COVID-19 short-term forecasts Confirmed 2021-02-13 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 2021-02-13

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-172021-01-0312-032021-01-252021-01-132021-01-152021-01-1609-142021-01-162021-01-162021-01-1407-182021-01-232021-01-152021-02-03 --2021-01-1605-262021-01-072021-01-132021-02-062021-01-1111-222021-01-1309-08
Peak daily increment 11651 104 90 1122 2087 55686 4115 17075 1226 1587 1791 329 2590 53 61 1275 16494 177 3354 934 6623 81 55 875 1085
Days since peak 30 119 41 72 19 31 29 28 152 28 28 30 210 21 29 10 28 263 37 31 7 33 83 31 158
Last total 2021553 8311 2061 12145 236036 9809754 772396 2190116 199187 228895 265527 57428 167071 8207 12016 159788 18830 1988695 6347 331692 143443 1220748 8794 7642 48459 132743
Last daily increment 6057 0 114 11 938 44299 3925 4947 0 1131 2010 0 788 26 0 764 303 9741 0 707 845 8439 16 5 631 484
Last week 41206 55 352 108 8069 285114 20510 32900 2749 5497 7245 1191 3934 225 210 5220 1745 56550 48 4601 5325 34050 123 26 3148 2627
Previous peak date10-19 -- -- --07-1708-0406-06 -- --07-2604-2408-05 --09-2106-0406-2809-2310-05 -- -- --08-0208-1409-19 -- --
Previous peak daily increment 14378 1578 45271 7349 1405 7778 420 66 177 795 162 22833 8380 89 119
Low between peaks 5479 93 19229 1343 400 -4305 90 13 6 305 4599 1490 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-02-14 to 2021-02-20

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-02-13 2021553 2061 236036 9809754 772396 2190116 199187 228895 265527 57428 167071 8207 12016 159788 18830 1988695 331692 143443 1220748 48459 132743
2021-02-14 2023000 2061 237500 9863000 775700 2194000 199500 229300 267100 57760 167700 8252 12060 160600 19200 1999000 332500 143900 1226000 48980 133200
2021-02-15 2024000 2090 238900 9870000 778700 2197000 200500 229300 267300 57960 167800 8297 12110 161400 19490 2009000 332800 144600 1232000 49480 133600
2021-02-16 2029000 2153 240400 9933000 781000 2200000 200800 229300 268100 58160 168600 8341 12150 162200 19870 2019000 333400 145400 1238000 49990 134000
2021-02-17 2033000 2194 241800 9987000 783400 2203000 201200 229300 268700 58350 169100 8385 12190 163000 20080 2029000 334000 146300 1244000 50490 134400
2021-02-18 2038000 2230 243300 10038000 786600 2206000 201600 230000 270600 58540 169800 8429 12230 163800 20400 2039000 334800 147100 1250000 51000 134800
2021-02-19 2042000 2269 244800 10085000 790200 2210000 201900 230500 271800 58740 170500 8473 12270 164600 20670 2049000 335600 148000 1256000 51500 135200
2021-02-20 2049000 2351 246300 10122000 793800 2213000 202000 231800 273700 58930 171200 8517 12300 165400 20900 2059000 336300 148700 1262000 52010 135600

Confirmed count average forecast Latin America (bold black line in graphs) 2021-02-14 to 2021-02-20

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-02-13 2021553 2061 236036 9809754 772396 2190116 199187 228895 265527 57428 167071 8207 12016 159788 18830 1988695 331692 143443 1220748 48459 132743
2021-02-14 2025000 2113 236800 9870000 775900 2195000 199400 229700 266700 57620 167500 8245 12050 160600 19120 1995000 332100 144100 1228000 48990 133100
2021-02-15 2030000 2145 237800 9875000 778900 2200000 200000 230500 266900 57800 167800 8288 12090 161400 19350 2002000 332300 144800 1233000 49420 133500
2021-02-16 2037000 2181 239000 9934000 781300 2206000 200300 231200 267500 57980 168400 8331 12130 162200 19670 2011000 332800 145600 1239000 49870 133800
2021-02-17 2043000 2214 240300 9990000 783600 2211000 200700 232000 268000 58160 168900 8373 12170 163100 19870 2019000 333300 146300 1244000 50330 134200
2021-02-18 2050000 2247 241200 10041000 786800 2216000 201100 233200 269000 58350 169400 8415 12210 163900 20120 2028000 333900 147100 1249000 50800 134500
2021-02-19 2058000 2281 242600 10089000 790700 2222000 201500 234300 269800 58530 169900 8458 12250 164700 20330 2040000 334600 147800 1255000 51270 134800
2021-02-20 2065000 2321 243500 10108000 794100 2227000 201700 235700 270800 58720 170500 8500 12290 165500 20560 2049000 335200 148500 1260000 51710 135100

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