COVID-19 short-term forecasts Confirmed 2021-01-07 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 2021-01-07

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-0307-1712-1006-06 --09-1407-2609-2312-2407-1809-2112-2706-2809-2212-1005-26 --12-1008-0212-2211-22 --09-08
Peak daily increment 14377 104 1113 1578 46316 7361 1225 1408 1225 247 2699 66 47 795 160 10651 145 858 8364 62 55 1086
Days since peak 80 82 35 174 28 215 115 165 106 14 173 108 11 193 107 28 226 28 158 16 46 121
Last total 1690006 7959 11152 168891 7961673 629176 1737347 176407 177493 218385 47948 141074 6469 10203 125118 13368 1493569 6097 269091 112856 1022018 6750 7210 23807 115322
Last daily increment 13835 14 44 1910 87843 3693 17576 1369 1115 1008 593 872 62 76 219 38 13734 0 4135 1162 0 84 14 759 414
Last week 60412 72 376 7906 261095 16612 82467 7086 5275 5007 1988 2838 121 188 2144 453 56384 51 19358 4507 6881 473 52 4054 1438
Previous peak date -- -- -- --08-04 -- -- -- --04-2408-05 -- --06-06 -- --10-05 -- -- -- --08-1309-19 -- --
Previous peak daily increment 45352 7756 420 179 23279 89 119
Low between peaks 19228 -4346 90 6 4836 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-08 to 2021-01-14

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-07 1690006 11152 168891 7961673 629176 1737347 176407 177493 218385 47948 141074 125118 13368 1493569 269091 112856 1022018 6750 23807 115322
2021-01-08 1690000 11190 170200 7982000 630200 1750000 176400 178700 218500 47950 141300 125500 13430 1503000 272800 113500 1025000 6818 24610 115600
2021-01-09 1696000 11230 171400 8000000 633200 1761000 176500 179500 219500 48090 141400 126000 13490 1509000 276400 113800 1026000 6884 25380 115900
2021-01-10 1702000 11270 172300 8016000 635400 1772000 176500 180100 219700 48640 141600 126400 13550 1514000 280100 114100 1028000 6951 26160 116100
2021-01-11 1710000 11310 173400 8037000 637600 1781000 179300 181500 220000 49030 141800 126800 13610 1520000 283600 114900 1029000 7015 26910 116400
2021-01-12 1721000 11340 174700 8093000 639500 1791000 180300 182100 220700 49190 142600 127200 13660 1531000 287200 115800 1031000 7080 27680 116600
2021-01-13 1734000 11380 176300 8151000 641900 1799000 181600 182700 222000 49310 143400 127600 13720 1543000 290900 116500 1032000 7146 28460 116900
2021-01-14 1744000 11410 178000 8223000 644800 1808000 182900 183800 222900 49760 144200 128100 13780 1556000 294600 117500 1034000 7212 29260 117100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-08 to 2021-01-14

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-07 1690006 11152 168891 7961673 629176 1737347 176407 177493 218385 47948 141074 125118 13368 1493569 269091 112856 1022018 6750 23807 115322
2021-01-08 1700000 11210 170200 7994000 632400 1752000 177200 178600 219100 48100 141500 125600 13430 1504000 272500 113500 1023000 6830 24520 115600
2021-01-09 1706000 11260 171200 8012000 635400 1765000 177600 179500 219500 48280 141800 126000 13480 1512000 275000 114000 1024000 6899 25120 115800
2021-01-10 1713000 11320 171900 8025000 637500 1776000 178000 180200 219800 48650 142000 126400 13550 1518000 277400 114400 1025000 6968 25730 116000
2021-01-11 1721000 11370 172900 8043000 639700 1788000 179900 181400 220100 48960 142300 126800 13620 1525000 279600 115000 1027000 7037 26360 116300
2021-01-12 1730000 11430 174000 8102000 641500 1800000 180700 182200 220400 49150 142800 127300 13660 1536000 282400 115800 1028000 7107 27020 116500
2021-01-13 1739000 11480 175200 8159000 643700 1813000 181700 183000 220800 49380 143400 127700 13710 1547000 285400 116500 1029000 7177 27670 116700
2021-01-14 1747000 11540 176600 8222000 646000 1825000 182600 184100 221200 49640 143900 128100 13750 1557000 288400 117200 1031000 7248 28350 116900

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