COVID-19 short-term forecasts Confirmed 2020-11-04 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 2020-11-04

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-2110-17 --07-1708-0406-0608-1309-1407-2609-2308-0507-1809-2106-0606-2809-2210-0505-2607-1309-0708-0208-1309-1910-3009-08
Peak daily increment 14073 96 1578 45353 7361 11286 1225 1408 1225 420 2699 66 179 795 160 22315 145 1089 800 8364 89 119 46 1086
Days since peak 14 18 110 92 151 83 51 101 42 91 109 44 151 129 43 30 162 114 58 94 83 46 5 57
Last total 1205928 6843 3905 142062 5590025 515042 1108086 113261 128278 171433 34015 109147 4324 9100 99124 9326 943630 5514 136024 65258 911787 5220 5764 3245 93480
Last daily increment 10652 53 115 126 23976 840 8694 1141 430 1323 0 664 79 43 436 30 5225 0 432 630 9284 2 10 49 380
Last week 62128 236 644 578 95649 7992 54964 5691 2365 5131 570 2357 226 43 2974 399 30819 0 3979 3208 16859 23 128 201 2200
Previous peak date -- -- -- -- -- -- -- -- --04-24 -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
Previous peak daily increment 7756
Low between peaks -4346

Confirmed count forecast Latin America (bold red line in graphs) 2020-11-05 to 2020-11-11

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2020-11-04 1205928 6843 3905 142062 5590025 515042 1108086 113261 128278 171433 34015 109147 4324 99124 9326 943630 136024 65258 911787 3245 93480
2020-11-05 1228000 6916 4085 142200 5627000 516700 1116000 114500 128700 172900 34180 109900 4355 99800 9382 951100 136600 65830 911900 3286 93980
2020-11-06 1241000 6986 4217 142300 5650000 518200 1125000 115700 129000 174000 34340 110400 4385 100400 9439 957400 137300 66400 914400 3326 94450
2020-11-07 1250000 7050 4353 142400 5669000 519700 1133000 116700 129700 175000 34480 111000 4415 101000 9493 963500 137900 66970 916700 3366 94900
2020-11-08 1258000 7115 4481 142500 5676000 521100 1141000 116700 130000 176100 34630 111200 4446 101600 9549 968000 138500 67530 919000 3405 95340
2020-11-09 1266000 7178 4610 142600 5686000 522300 1149000 117900 130300 176500 34770 111300 4476 102200 9605 971500 139100 68090 921300 3445 95790
2020-11-10 1277000 7240 4739 142800 5700000 523200 1157000 118600 130400 177100 34910 111700 4507 102800 9661 976800 139700 68660 923500 3484 96240
2020-11-11 1288000 7303 4869 142900 5724000 524000 1165000 119800 130900 178400 35050 112400 4537 103400 9717 981800 140300 69230 925800 3524 96680

Confirmed count average forecast Latin America (bold black line in graphs) 2020-11-05 to 2020-11-11

DateArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2020-11-04 1205928 6843 3905 142062 5590025 515042 1108086 113261 128278 171433 34015 109147 4324 99124 9326 943630 136024 65258 911787 3245 93480
2020-11-05 1218000 6886 3997 142200 5611000 516300 1116000 114200 128600 172400 34100 109600 4360 99600 9385 949400 136600 65810 914400 3281 93870
2020-11-06 1230000 6938 4083 142300 5634000 517800 1125000 115200 129000 173200 34250 110100 4388 100200 9440 955100 137200 66380 916300 3319 94260
2020-11-07 1240000 6990 4147 142400 5652000 519300 1134000 116200 129500 174100 34400 110600 4416 100900 9505 960600 137800 66940 918200 3357 94660
2020-11-08 1249000 7040 4217 142500 5659000 520800 1142000 116600 129800 174900 34550 110900 4445 101500 9557 965200 138300 67500 920000 3392 95060
2020-11-09 1259000 7094 4273 142500 5670000 522100 1151000 117800 130100 175700 34700 111100 4474 102100 9615 969400 138800 68040 921800 3427 95460
2020-11-10 1271000 7147 4353 142700 5691000 523100 1160000 118700 130500 176400 34850 111600 4503 102700 9662 974800 139300 68590 923700 3470 95870
2020-11-11 1283000 7197 4421 142800 5720000 524000 1169000 119800 130900 177300 34990 112200 4533 103400 9710 980200 139900 69240 925600 3511 96280

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