COVID-19 short-term forecasts Confirmed 2020-12-30 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 2020-12-30

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-0307-1712-1606-0612-2509-1407-2609-2312-1907-1809-2112-1406-2809-2212-1005-26 --12-1808-0212-2211-2212-2609-08
Peak daily increment 14377 104 1125 1578 46145 7361 13209 1225 1408 1225 248 2699 66 33 795 160 10394 145 853 8364 78 55 593 1086
Days since peak 72 74 27 166 14 207 5 107 157 98 11 165 100 16 185 99 20 218 12 150 8 38 4 113
Last total 1613928 7857 10724 158372 7619200 606950 1626461 168114 169579 211512 45960 137166 6319 9999 120912 12793 1413935 6046 242744 106958 1010496 6181 7132 18480 113121
Last daily increment 11765 11 56 1485 55649 2964 11639 1315 1314 1186 545 879 18 41 0 41 12406 0 4465 822 1588 83 5 518 260
Last week 50063 69 234 5251 195255 12798 66695 5124 5925 3502 1341 2272 53 194 2876 274 51371 55 19070 3629 10343 371 43 3023 1876
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 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2020-12-31 to 2021-01-06

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2020-12-30 1613928 10724 158372 7619200 606950 1626461 168114 169579 211512 45960 137166 120912 12793 1413935 242744 106958 1010496 6181 18480 113121
2020-12-31 1614000 10790 159500 7695000 608400 1638000 169200 170200 212100 46160 137800 121300 12830 1428000 246100 108300 1012000 6246 18990 113400
2021-01-01 1623000 10860 160500 7725000 610300 1649000 169600 171200 212800 46190 138200 121700 12860 1437000 249400 108900 1014000 6307 19500 113700
2021-01-02 1628000 10930 161500 7748000 612600 1659000 169600 172200 213400 46450 138400 122000 12890 1444000 252600 109300 1015000 6369 20010 114000
2021-01-03 1633000 11000 162500 7766000 614300 1670000 169600 173100 213600 46530 138600 122400 12910 1450000 255800 109700 1017000 6428 20520 114300
2021-01-04 1639000 11080 163400 7787000 616100 1681000 172000 173600 213800 47130 138600 122800 12940 1455000 259000 110500 1018000 6488 21040 114600
2021-01-05 1649000 11150 164400 7836000 617900 1692000 172900 174400 214200 47160 139300 123100 12970 1466000 262300 111200 1020000 6548 21560 114900
2021-01-06 1659000 11220 165400 7886000 620100 1703000 174100 175400 215300 47530 140100 123500 13000 1477000 265600 112000 1021000 6608 22090 115200

Confirmed count average forecast Latin America (bold black line in graphs) 2020-12-31 to 2021-01-06

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2020-12-30 1613928 10724 158372 7619200 606950 1626461 168114 169579 211512 45960 137166 120912 12793 1413935 242744 106958 1010496 6181 18480 113121
2020-12-31 1621000 10810 159400 7676000 609700 1638000 169100 170600 212100 46370 137800 121300 12830 1425000 246400 107700 1012000 6251 19080 113400
2021-01-01 1628000 10900 160100 7708000 611600 1650000 169900 171600 212400 46500 138100 121600 12890 1435000 249000 108400 1013000 6308 19590 113600
2021-01-02 1633000 10990 160700 7732000 613800 1661000 170300 172500 212700 46740 138500 121900 12940 1442000 251400 108900 1015000 6365 20110 113900
2021-01-03 1638000 11080 161200 7748000 615500 1673000 170700 173400 212900 46900 138700 122200 12990 1449000 253800 109400 1016000 6422 20670 114200
2021-01-04 1644000 11210 161800 7773000 617500 1685000 172200 174100 213200 47320 139000 122500 13030 1457000 256000 110200 1017000 6479 21250 114400
2021-01-05 1651000 11320 162600 7831000 619100 1697000 173000 174900 213400 47460 139500 122900 13070 1468000 258600 111000 1019000 6537 21860 114700
2021-01-06 1658000 11440 163200 7887000 620800 1709000 173900 175700 213800 47660 140000 123200 13130 1479000 261200 111800 1020000 6595 22510 114900

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