COVID-19 short-term forecasts Deaths 2022-01-19 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 Deaths in Latin America 2022-01-19

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --2021-10-182021-10-272021-11-102021-10-192021-06-152021-10-072021-12-092021-09-202021-02-052021-12-062021-10-052021-09-042021-09-182021-11-082021-08-042021-08-312021-08-2505-262022-01-152021-09-032021-12-182021-10-072021-12-182021-11-182021-06-29
Peak daily increment 14 3 14 23 2009 166 70 34 22 141 15 61 6 8 40 17 731 6 12 130 62 9 26 2 17
Days since peak 93 84 70 92 218 104 41 121 348 44 106 137 123 72 168 141 147 603 4 138 32 104 32 62 204
Last total 118628 719 271 611 20377 622125 39431 131437 7434 4274 34232 3840 16203 1108 780 10457 2555 302112 219 7554 16866 203750 1222 3238 6258 5392
Last daily increment 208 0 0 0 86 322 4 169 9 5 0 3 12 7 0 0 4 643 1 9 22 105 1 14 5 9
Last week 820 0 2 4 274 1295 100 812 33 15 533 11 58 33 0 8 41 1200 1 68 196 495 19 104 47 22
Previous peak date --2021-07-16 --12-032021-06-12 --2021-07-032021-06-242021-06-0709-052021-07-202021-01-032021-06-29 --2021-06-292021-05-122021-06-09 -- --2021-08-022021-06-082021-07-112021-06-132021-06-062021-06-09 --
Previous peak daily increment 9 6 85 116 647 26 22 7806 26 59 6 47 5 10 129 548 8 13 53
Low between peaks 1 0 6 7 25 10 1 -36 3 27 0 20 2 1 18 26 2 6 1

Deaths count forecast Latin America (bold red line in graphs) 2022-01-20 to 2022-01-26

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorGuatemalaGuyanaJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-01-19 118628 20377 622125 39431 131437 7434 34232 16203 1108 2555 302112 7554 16866 203750 3238 6258 5392
2022-01-20 118800 20380 622100 39450 131600 7434 34250 16210 1113 2560 302500 7565 16890 203800 3258 6263 5395
2022-01-21 119000 20400 622200 39470 131700 7434 34260 16220 1118 2560 302700 7576 16900 203900 3285 6267 5396
2022-01-22 119100 20410 622200 39480 131800 7434 34280 16230 1122 2560 303200 7586 16920 203900 3307 6271 5398
2022-01-23 119200 20470 622300 39490 131900 7434 34290 16240 1127 2560 303400 7596 16930 204000 3327 6276 5400
2022-01-24 119400 20480 622400 39510 132000 7440 34300 16250 1132 2560 303600 7606 16950 204000 3346 6281 5403
2022-01-25 119500 20510 622700 39510 132100 7445 34310 16260 1136 2560 303800 7616 16970 204100 3364 6286 5406
2022-01-26 119600 20580 622900 39510 132200 7452 34320 16270 1141 2562 304100 7626 16980 204100 3381 6291 5409

Deaths count average forecast Latin America (bold black line in graphs) 2022-01-20 to 2022-01-26

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorGuatemalaGuyanaJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2022-01-19 118628 20377 622125 39431 131437 7434 34232 16203 1108 2555 302112 7554 16866 203750 3238 6258 5392
2022-01-20 118800 20400 622300 39460 131600 7440 34250 16210 1113 2561 302400 7563 16890 203800 3255 6262 5396
2022-01-21 118900 20440 622500 39480 131600 7443 34290 16220 1116 2563 302400 7571 16910 203900 3275 6267 5399
2022-01-22 119000 20450 622600 39500 131700 7444 34300 16220 1119 2565 302800 7579 16920 203900 3294 6273 5402
2022-01-23 119100 20510 622700 39510 131800 7445 34310 16230 1122 2569 302800 7587 16940 204000 3312 6282 5404
2022-01-24 119300 20530 622800 39530 131900 7451 34320 16230 1126 2571 302800 7595 16980 204000 3329 6285 5407
2022-01-25 119300 20560 623000 39530 132000 7456 34330 16240 1129 2575 302900 7603 17000 204100 3347 6289 5410
2022-01-26 119400 20610 623100 39530 132100 7461 34340 16240 1133 2578 303100 7611 17020 204100 3364 6294 5413

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 Deaths