COVID-19 short-term forecasts Confirmed 2020-11-16 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:
    [2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.

Peak increase in estimated trend of Confirmed in Latin America 2020-11-16

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1610-1711-1207-1708-0406-0608-1309-1407-2609-2310-1607-1809-2106-0606-2809-2210-0505-2607-1309-0708-0208-1309-19 --09-08
Peak daily increment 14771 117 92 1578 45354 7362 11286 1225 1408 1225 180 2699 66 179 795 160 23280 145 1089 800 8364 89 119 1086
Days since peak 31 30 4 122 104 163 95 63 113 54 31 121 56 163 141 55 42 174 126 70 106 95 58 69
Last total 1318384 7256 4883 143371 5876464 532604 1205217 124592 134203 180676 36669 115032 4894 9188 103239 9929 1009396 5661 147667 72099 937011 5275 6096 4104 97739
Last daily increment 7893 70 22 125 13371 1331 6471 1369 479 381 311 147 71 20 137 45 2874 0 1014 525 2112 1 16 74 387
Last week 55908 244 469 707 177459 8697 49861 6026 3072 4965 1524 2903 364 51 2435 356 30865 0 6365 3602 13484 30 216 484 2294
Previous peak date -- -- -- -- -- -- -- -- --04-2408-05 -- -- -- -- -- -- -- -- -- -- -- -- -- --
Previous peak daily increment 7756 420
Low between peaks -4346 90

Confirmed count forecast Latin America (bold red line in graphs) 2020-11-17 to 2020-11-23

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-11-16 1318384 7256 4883 143371 5876464 532604 1205217 124592 134203 180676 36669 115032 4894 103239 9929 1009396 147667 72099 937011 6096 4104 97739
2020-11-17 1328000 7289 4969 143500 5876000 533700 1213000 125300 134400 181500 36670 115600 4936 103600 9980 1016000 148600 72640 939600 6117 4173 98100
2020-11-18 1335000 7321 5053 143600 5915000 534500 1221000 126400 134600 182300 36670 116200 4976 104000 10030 1022000 149600 73170 941800 6140 4239 98400
2020-11-19 1343000 7352 5132 143700 5940000 536000 1229000 127500 135000 183200 37480 116900 5015 104300 10080 1027000 150500 73700 943900 6162 4303 98700
2020-11-20 1352000 7384 5211 143900 5968000 537600 1237000 128600 135700 184000 37590 117500 5053 104700 10130 1032000 151400 74220 945900 6185 4365 99100
2020-11-21 1359000 7415 5291 144000 5997000 539100 1245000 129600 136300 184800 37720 118000 5092 105000 10180 1038000 152300 74740 948000 6208 4427 99400
2020-11-22 1360000 7446 5371 144100 6009000 540500 1253000 129600 136700 185700 37780 118200 5131 105400 10230 1041000 153300 75260 950100 6231 4488 99700
2020-11-23 1368000 7477 5452 144200 6020000 541800 1260000 130800 137100 186500 37960 118300 5169 105700 10280 1045000 154200 75790 952100 6254 4550 100000

Confirmed count average forecast Latin America (bold black line in graphs) 2020-11-17 to 2020-11-23

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-11-16 1318384 7256 4883 143371 5876464 532604 1205217 124592 134203 180676 36669 115032 4894 103239 9929 1009396 147667 72099 937011 6096 4104 97739
2020-11-17 1327000 7289 4963 143500 5900000 533600 1213000 125400 134700 181300 36800 115500 4945 103500 9980 1014000 148700 72650 939000 6124 4171 98100
2020-11-18 1336000 7326 5055 143500 5940000 534400 1220000 126400 135000 181900 36900 116100 4987 103900 10020 1020000 149500 73190 940900 6150 4235 98400
2020-11-19 1344000 7369 5140 143600 5964000 535900 1228000 127400 135500 182600 37380 116600 5029 104200 10070 1026000 150400 73720 942700 6177 4300 98800
2020-11-20 1353000 7397 5231 143700 5994000 537400 1236000 128400 136000 183200 37540 117100 5072 104500 10130 1031000 151200 74290 944500 6206 4363 99100
2020-11-21 1362000 7451 5322 143700 6018000 538900 1244000 129300 136500 183800 37680 117600 5115 104800 10170 1036000 151900 74840 946300 6239 4420 99500
2020-11-22 1368000 7491 5416 143800 6026000 540300 1251000 129800 136900 184400 37820 117900 5158 105100 10230 1041000 152800 75350 948100 6264 4482 99800
2020-11-23 1377000 7530 5502 143900 6033000 541500 1259000 130800 137300 185000 37950 118100 5202 105400 10270 1046000 153600 75860 949900 6289 4543 100100

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

[2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.
[2020-10-10]Temporarily 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