COVID-19 short-term forecasts Confirmed 2021-03-08 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 2021-03-08

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-172021-01-0312-032021-01-22 --2021-01-222021-01-1609-142021-01-182021-01-162021-01-1407-182021-01-232021-01-232021-02-03 --2021-01-2005-262021-01-07 --2021-02-202021-01-1011-22 --09-08
Peak daily increment 11293 104 90 1122 2113 4180 17013 1226 1589 1869 313 2590 55 63 1366 16981 177 3354 6898 81 55 1085
Days since peak 53 142 64 95 45 45 51 175 49 51 53 233 44 44 33 47 286 60 16 57 106 181
Last total 2154694 8600 3303 12335 254736 11051665 860533 2278861 206640 243526 294618 60800 178770 8814 12536 174243 26904 2130477 6489 345236 169860 1371176 8980 7736 64700 142774
Last daily increment 5058 0 18 0 463 32321 4748 2205 0 279 115 0 210 7 0 514 878 1877 0 402 1817 6212 3 0 863 436
Last week 36018 81 163 15 4179 404739 28021 19262 1126 3325 7893 309 3359 188 43 2485 3066 33283 0 3217 8330 32879 41 19 5529 2840
Previous peak date10-19 -- -- --07-1708-0406-06 -- --07-2604-2408-05 --09-2106-0406-2809-2310-05 -- --06-2708-0208-1409-19 -- --
Previous peak daily increment 14378 1578 45272 7349 1405 7778 420 66 177 795 162 22833 155 8380 89 119
Low between peaks 5479 93 1343 400 -4305 90 13 6 305 4599 1490 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-03-09 to 2021-03-15

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-08 2154694 254736 11051665 860533 2278861 206640 243526 294618 60800 178770 174243 26904 2130477 345236 169860 1371176 64700 142774
2021-03-09 2160000 256000 11066000 864000 2282000 207500 244000 295700 61450 179300 174800 26950 2140000 346000 171300 1380000 65620 143200
2021-03-10 2167000 257100 11111000 867100 2287000 208000 245100 298000 61460 180000 175800 27160 2150000 346800 172500 1383000 66530 143500
2021-03-11 2174000 258000 11166000 871800 2291000 208400 246000 299000 61720 180500 176600 27500 2159000 347400 173700 1390000 67420 143900
2021-03-12 2181000 259000 11224000 876500 2295000 208800 246700 300200 61880 181200 177200 27890 2166000 347900 174900 1398000 68290 144300
2021-03-13 2185000 259700 11277000 880500 2299000 208800 247300 301900 61880 181800 177900 28270 2173000 348600 176000 1405000 69160 144700
2021-03-14 2188000 259900 11336000 885100 2302000 208800 247900 303400 61880 181900 178400 28950 2175000 348900 177200 1412000 70030 145100
2021-03-15 2192000 260300 11363000 889400 2305000 209000 248400 303500 61940 182100 179000 29550 2176000 349300 178300 1416000 70900 145500

Confirmed count average forecast Latin America (bold black line in graphs) 2021-03-09 to 2021-03-15

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-08 2154694 254736 11051665 860533 2278861 206640 243526 294618 60800 178770 174243 26904 2130477 345236 169860 1371176 64700 142774
2021-03-09 2160000 255400 11119000 864000 2282000 206900 243800 295200 60950 179300 174800 27350 2137000 345700 171100 1377000 65520 143200
2021-03-10 2167000 256100 11181000 866900 2285000 207200 244400 296800 61070 179900 175500 27570 2144000 346200 172200 1380000 66330 143500
2021-03-11 2173000 256700 11248000 871500 2289000 207500 244900 297500 61230 180400 176100 27850 2151000 346500 173400 1385000 67140 143800
2021-03-12 2179000 257300 11312000 876400 2292000 207700 245500 298400 61380 181000 176600 28130 2157000 346800 174600 1392000 67970 144100
2021-03-13 2184000 257800 11370000 880600 2295000 207800 246100 299600 61480 181500 177100 28440 2163000 347300 175600 1399000 68810 144400
2021-03-14 2189000 258100 11416000 884100 2298000 207900 246600 300600 61570 181800 177600 28860 2167000 347500 176500 1404000 69620 144700
2021-03-15 2194000 258500 11441000 888600 2302000 208300 247100 301000 61720 182000 178000 29210 2170000 347800 177500 1408000 70430 145000

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