COVID-19 short-term forecasts Confirmed 2021-03-23 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 2021-03-23

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-172021-02-1812-032021-01-222021-03-19 --2021-01-1609-142021-01-182021-01-162021-01-1407-182021-03-192021-01-232021-02-03 --2021-01-2005-262021-01-07 -- --2021-01-1011-22 --2021-03-11
Peak daily increment 11293 104 110 1122 2113 73911 17013 1226 1589 1869 313 2590 72 63 1356 16981 177 3354 81 55 587
Days since peak 68 157 33 110 60 4 66 190 64 66 68 248 4 59 48 62 301 75 72 121 12
Last total 2261577 8935 3574 12410 266086 12130019 942958 2347224 213438 250177 313570 63344 189067 9732 12722 184031 36231 2203041 6629 351667 198135 1481259 9074 7865 86007 152508
Last daily increment 9405 12 15 3 879 82493 4864 4946 1535 401 719 0 948 64 8 751 335 5881 47 454 2023 14933 7 4 1777 614
Last week 43152 135 103 14 4030 436181 38746 33070 2991 2813 7972 813 4133 410 53 3760 3503 27579 47 2647 12247 45661 32 58 10869 4931
Previous peak date10-19 -- -- --07-1708-0406-06 -- --07-2604-2408-05 --09-2106-0406-2809-2310-05 -- --06-2708-2008-1409-19 --09-08
Previous peak daily increment 14378 1578 45271 7349 1405 7778 420 66 177 795 162 22833 155 8111 89 119 1085
Low between peaks 5479 93 19229 400 -4305 90 13 6 305 4599 1 23 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-03-24 to 2021-03-30

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-23 2261577 266086 12130019 942958 2347224 213438 250177 313570 63344 189067 9732 184031 36231 2203041 351667 198135 1481259 86007 152508
2021-03-24 2266000 267000 12190000 943000 2352000 213400 250600 315000 63550 189600 9784 184600 36940 2210000 352200 200100 1483000 87690 153100
2021-03-25 2272000 267800 12256000 946200 2357000 213400 251300 316100 63690 190200 9786 185000 37890 2217000 352800 202200 1483000 89180 153100
2021-03-26 2279000 268600 12333000 949800 2362000 214600 251700 317600 63830 191000 9811 185500 38730 2223000 353400 204200 1495000 90750 153300
2021-03-27 2285000 269200 12399000 953600 2367000 214600 252100 318800 63970 191700 9847 186000 39500 2229000 353800 206000 1497000 92310 153700
2021-03-28 2288000 269400 12440000 956600 2372000 214600 252600 320300 64100 191800 9886 186500 40250 2231000 354100 207800 1506000 93900 154100
2021-03-29 2293000 269900 12480000 960400 2376000 214600 252800 320400 64230 192000 9931 187000 40970 2232000 354300 209500 1508000 95520 154600
2021-03-30 2302000 270700 12555000 965000 2381000 215900 253100 321000 64370 192900 9979 187600 41680 2236000 354700 211200 1519000 97160 155100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-03-24 to 2021-03-30

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-23 2261577 266086 12130019 942958 2347224 213438 250177 313570 63344 189067 9732 184031 36231 2203041 351667 198135 1481259 86007 152508
2021-03-24 2269000 266900 12215000 946900 2352000 213700 250600 315100 63470 189800 9780 184700 36900 2209000 352000 200200 1491000 87730 153000
2021-03-25 2276000 267500 12292000 953300 2356000 213900 251100 315900 63610 190400 9820 185200 37560 2214000 352300 202200 1494000 89110 153300
2021-03-26 2282000 268000 12374000 959300 2360000 214600 251600 316900 63730 191000 9870 185700 38100 2219000 352700 203900 1504000 90560 153600
2021-03-27 2289000 268400 12445000 965300 2365000 214700 251900 317600 63860 191600 9920 186200 38740 2224000 352900 205600 1507000 92030 154000
2021-03-28 2293000 268500 12490000 970400 2369000 214800 252400 318500 63990 191900 9960 186700 39500 2228000 353100 206900 1515000 93540 154400
2021-03-29 2299000 268800 12527000 976000 2374000 214900 252700 318600 64110 192200 10010 187200 40190 2230000 353300 208400 1518000 95050 154900
2021-03-30 2306000 269200 12604000 980800 2378000 215500 253000 318800 64240 192800 10060 187700 40710 2235000 353600 210200 1524000 96590 155300

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