Latin America (bold red line in graphs) 2022-04-12 to 2022-04-18

DateArgentinaBarbadosBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaMexicoPanamaPeruTrinidad and TobagoUruguayVenezuela
2022-04-11 9054126 62425 903477 30161909 3514291 6088335 578531 865263 162089 835425 5722933 766931 3551800 140457 894267 521471
2022-04-12 9058000 62640 903600 30210000 3519000 6089000 578600 866100 162200 836400 5723000 767300 3552000 140800 894800 521600
2022-04-13 9061000 62640 903900 30250000 3525000 6089000 578700 866400 162500 837300 5725000 767600 3553000 141100 895900 521700
2022-04-14 9063000 62640 904100 30286000 3530000 6089000 578700 867200 162700 838000 5733000 767900 3554000 141300 896600 521800
2022-04-15 9064000 62650 904300 30320000 3535000 6090000 578800 867500 162900 838200 5738000 768100 3554000 141600 897300 521900
2022-04-16 9066000 62770 904500 30345000 3538000 6090000 578800 867600 163100 838700 5738000 768300 3554000 141600 898000 522000
2022-04-17 9066000 62950 904600 30356000 3542000 6090000 578900 867900 163200 838700 5738000 768500 3555000 141900 898500 522100
2022-04-18 9067000 63140 904700 30363000 3544000 6090000 578900 869200 163400 838700 5738000 768800 3555000 142100 899100 522200

Confirmed count average forecast Latin America (bold black line in graphs) 2022-04-12 to 2022-04-18

DateArgentinaBarbadosBoliviaBrazilChileColombiaDominican RepublicEcuadorEl SalvadorGuatemalaMexicoPanamaPeruTrinidad and TobagoUruguayVenezuela
2022-04-11 9054126 62425 903477 30161909 3514291 6088335 578531 865263 162089 835425 5722933 766931 3551800 140457 894267 521471
2022-04-12 9056000 62760 903600 30191000 3516000 6089000 578600 865600 162100 836400 5725000 767200 3552000 140700 894900 521500
2022-04-13 9058000 62900 903700 30222000 3519000 6089000 578700 865900 162200 837600 5729000 767400 3553000 141000 895600 521600
2022-04-14 9059000 63110 903900 30248000 3523000 6089000 578700 866500 162300 838500 5743000 767700 3553000 141100 896300 521700
2022-04-15 9061000 63170 904000 30275000 3527000 6089000 578800 866800 162400 839200 5747000 767900 3553000 141400 896800 521800
2022-04-16 9062000 63470 904100 30289000 3530000 6090000 578800 867000 162500 840000 5748000 768100 3554000 141500 897300 521900
2022-04-17 9063000 63640 904200 30297000 3533000 6090000 578900 867400 162600 840400 5749000 768400 3554000 141700 897700 521900
2022-04-18 9064000 63830 904300 30302000 3535000 6090000 578900 868600 162700 840800 5749000 768500 3554000 141900 898000 522000

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