COVID-19 short-term forecasts Confirmed 2020-10-17 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 2020-10-17

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) -- -- --07-1708-0406-0608-1309-1407-2609-1608-0507-1809-1906-0606-2809-1910-0505-2607-1309-1608-0208-1309-19 --09-08
Peak daily increment 1578 45354 7362 11286 1251 1408 1259 420 2699 65 179 795 161 23197 145 1089 801 8364 89 125 1086
Days since peak 92 74 133 65 33 83 31 73 91 28 133 111 28 12 144 96 31 76 65 28 39
Last total 979119 5628 2775 139710 5224362 490003 952371 95514 120925 152422 31456 101028 3710 8956 87594 8195 847108 5353 124107 54015 862417 5123 5281 2501 86289
Last daily increment 13510 111 47 148 24062 1813 7017 1166 475 763 191 597 38 31 903 63 5447 0 609 533 2677 10 40 51 531
Last week 84913 550 244 1136 129383 8632 41055 8075 2448 5389 1260 3313 241 74 3513 477 29605 89 3794 4340 13046 72 238 207 3152
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
Previous peak daily increment 7756
Low between peaks -4346

Confirmed count forecast Latin America (bold red line in graphs) 2020-10-18 to 2020-10-24

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-10-17 979119 5628 2775 139710 5224362 490003 952371 95514 120925 152422 31456 101028 3710 87594 8195 847108 124107 54015 862417 5281 2501 86289
2020-10-18 993000 5726 2822 139900 5254000 492300 959300 95500 121600 153300 31600 101500 3754 88200 8287 849700 124800 54780 865100 5322 2523 86760
2020-10-19 1007000 5831 2867 140200 5262000 493600 966000 97300 121900 154200 31730 101600 3796 88790 8383 853300 125400 55520 867800 5362 2545 87230
2020-10-20 1021000 5938 2913 140400 5281000 494900 972800 98300 122100 155000 31870 102200 3839 89390 8475 857100 126000 56270 870400 5402 2567 87680
2020-10-21 1036000 6045 2958 140600 5306000 496000 979400 99700 122700 155900 32000 103000 3881 89970 8569 860700 126700 57000 873100 5442 2588 88140
2020-10-22 1050000 6155 3003 140900 5335000 497200 986100 101000 123200 156700 32130 103600 3923 90550 8663 865100 127300 57740 875700 5481 2610 88590
2020-10-23 1065000 6268 3049 141100 5358000 498700 992800 102100 123600 157600 32260 104200 3965 91140 8758 870300 127900 58480 878400 5521 2631 89040
2020-10-24 1080000 6383 3095 141400 5386000 500400 999500 103300 124000 158400 32390 104900 4007 91730 8853 876000 128500 59230 881100 5560 2652 89500

Confirmed count average forecast Latin America (bold black line in graphs) 2020-10-18 to 2020-10-24

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-10-17 979119 5628 2775 139710 5224362 490003 952371 95514 120925 152422 31456 101028 3710 87594 8195 847108 124107 54015 862417 5281 2501 86289
2020-10-18 994000 5713 2828 139900 5233000 491600 960000 96100 121400 153100 31670 101400 3752 88270 8296 851100 124700 54730 864700 5322 2535 86770
2020-10-19 1007000 5790 2877 140100 5240000 492900 967000 97500 121800 153700 31780 101700 3794 88860 8382 855400 125300 55450 867100 5361 2559 87310
2020-10-20 1021000 5851 2921 140300 5258000 494200 973000 98600 122200 154300 31940 102200 3836 89450 8472 860000 125800 56220 869400 5401 2583 87850
2020-10-21 1035000 5948 2972 140500 5282000 495200 980000 99800 122700 154900 32080 102800 3879 90040 8549 864400 126400 56960 871700 5441 2607 88400
2020-10-22 1049000 6051 3021 140700 5315000 496500 987000 100900 123200 155500 32240 103400 3922 90630 8622 869600 127000 57760 874000 5482 2631 88960
2020-10-23 1064000 6125 3075 141000 5340000 498200 994000 102000 123700 156100 32350 103900 3967 91230 8694 874700 127500 58580 876300 5524 2655 89530
2020-10-24 1078000 6289 3127 141200 5372000 500100 1002000 103200 124200 156700 32450 104500 4011 91830 8799 880600 128100 59350 878500 5566 2679 90100

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