COVID-19 short-term forecasts Confirmed 2021-01-02 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 2021-01-02

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-0307-1712-1606-06 --09-1407-2609-2312-2407-1809-2112-1406-2809-2212-1005-2612-3012-0308-0212-2211-22 --09-08
Peak daily increment 14377 104 1087 1578 45467 7362 1225 1408 1225 249 2699 66 32 795 160 10339 145 4197 849 8364 68 55 1086
Days since peak 75 77 30 169 17 210 110 160 101 9 168 103 19 188 102 23 221 3 30 153 11 41 116
Last total 1634834 7887 10807 162055 7716405 615902 1666408 169321 172965 214513 45960 138316 6351 10077 123144 12931 1443544 6046 251764 108718 1015137 6343 7162 20275 114083
Last daily increment 5240 0 31 1070 15827 3338 11528 0 747 1135 0 80 3 62 170 16 6359 0 2031 369 0 66 4 522 199
Last week 51537 53 246 7212 232120 15797 71911 6331 6201 5158 1341 3007 58 130 4047 208 60110 55 20407 4296 9591 365 50 3547 1767
Previous peak date -- -- -- --08-04 -- -- -- --04-2408-05 -- --06-06 -- --10-05 -- -- -- --08-1309-19 -- --
Previous peak daily increment 45352 7756 420 179 23279 89 119
Low between peaks 19228 -4346 90 6 4836 0 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-03 to 2021-01-09

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-02 1634834 10807 162055 7716405 615902 1666408 169321 172965 214513 45960 138316 123144 12931 1443544 251764 108718 1015137 6343 20275 114083
2021-01-03 1635000 10870 162600 7764000 616200 1679000 169400 173600 215200 46320 138700 123500 12950 1453000 252100 109400 1017000 6398 20640 114400
2021-01-04 1642000 10930 163400 7781000 618300 1691000 171900 174300 215800 46910 138800 123900 12960 1457000 253100 110100 1019000 6451 21160 114700
2021-01-05 1652000 11000 164600 7831000 620200 1703000 172800 175100 216400 46920 139600 124300 12980 1469000 255500 110800 1021000 6504 21760 114900
2021-01-06 1662000 11060 165700 7882000 622500 1714000 173900 176100 217000 47260 140300 124700 13000 1480000 257200 111400 1022000 6556 22310 115200
2021-01-07 1669000 11120 167100 7936000 624600 1726000 174900 177200 217600 47410 141100 125100 13010 1492000 259600 112100 1024000 6608 22950 115500
2021-01-08 1675000 11190 167900 7960000 627500 1738000 174900 178400 218200 47410 141300 125500 13030 1502000 261000 112800 1025000 6661 23530 115800
2021-01-09 1680000 11250 168800 7977000 630200 1750000 174900 179200 218800 47520 141400 125900 13050 1508000 263400 113500 1027000 6714 24040 116100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-03 to 2021-01-09

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-02 1634834 10807 162055 7716405 615902 1666408 169321 172965 214513 45960 138316 123144 12931 1443544 251764 108718 1015137 6343 20275 114083
2021-01-03 1641000 10840 162900 7730000 618200 1679000 169600 174000 215100 46060 138500 123700 12970 1450000 254200 109100 1016000 6402 20820 114300
2021-01-04 1648000 10890 163800 7747000 620200 1691000 171300 174800 215400 46480 138700 124100 13000 1456000 257000 109800 1018000 6464 21360 114600
2021-01-05 1656000 10940 164700 7794000 622000 1703000 172100 175600 215600 46600 139200 124400 13030 1466000 260100 110500 1019000 6525 21940 114900
2021-01-06 1665000 11000 165600 7844000 624400 1715000 173000 176600 216100 46900 139800 124800 13060 1476000 263100 111200 1021000 6587 22520 115100
2021-01-07 1671000 11050 166600 7901000 626500 1727000 173900 177600 216400 47100 140300 125200 13100 1487000 266200 112000 1022000 6649 23130 115300
2021-01-08 1677000 11100 167400 7930000 628800 1739000 174400 178600 216800 47240 140600 125600 13150 1497000 269100 112700 1024000 6712 23740 115600
2021-01-09 1683000 11150 168200 7958000 631100 1751000 174900 179400 217100 47470 140800 126000 13190 1504000 272100 113300 1025000 6775 24320 115800

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