COVID-19 short-term forecasts Confirmed 2021-01-20 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 2021-01-20

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-03 -- --06-062021-01-1709-14 --09-1612-2007-1809-2112-14 --09-2212-1105-262021-01-07 --08-0212-2211-222021-01-1409-08
Peak daily increment 14378 104 1122 7349 18005 1226 1216 247 2590 66 36 160 10409 177 3304 8380 64 55 939 1085
Days since peak 93 95 48 228 3 128 126 31 186 121 37 120 40 239 13 171 29 59 6 134
Last total 1831681 8075 11642 193745 8638249 680740 1956979 187712 198123 234315 51437 151324 7015 10963 136898 14487 1688944 6204 303777 124447 1073214 7783 7430 34294 121691
Last daily increment 12112 7 27 2655 64385 3589 17908 835 1532 1747 0 1047 65 56 830 68 20548 0 2243 1088 12647 74 15 848 574
Last week 60966 64 186 12729 313955 24028 107878 4470 10636 8313 1280 4387 270 328 4935 572 100575 52 12492 4807 32983 483 110 4305 3276
Previous peak date -- -- --07-1707-29 -- -- --07-2604-2408-05 -- --06-0607-03 --10-05 -- -- -- --08-1409-19 -- --
Previous peak daily increment 1622 48669 1405 7778 420 179 870 22834 89 119
Low between peaks -4305 90 6 4599 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-21 to 2021-01-27

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-20 1831681 193745 8638249 680740 1956979 187712 198123 234315 51437 151324 7015 10963 136898 14487 1688944 303777 124447 1073214 7783 34294 121691
2021-01-21 1844000 196100 8720000 684500 1970000 189700 199500 236000 51870 151800 7051 11010 137600 14570 1703000 309900 125300 1073000 7860 35040 122200
2021-01-22 1856000 198400 8783000 688700 1989000 190400 200800 237000 52140 152500 7084 11060 138300 14640 1715000 312300 126100 1074000 7935 35770 122700
2021-01-23 1865000 200700 8845000 692600 2007000 190400 202100 238100 52400 153400 7116 11100 138900 14720 1728000 314500 126900 1075000 8009 36490 123200
2021-01-24 1871000 203000 8871000 696200 2022000 190400 203400 238900 52650 153600 7147 11150 139500 14790 1740000 316100 127700 1077000 8082 37200 123700
2021-01-25 1879000 205300 8906000 699900 2036000 192300 204600 238900 52900 153700 7178 11190 140100 14860 1753000 317800 128500 1078000 8155 37920 124200
2021-01-26 1891000 207600 8969000 702900 2051000 193200 205900 239600 53150 155000 7209 11240 140600 14940 1765000 320100 129300 1080000 8229 38650 124600
2021-01-27 1902000 210000 9024000 706400 2068000 194100 207200 241200 53400 155900 7240 11280 141100 15010 1777000 322600 130100 1082000 8303 39380 125100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-21 to 2021-01-27

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-20 1831681 193745 8638249 680740 1956979 187712 198123 234315 51437 151324 7015 10963 136898 14487 1688944 303777 124447 1073214 7783 34294 121691
2021-01-21 1844000 196100 8715000 684800 1974000 188700 199800 235500 51670 152200 7056 11020 137700 14570 1707000 306300 125300 1077000 7861 35080 122200
2021-01-22 1854000 198000 8771000 689100 1993000 189400 201300 236300 51950 152800 7094 11070 138400 14650 1722000 308400 126200 1081000 7937 35850 122700
2021-01-23 1865000 199900 8820000 693400 2010000 189800 203000 237100 52220 153600 7131 11120 139100 14720 1736000 310500 127000 1083000 8012 36600 123100
2021-01-24 1873000 201600 8843000 697300 2027000 190100 204400 237800 52490 153900 7168 11170 139800 14790 1749000 312000 127800 1085000 8087 37380 123500
2021-01-25 1882000 203300 8869000 701100 2043000 191500 205800 238300 52770 154300 7205 11220 140400 14880 1760000 313700 128600 1087000 8163 38130 123900
2021-01-26 1894000 205000 8920000 704600 2059000 192400 207100 238900 53040 155100 7243 11270 141100 14960 1771000 316500 129500 1089000 8239 38930 124200
2021-01-27 1905000 206800 8977000 708300 2076000 193300 208400 239800 53320 155800 7280 11320 141900 15030 1783000 319700 130400 1092000 8316 39820 124600

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