COVID-19 short-term forecasts Confirmed 2020-11-19 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 2020-11-19

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1711-1507-1708-0406-0608-1309-1407-2609-2308-0507-1809-2111-0406-2809-2210-0505-26 --09-0708-0208-1309-19 --09-08
Peak daily increment 14938 112 91 1578 45353 7362 11286 1225 1408 1225 420 2699 66 15 795 160 23279 145 800 8364 89 119 1086
Days since peak 31 33 4 125 107 166 98 66 116 57 106 124 59 15 144 58 45 177 73 109 98 61 72
Last total 1349434 7348 5018 143756 5981767 536012 1225490 128231 136183 183246 37109 117066 4976 9208 103488 10088 1019543 5725 151089 74495 939931 5284 6233 4377 98665
Last daily increment 10097 25 60 187 35918 1454 7487 1219 1026 996 144 685 62 17 0 69 4472 0 1256 856 0 2 53 81 315
Last week 53056 185 303 687 171115 7982 42793 6108 3629 4572 914 2943 252 40 1409 365 22150 64 6612 4103 9694 16 253 494 2224
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- --06-06 -- -- -- -- -- -- -- -- -- -- --
Previous peak daily increment 7756 179
Low between peaks -4346 7

Confirmed count forecast Latin America (bold red line in graphs) 2020-11-20 to 2020-11-26

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-11-19 1349434 5018 143756 5981767 536012 1225490 128231 136183 183246 37109 117066 4976 103488 10088 1019543 151089 74495 939931 6233 4377 98665
2020-11-20 1364000 5094 143900 5989000 537500 1233000 129100 136600 184100 37380 117500 5016 103800 10140 1024000 152700 74770 941700 6264 4448 99000
2020-11-21 1371000 5171 144000 6022000 539000 1241000 130100 137100 184800 37530 118100 5055 104100 10190 1028000 153800 75380 942900 6292 4516 99300
2020-11-22 1375000 5245 144100 6036000 540500 1248000 130100 137500 185600 37600 118300 5093 104400 10240 1032000 155100 75920 944000 6320 4583 99600
2020-11-23 1383000 5321 144200 6047000 541700 1255000 131400 138000 186300 37790 118400 5131 104600 10290 1035000 156200 76370 945100 6347 4649 100000
2020-11-24 1393000 5397 144300 6075000 542600 1262000 132300 138400 187100 37790 119100 5169 104900 10340 1038000 157300 77000 946200 6373 4714 100300
2020-11-25 1402000 5474 144400 6110000 543400 1270000 133600 138900 187800 37950 119800 5207 105200 10390 1043000 158400 77710 947300 6400 4779 100600
2020-11-26 1412000 5551 144500 6138000 544800 1277000 134700 139300 188600 38350 120500 5246 105500 10440 1047000 159500 78420 948500 6427 4844 100900

Confirmed count average forecast Latin America (bold black line in graphs) 2020-11-20 to 2020-11-26

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruTrinidad and TobagoUruguayVenezuela
2020-11-19 1349434 5018 143756 5981767 536012 1225490 128231 136183 183246 37109 117066 4976 103488 10088 1019543 151089 74495 939931 6233 4377 98665
2020-11-20 1360000 5082 143900 6017000 537600 1233000 129400 136900 184000 37240 117600 5019 103600 10150 1023000 152200 75210 941200 6273 4460 99000
2020-11-21 1368000 5164 143900 6049000 539100 1240000 130300 137400 184500 37390 118200 5061 103900 10200 1028000 153000 75790 942900 6303 4536 99300
2020-11-22 1374000 5247 144000 6062000 540500 1247000 130700 137900 185000 37520 118500 5105 104100 10260 1032000 153900 76330 944500 6334 4610 99600
2020-11-23 1382000 5322 144100 6074000 541700 1254000 131700 138400 185500 37700 118700 5148 104400 10310 1035000 154600 76850 946100 6364 4683 99900
2020-11-24 1391000 5408 144100 6097000 542600 1261000 132600 138800 186000 37800 119300 5192 104700 10350 1039000 155300 77440 947700 6395 4759 100300
2020-11-25 1400000 5513 144200 6129000 543400 1268000 133600 139200 186500 37930 119800 5236 104900 10380 1044000 156100 78060 949300 6426 4831 100600
2020-11-26 1409000 5597 144300 6152000 544900 1276000 134500 139600 187000 38310 120300 5281 105200 10440 1049000 156700 78580 950900 6458 4911 100900

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