COVID-19 short-term forecasts Confirmed 2021-04-17 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:
    [2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.

Peak increase in estimated trend of Confirmed in Latin America 2021-04-17

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-03-16 -- --2021-04-142021-01-182021-01-162021-04-1107-18 --2021-01-232021-02-032021-03-182021-01-2005-262021-01-07 -- --2021-01-1011-222021-04-09 --
Peak daily increment 104 106 1122 2113 75279 1486 1589 1869 855 2590 63 1356 698 16981 177 3354 81 55 4941
Days since peak 182 58 135 85 32 3 89 91 6 273 84 73 30 87 326 100 97 146 8
Last total 2677747 9634 3773 12538 287360 13900091 1117348 2636076 228577 260627 358157 67404 212307 11762 12885 200259 43684 2304096 6778 360597 248364 1697626 9545 8806 162400 181903
Last daily increment 19119 0 8 0 0 67636 8037 16654 0 494 2193 155 1640 120 9 577 211 4157 0 348 3836 16563 49 128 2831 1294
Last week 145185 270 43 53 5265 418068 40849 99878 6033 2929 11340 915 9236 613 45 4479 1184 23883 51 1986 13072 49932 243 402 17758 7016
Previous peak date10-16 -- -- --07-1708-0406-062021-01-1609-1407-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-2008-1409-19 --09-08
Previous peak daily increment 14331 1578 45270 7349 17013 1226 1405 7778 420 77 177 795 160 22833 8111 89 119 1085
Low between peaks 93 19229 307 400 -4305 25 6 305 50 4599 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-18 to 2021-04-24

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-17 2677747 9634 287360 13900091 1117348 2636076 228577 260627 358157 67404 212307 11762 200259 43684 2304096 360597 248364 1697626 9545 8806 162400 181903
2021-04-18 2691000 9653 288000 13957000 1125000 2651000 229100 261400 359400 67480 212600 11840 200900 43980 2307000 361000 249800 1705000 9580 8806 162500 183300
2021-04-19 2711000 9664 288600 13992000 1131000 2663000 229100 261800 359500 67480 213200 11840 201200 44550 2308000 361200 251700 1710000 9601 8895 165500 184800
2021-04-20 2734000 9685 289700 14071000 1136000 2677000 231300 262500 360200 67480 215200 11850 201700 44980 2313000 361600 254200 1715000 9627 8991 168400 186200
2021-04-21 2755000 9709 290700 14146000 1141000 2690000 231300 263100 362500 67480 217000 11890 202200 45330 2318000 361900 256400 1723000 9656 9083 171300 187500
2021-04-22 2777000 9735 291500 14221000 1148000 2704000 231300 263600 365800 67480 218600 11940 202700 45670 2322000 362300 258700 1729000 9685 9175 174200 188800
2021-04-23 2802000 9762 292600 14298000 1156000 2718000 231400 264300 366600 67530 220700 11990 203300 45970 2326000 362600 260200 1740000 9716 9267 177200 190100
2021-04-24 2821000 9790 292800 14352000 1163000 2732000 231400 264700 368400 67580 221900 12050 203900 46260 2331000 362900 262700 1753000 9748 9358 180100 191400

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-18 to 2021-04-24

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-17 2677747 9634 287360 13900091 1117348 2636076 228577 260627 358157 67404 212307 11762 200259 43684 2304096 360597 248364 1697626 9545 8806 162400 181903
2021-04-18 2698000 9665 287900 13935000 1125000 2652000 229100 261100 359900 67540 213400 11850 200800 43940 2307000 360800 250200 1706000 9579 8849 165800 183100
2021-04-19 2719000 9692 288400 13965000 1131000 2667000 229500 261400 360200 67640 213900 11910 201300 44220 2309000 361000 251800 1711000 9599 8887 169200 184300
2021-04-20 2742000 9722 289000 14045000 1136000 2681000 230800 261700 360800 67740 214900 11970 201800 44440 2313000 361200 253600 1716000 9621 8926 172400 185400
2021-04-21 2765000 9744 289700 14120000 1141000 2696000 230900 262100 362200 67840 216000 12030 202300 44630 2316000 361400 255400 1723000 9643 8965 175800 186400
2021-04-22 2789000 9769 290100 14192000 1149000 2711000 231200 262500 364000 67940 217000 12110 202900 44830 2320000 361600 257100 1731000 9666 9004 179300 187400
2021-04-23 2813000 9804 290700 14272000 1156000 2726000 231600 262900 364600 68040 217900 12180 203500 45050 2324000 361800 258600 1740000 9690 9044 182800 188400
2021-04-24 2835000 9827 291000 14334000 1164000 2741000 231900 263200 365500 68150 218700 12250 204000 45270 2328000 362000 260200 1748000 9714 9086 186200 189300

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

[2021-01-07]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