COVID-19 short-term forecasts Confirmed 2021-01-04 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 2021-01-04

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-0307-1712-1606-0612-2909-1407-2609-2312-2407-1809-2112-2706-2809-2212-1005-2612-3012-0408-0212-2211-22 --09-08
Peak daily increment 14377 104 1094 1578 45254 7361 12952 1225 1408 1225 247 2699 66 40 795 160 10347 145 3783 853 8364 64 55 1086
Days since peak 77 79 32 171 19 212 6 112 162 103 11 170 105 8 190 104 25 223 5 31 155 13 43 118
Last total 1648940 7924 10938 163671 7753752 620641 1686131 172436 175374 215080 47087 138656 6379 10127 123398 13203 1455219 6046 256230 109837 1019475 6493 7178 21426 114407
Last daily increment 8222 10 37 1010 20006 2450 10311 3115 2043 466 284 181 21 0 29 154 6464 0 2494 764 1376 100 10 603 177
Last week 46777 78 270 6784 190201 16655 71309 5637 7109 4754 1672 2369 78 169 2486 451 53690 0 17951 3701 10567 395 51 3464 1546
Previous peak date -- -- -- --08-04 -- -- -- --04-2408-05 -- --06-06 -- --10-05 -- -- -- --08-1309-19 -- --
Previous peak daily increment 45353 7756 420 179 23279 89 119
Low between peaks 19229 -4346 90 6 4836 -1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-01-05 to 2021-01-11

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-04 1648940 10938 163671 7753752 620641 1686131 172436 175374 215080 47087 138656 123398 13203 1455219 256230 109837 1019475 6493 21426 114407
2021-01-05 1654000 11000 164300 7822000 620900 1696000 172900 175500 215700 47180 139400 123800 13270 1468000 259100 111000 1021000 6541 22160 114700
2021-01-06 1664000 11050 165300 7869000 623500 1706000 174100 176500 216300 47560 140100 124200 13340 1479000 261800 111700 1023000 6602 22880 115000
2021-01-07 1673000 11110 166500 7920000 625800 1715000 175200 177700 216900 47730 140800 124600 13400 1490000 264500 112500 1025000 6663 23610 115300
2021-01-08 1678000 11170 167200 7943000 628500 1724000 175300 178900 217500 47730 141000 124900 13460 1500000 267200 113000 1026000 6722 24340 115600
2021-01-09 1682000 11220 167900 7961000 631200 1734000 175300 179600 218000 47850 141100 125300 13520 1505000 269900 113400 1028000 6782 25090 115900
2021-01-10 1688000 11280 168300 7972000 633400 1743000 175300 180300 218600 48390 141200 125700 13580 1510000 272600 113700 1029000 6841 25870 116200
2021-01-11 1695000 11330 169200 7992000 635400 1753000 178200 181700 219200 48780 141300 126100 13630 1516000 275300 114400 1031000 6901 26660 116500

Confirmed count average forecast Latin America (bold black line in graphs) 2021-01-05 to 2021-01-11

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2021-01-04 1648940 10938 163671 7753752 620641 1686131 172436 175374 215080 47087 138656 123398 13203 1455219 256230 109837 1019475 6493 21426 114407
2021-01-05 1658000 10980 164700 7805000 622700 1697000 173400 176400 215500 47260 139200 123700 13280 1465000 259200 110500 1021000 6562 22040 114600
2021-01-06 1666000 11030 165700 7856000 625200 1708000 174300 177400 216000 47540 139700 124100 13330 1475000 262100 111100 1022000 6623 22630 114800
2021-01-07 1674000 11080 166700 7910000 627500 1720000 175200 178400 216400 47740 140200 124600 13380 1485000 265000 111800 1024000 6684 23250 115100
2021-01-08 1680000 11120 167500 7935000 630300 1731000 175600 179500 216800 47850 140500 125000 13430 1495000 267700 112400 1025000 6745 23870 115300
2021-01-09 1686000 11160 168300 7958000 632800 1743000 176000 180300 217100 48040 140700 125400 13480 1502000 270200 112900 1027000 6806 24490 115500
2021-01-10 1691000 11210 169000 7976000 634600 1754000 176300 181200 217300 48320 140900 125800 13550 1509000 272500 113400 1028000 6869 25130 115700
2021-01-11 1698000 11260 169800 7994000 636500 1766000 177900 182100 217500 48680 141200 126200 13590 1516000 274800 114100 1029000 6931 25800 115900

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