COVID-19 short-term forecasts Confirmed 2021-04-09 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 2021-04-09

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-04-052021-03-272021-01-162021-04-062021-01-182021-01-162021-01-1707-18 --2021-01-232021-02-032021-03-152021-01-2005-262021-01-072021-03-24 --2021-01-1011-22 --2021-04-05
Peak daily increment 104 106 1122 2113 76294 6756 17013 1278 1589 1869 318 2590 63 1356 651 16981 177 3354 2011 81 55 1613
Days since peak 174 50 127 77 4 13 83 3 81 83 82 265 76 65 25 79 318 92 16 89 138 4
Last total 2497881 9364 3708 12485 280649 13373174 1060421 2504206 222544 256563 342678 65491 201295 10958 12816 194548 41843 2272064 6727 358098 232142 1628519 9265 8323 137946 172461
Last daily increment 24130 25 4 0 1442 93317 9151 12125 2698 532 1059 0 1331 105 13 765 239 5045 0 394 2547 10655 11 47 7289 1088
Last week 114344 193 40 29 6182 419577 40943 67009 5780 2427 9503 311 6056 418 28 4406 1394 22869 50 2025 12749 54558 112 187 24042 8124
Previous peak date10-21 -- -- --07-1708-0406-06 --09-1407-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-2308-1409-19 --09-08
Previous peak daily increment 14882 1578 45270 7349 1226 1405 7778 420 66 177 795 160 22833 8419 89 119 1085
Low between peaks 93 19228 1343 264 400 -4305 87 6 305 50 4599 1 23 276

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-10 to 2021-04-16

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-09 2497881 280649 13373174 1060421 2504206 222544 256563 342678 65491 201295 10958 194548 41843 2272064 358098 232142 1628519 137946 172461
2021-04-10 2518000 281800 13438000 1066000 2515000 222500 257400 344100 65500 201300 11030 195200 42130 2277000 358600 233600 1636000 141300 173700
2021-04-11 2535000 282600 13467000 1072000 2525000 222900 257900 346500 65510 201400 11070 195600 42620 2279000 359000 235400 1647000 145300 175200
2021-04-12 2553000 283600 13494000 1077000 2535000 223000 258400 347500 65510 201600 11090 196100 43010 2281000 359200 236900 1657000 149400 176500
2021-04-13 2571000 285100 13573000 1082000 2545000 224800 258800 348600 65520 202500 11120 196700 43340 2285000 359600 238900 1667000 153400 177700
2021-04-14 2589000 286300 13658000 1087000 2555000 226200 259300 350400 65520 203400 11190 197200 43660 2291000 360000 241200 1676000 157500 179000
2021-04-15 2607000 287700 13737000 1094000 2565000 226200 259900 352200 65520 204700 11320 197800 43960 2296000 360400 243300 1685000 161700 180200
2021-04-16 2626000 288700 13816000 1102000 2575000 229900 260400 353700 65530 205700 11410 198300 44250 2300000 360800 245400 1694000 166000 181400

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-10 to 2021-04-16

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-04-09 2497881 280649 13373174 1060421 2504206 222544 256563 342678 65491 201295 10958 194548 41843 2272064 358098 232142 1628519 137946 172461
2021-04-10 2519000 281500 13433000 1068000 2515000 223000 257100 343700 65500 202100 11040 195100 42100 2276000 358400 234000 1636000 142500 173600
2021-04-11 2534000 281800 13471000 1075000 2524000 223300 257500 345200 65530 202300 11090 195600 42350 2278000 358600 235600 1643000 146200 174900
2021-04-12 2550000 282200 13497000 1081000 2534000 223700 257800 345900 65560 202500 11120 196000 42630 2280000 358700 237100 1649000 149800 176000
2021-04-13 2565000 283000 13579000 1086000 2543000 224500 258100 346700 65590 203100 11160 196500 42880 2284000 358900 238900 1654000 153500 177200
2021-04-14 2582000 283600 13678000 1091000 2553000 225300 258500 347800 65610 203700 11220 197000 43080 2288000 359200 240900 1661000 157400 178300
2021-04-15 2598000 284200 13772000 1099000 2563000 225500 259000 348900 65640 204400 11300 197400 43320 2292000 359500 242700 1671000 161200 179400
2021-04-16 2614000 284600 13856000 1107000 2573000 226600 259400 350000 65670 204800 11370 197900 43560 2296000 359700 244300 1678000 165200 180500

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