COVID-19 short-term forecasts Confirmed 2021-04-23 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 2021-04-23

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-03-242021-04-16 -- --2021-01-182021-01-162021-04-1107-18 --2021-01-232021-02-032021-03-182021-01-2005-262021-01-07 -- -- --2021-04-202021-04-092021-04-05
Peak daily increment 104 106 1122 2113 75801 7097 1589 1869 723 2590 63 1356 662 16981 177 3354 144 4939 1333
Days since peak 188 64 141 91 30 7 95 97 12 279 90 79 36 93 332 106 3 14 18
Last total 2824652 9976 3820 12599 295892 14237078 1155902 2740544 236930 263470 371306 68318 219789 12448 12944 205696 44642 2323430 6835 362358 263134 1745655 9932 9383 179537 189381
Last daily increment 27884 108 14 3 1501 69105 7582 19925 1656 535 5913 157 1644 214 0 868 140 3834 0 366 2752 11049 67 0 3646 1318
Last week 146905 342 47 61 8532 336987 38554 104468 8353 2843 13149 914 7482 686 59 5437 958 19334 57 1761 14770 48029 387 577 17137 7478
Previous peak date10-21 -- -- --07-1708-0406-062021-01-1509-1807-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-2008-1409-19 --09-08
Previous peak daily increment 14882 1578 45270 7349 18368 1284 1405 7778 420 77 177 795 160 22833 8111 89 119 1085
Low between peaks 93 19229 1343 400 -4305 67 6 305 50 4599 4 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-24 to 2021-04-30

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-23 2824652 9976 295892 14237078 1155902 2740544 236930 263470 371306 68318 219789 12448 205696 44642 2323430 362358 263134 1745655 9932 9383 179537 189381
2021-04-24 2852000 10000 295900 14333000 1164000 2764000 237300 264000 371300 68470 220000 12580 206100 44930 2330000 362700 267200 1756000 10000 9420 182500 190600
2021-04-25 2887000 10000 296800 14380000 1172000 2786000 237300 264500 372600 68580 220000 12680 206100 45480 2332000 363000 269700 1758000 10080 9459 185400 191900
2021-04-26 2918000 10010 297700 14411000 1178000 2804000 237300 264900 372600 68700 220000 12790 206100 45900 2334000 363200 272600 1766000 10150 9498 188300 193100
2021-04-27 2947000 10030 298800 14483000 1182000 2822000 237300 265100 373000 68830 220000 12890 206100 46240 2338000 363500 275600 1774000 10220 9540 191100 194200
2021-04-28 2975000 10060 299800 14562000 1187000 2839000 237300 265700 374600 68970 220500 12990 206100 46560 2342000 363800 279000 1777000 10290 9582 193900 195400
2021-04-29 3003000 10090 301000 14612000 1194000 2857000 237500 266200 377400 69110 221900 13100 206200 46850 2346000 364100 282200 1789000 10360 9625 196700 196600
2021-04-30 3031000 10130 302300 14680000 1201000 2876000 237900 266700 380900 69250 223300 13210 206500 47120 2350000 364400 284200 1800000 10420 9669 199500 197700

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-24 to 2021-04-30

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-23 2824652 9976 295892 14237078 1155902 2740544 236930 263470 371306 68318 219789 12448 205696 44642 2323430 362358 263134 1745655 9932 9383 179537 189381
2021-04-24 2851000 10020 296500 14297000 1163000 2759000 238000 264000 373700 68460 221200 12580 206600 44820 2327000 362600 265800 1757000 10000 9451 182600 190500
2021-04-25 2879000 10040 297300 14336000 1171000 2777000 238700 264500 375000 68600 222000 12680 207200 45020 2330000 362800 267500 1761000 10060 9514 185400 191500
2021-04-26 2906000 10080 298000 14365000 1177000 2794000 239400 264900 375500 68740 222700 12770 207700 45210 2332000 362900 269400 1769000 10120 9580 188000 192500
2021-04-27 2932000 10110 298800 14435000 1181000 2810000 240000 265300 376100 68880 223900 12870 208300 45350 2336000 363100 271600 1776000 10170 9648 190600 193400
2021-04-28 2959000 10140 299500 14509000 1186000 2827000 240700 265800 377300 69030 224900 12960 208900 45470 2340000 363300 273900 1780000 10230 9717 193400 194300
2021-04-29 2985000 10170 300100 14570000 1194000 2843000 241300 266300 379100 69170 225900 13060 209500 45610 2343000 363500 276300 1789000 10280 9788 196500 195300
2021-04-30 3012000 10220 300800 14651000 1201000 2860000 242000 266800 380200 69320 227300 13150 210100 45750 2347000 363700 278000 1798000 10330 9859 199400 196200

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