COVID-19 short-term forecasts Confirmed 2021-04-15 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 2021-04-15

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-03-162021-03-27 -- --2021-01-182021-01-162021-04-1107-18 --2021-01-232021-02-032021-03-152021-01-2005-262021-01-07 --2021-04-092021-01-1011-22 --2021-04-05
Peak daily increment 104 106 1122 2113 75545 6766 1589 1869 837 2590 63 1356 677 16981 177 3354 9049 81 55 1399
Days since peak 180 56 133 83 30 19 87 89 4 271 82 71 31 85 324 98 6 95 144 10
Last total 2629156 9505 3753 12529 286114 13746681 1101698 2602719 227533 259639 355431 67099 208694 11527 12857 198975 43240 2295435 6778 359830 244528 1667737 9458 8678 156499 179365
Last daily increment 24999 0 9 7 823 73174 7431 16918 2190 379 4892 143 1567 153 2 761 186 4189 0 314 2367 0 41 82 4410 1271
Last week 131275 141 45 44 5465 373507 41277 98513 4989 3076 12753 1608 7399 569 41 4427 1397 23371 51 1732 12386 39218 193 355 18553 6904
Previous peak date10-21 -- -- --07-1708-0406-062021-01-1609-1407-2604-2408-05 --09-2106-0406-2809-2210-05 -- --06-2708-0208-1409-19 --09-08
Previous peak daily increment 14882 1578 45270 7349 17013 1226 1405 7778 420 77 177 795 160 22833 155 8380 89 119 1085
Low between peaks 93 19229 1343 400 -4305 20 6 305 50 4599 1490 1 23 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-16 to 2021-04-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-04-15 2629156 286114 13746681 1101698 2602719 227533 259639 355431 67099 208694 11527 198975 43240 2295435 359830 244528 1667737 8678 156499 179365
2021-04-16 2653000 286800 13840000 1109000 2618000 227500 259700 355400 67260 208700 11630 199600 43440 2299000 360400 246900 1686000 8738 160900 180600
2021-04-17 2681000 287300 13912000 1116000 2633000 227500 260000 356600 67450 208900 11710 199900 43940 2304000 360700 248800 1694000 8779 166300 182000
2021-04-18 2706000 287600 13947000 1123000 2647000 227500 260300 358300 67620 208900 11790 200300 44300 2306000 361000 250400 1702000 8825 171300 183400
2021-04-19 2730000 288300 13978000 1129000 2662000 227500 260500 358600 67770 208900 11880 200800 44590 2307000 361200 252300 1706000 8873 175800 184600
2021-04-20 2754000 289300 14059000 1134000 2676000 228500 260900 359400 67910 210100 11960 201300 44860 2312000 361500 254500 1710000 8921 180400 185800
2021-04-21 2778000 290400 14132000 1139000 2691000 228500 261400 361700 68050 211600 12040 201800 45090 2317000 361900 256400 1718000 8972 184900 187000
2021-04-22 2801000 291200 14208000 1146000 2706000 229800 261800 365200 68190 212900 12130 202400 45310 2321000 362200 258200 1724000 9023 189500 188100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-16 to 2021-04-22

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2021-04-15 2629156 286114 13746681 1101698 2602719 227533 259639 355431 67099 208694 11527 198975 43240 2295435 359830 244528 1667737 8678 156499 179365
2021-04-16 2653000 286900 13826000 1110000 2618000 229200 260000 357300 67240 209800 11630 199700 43500 2299000 360100 246600 1676000 8737 160500 180600
2021-04-17 2675000 287300 13889000 1117000 2631000 229600 260400 358300 67350 210300 11690 200100 43800 2303000 360300 248100 1684000 8765 164700 181900
2021-04-18 2697000 287700 13918000 1125000 2645000 230000 260800 359500 67460 210700 11760 200600 44100 2305000 360500 249600 1692000 8798 168700 183100
2021-04-19 2719000 288100 13946000 1131000 2659000 230400 261100 359900 67570 211100 11830 201100 44350 2308000 360600 251200 1697000 8832 172700 184100
2021-04-20 2741000 288600 14025000 1136000 2673000 231800 261400 360600 67680 211900 11900 201600 44560 2311000 360800 252900 1701000 8868 176500 185200
2021-04-21 2763000 289200 14102000 1141000 2687000 232000 261700 361900 67790 212700 11970 202100 44750 2315000 361000 254500 1707000 8905 180400 186200
2021-04-22 2785000 289600 14180000 1148000 2701000 232400 262100 363200 67910 213400 12040 202700 44960 2319000 361200 256100 1718000 8941 184700 187300

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