COVID-19 short-term forecasts Confirmed 2021-03-15 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 2021-03-15

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-172021-02-1812-032021-01-222021-03-122021-01-222021-01-1609-142021-01-182021-01-162021-01-1407-182021-03-122021-01-232021-02-03 --2021-01-2005-262021-01-07 --2021-02-112021-01-1011-22 --09-08
Peak daily increment 11293 104 97 1122 2113 71728 4180 17013 1226 1589 1869 313 2590 69 63 1356 16981 177 3354 7099 81 55 1085
Days since peak 60 149 25 102 52 3 52 58 182 56 58 60 240 3 51 40 54 293 67 32 64 113 188
Last total 2201886 8765 3442 12383 260059 11519609 896231 2305884 209093 246299 302498 62377 183014 9187 12632 178925 31305 2167729 6537 348155 181414 1412406 9028 7788 72862 146488
Last daily increment 6164 107 21 13 670 36239 5121 2740 0 254 277 291 133 27 0 648 806 1439 0 236 1400 4443 4 5 1171 1109
Last week 39885 123 109 38 4438 397180 32167 23512 1261 2521 7384 838 3451 340 96 4417 3840 29845 0 2396 9429 32383 43 52 7335 3167
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) 2021-03-16 to 2021-03-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-15 2201886 260059 11519609 896231 2305884 209093 246299 302498 62377 183014 9187 178925 31305 2167729 348155 181414 1412406 72862 146488
2021-03-16 2207000 261100 11568000 900200 2309000 210000 247000 303900 62500 183600 9204 179500 32180 2177000 349100 183600 1422000 73890 147200
2021-03-17 2214000 262100 11625000 903900 2313000 210100 247700 306100 62660 184200 9204 180100 33190 2185000 349900 185400 1423000 74680 147800
2021-03-18 2221000 263000 11687000 909300 2316000 210200 248500 307400 62820 184800 9204 180700 34120 2192000 350500 187100 1431000 75570 148400
2021-03-19 2228000 264000 11756000 914200 2320000 211100 249000 308600 62970 185500 9204 181200 34960 2198000 350900 188900 1438000 76480 149000
2021-03-20 2233000 264700 11814000 918500 2323000 211100 249400 310200 63120 186100 9208 181800 35800 2204000 351500 190400 1446000 77410 149600
2021-03-21 2236000 265000 11858000 923800 2326000 211100 249800 311600 63270 186300 9224 182300 36610 2205000 351700 191400 1451000 78370 150300
2021-03-22 2241000 265500 11887000 928400 2329000 211200 250100 311600 63420 186400 9241 182900 37430 2207000 352000 192800 1455000 79350 150900

Confirmed count average forecast Latin America (bold black line in graphs) 2021-03-16 to 2021-03-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-15 2201886 260059 11519609 896231 2305884 209093 246299 302498 62377 183014 9187 178925 31305 2167729 348155 181414 1412406 72862 146488
2021-03-16 2208000 260800 11578000 900400 2309000 209600 246600 303100 62720 183500 9222 179500 31810 2173000 348500 183000 1419000 73960 147000
2021-03-17 2215000 261500 11646000 904000 2312000 209800 247000 304400 62850 184100 9261 179900 32430 2180000 348900 184600 1421000 74910 147300
2021-03-18 2221000 262000 11713000 909300 2315000 209900 247600 305200 62990 184600 9299 180400 32990 2186000 349200 186300 1427000 75910 147600
2021-03-19 2228000 262700 11785000 914800 2318000 210400 248000 306000 63120 185200 9338 180900 33400 2191000 349500 187900 1433000 76940 147900
2021-03-20 2233000 263200 11847000 919500 2322000 210600 248400 307000 63240 185700 9378 181300 33910 2197000 349800 189300 1439000 77960 148200
2021-03-21 2238000 263400 11897000 924100 2325000 210700 248900 308000 63330 186000 9418 181800 34600 2201000 350100 190700 1443000 79000 148500
2021-03-22 2243000 263800 11931000 929000 2328000 210800 249400 308300 63440 186200 9459 182300 35310 2204000 350300 192200 1448000 80030 148800

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