COVID-19 short-term forecasts Confirmed 2021-04-27 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 2021-04-27

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-03-252021-04-092021-04-24 --2021-01-182021-01-162021-04-1107-18 --2021-01-232021-02-032021-03-182021-01-2005-262021-01-072021-04-23 --2021-04-21 --2021-04-092021-04-05
Peak daily increment 104 106 1122 2113 75582 7064 18325 1589 1869 788 2590 63 1356 662 16981 177 3354 2656 98 4979 1331
Days since peak 192 68 145 95 33 18 3 99 101 16 283 94 83 40 97 336 110 4 6 18 22
Last total 2905172 10220 3828 12631 300258 14441563 1179772 2804881 243167 265481 375329 68922 223025 12826 12999 207320 45212 2333126 6898 363533 271814 1768186 10157 9947 190096 193721
Last daily increment 25495 0 3 7 1480 72140 4158 17578 1927 78 554 454 1086 72 41 413 67 3592 63 368 2461 0 76 151 2747 1223
Last week 135620 352 31 46 7197 318768 38369 103568 9669 3077 12486 915 6696 694 81 4907 875 17315 63 1855 14108 49098 366 731 17495 6976
Previous peak date10-21 -- -- --07-1708-0406-062021-01-1609-1807-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-2008-1409-18 --09-08
Previous peak daily increment 14882 1578 45270 7349 17013 1284 1405 7778 420 66 177 795 160 22833 8111 89 144 1085
Low between peaks 93 19229 1343 3454 400 -4305 72 6 305 50 4599 0 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-28 to 2021-05-04

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-27 2905172 10220 300258 14441563 1179772 2804881 243167 265481 375329 68922 223025 12826 207320 45212 2333126 363533 271814 1768186 10157 9947 190096 193721
2021-04-28 2933000 10260 301000 14534000 1187000 2822000 243600 265900 376500 69110 224800 12880 208000 45370 2339000 363900 272500 1777000 10230 10100 192900 194800
2021-04-29 2965000 10260 301800 14596000 1195000 2839000 244400 266400 378900 69260 226200 12960 208900 45650 2343000 364200 273900 1790000 10330 10230 196300 196200
2021-04-30 2994000 10260 303000 14671000 1202000 2855000 245300 266900 382400 69420 227600 13100 209600 45870 2347000 364500 275100 1800000 10420 10240 199300 197400
2021-05-01 3022000 10290 303700 14735000 1209000 2872000 246100 267300 383700 69580 228900 13210 210300 46050 2351000 364800 276700 1809000 10490 10320 202100 198500
2021-05-02 3049000 10320 304300 14768000 1216000 2888000 246800 268000 384700 69750 229000 13280 210900 46220 2353000 365000 277800 1814000 10570 10460 205000 199600
2021-05-03 3077000 10360 305400 14791000 1221000 2904000 247500 268400 385500 69910 229200 13340 211600 46380 2353000 365200 279200 1821000 10640 10520 207700 200700
2021-05-04 3104000 10400 306700 14861000 1225000 2920000 248100 268500 385900 70080 230400 13400 212200 46530 2357000 365500 281500 1823000 10710 10630 210400 201800

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-28 to 2021-05-04

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-27 2905172 10220 300258 14441563 1179772 2804881 243167 265481 375329 68922 223025 12826 207320 45212 2333126 363533 271814 1768186 10157 9947 190096 193721
2021-04-28 2927000 10280 301400 14514000 1185000 2822000 244600 265900 376800 69130 224200 12930 207800 45320 2337000 363800 273900 1772000 10220 10060 192900 194800
2021-04-29 2949000 10300 302200 14568000 1192000 2840000 245800 266400 378600 69260 225600 13040 208500 45470 2341000 364000 276100 1783000 10300 10200 195700 195800
2021-04-30 2971000 10340 303100 14639000 1199000 2858000 246900 266900 380900 69400 226900 13170 209200 45610 2344000 364200 278000 1793000 10370 10220 198500 196800
2021-05-01 2993000 10370 303800 14701000 1206000 2875000 248000 267400 382100 69530 228100 13290 209800 45770 2348000 364400 280100 1801000 10430 10300 201000 197700
2021-05-02 3014000 10410 304200 14737000 1213000 2892000 249100 268000 383000 69670 228700 13360 210400 45900 2350000 364500 281900 1807000 10490 10420 203600 198600
2021-05-03 3036000 10460 304900 14767000 1219000 2909000 250100 268400 383600 69800 229200 13450 211100 46030 2352000 364700 284000 1814000 10560 10490 206100 199500
2021-05-04 3057000 10510 305600 14841000 1224000 2926000 251200 268700 384200 69940 230400 13530 211700 46140 2356000 364800 286400 1820000 10620 10610 208600 200400

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