COVID-19 short-term forecasts Deaths 2020-11-14 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 Deaths in Latin America 2020-11-14

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruVenezuela
Peak date (mm-dd)10-0109-0707-2107-1708-2309-2809-0309-0708-0706-0507-1007-3009-2910-0507-2309-2607-2309-20
Peak daily increment 2439 1427 1065 785 318 18 22 3440 11 43 6 35 4 1724 28 20 2947 9
Days since peak 44 68 116 120 83 47 72 68 99 162 127 107 46 40 114 49 114 55
Last total 35307 8841 165658 14777 33829 1546 2282 12997 1039 3920 232 2822 229 98259 2867 1569 35106 848
Last daily increment 262 6 921 39 160 9 2 20 6 40 0 2 2 635 11 13 39 4
Last week 1747 46 3261 234 1038 82 17 167 29 99 0 77 8 3232 69 79 285 22
Previous peak date -- -- -- -- -- --04-1205-10 -- -- -- -- -- -- -- -- -- --
Previous peak daily increment 22 167
Low between peaks 4 14

Deaths count forecast Latin America (bold red line in graphs) 2020-11-15 to 2020-11-21

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-11-14 35307 8841 165658 14777 33829 1546 12997 1039 3920 2822 98259 2867 1569 35106 848
2020-11-15 35680 8854 165700 14840 34010 1552 13030 1043 3924 2832 98300 2878 1581 35180 852
2020-11-16 36030 8867 165900 14880 34180 1575 13050 1048 3925 2841 98500 2889 1593 35240 855
2020-11-17 36330 8879 166100 14900 34360 1587 13080 1052 3935 2851 99100 2900 1605 35300 858
2020-11-18 36680 8892 166700 14920 34530 1597 13100 1056 3947 2860 99700 2910 1616 35360 862
2020-11-19 36930 8904 167300 15000 34700 1609 13130 1060 3961 2869 100200 2921 1628 35420 865
2020-11-20 37200 8916 167800 15030 34870 1619 13150 1064 3981 2878 100800 2932 1639 35480 869
2020-11-21 37420 8928 168500 15070 35040 1628 13180 1069 4010 2887 101300 2942 1651 35530 872

Deaths count average forecast Latin America (bold black line in graphs) 2020-11-15 to 2020-11-21

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-11-14 35307 8841 165658 14777 33829 1546 12997 1039 3920 2822 98259 2867 1569 35106 848
2020-11-15 35520 8849 166100 14820 34000 1551 13020 1044 3938 2831 98500 2880 1581 35150 852
2020-11-16 35840 8860 166300 14860 34170 1569 13030 1048 3945 2841 98700 2891 1593 35200 855
2020-11-17 36120 8873 166600 14890 34330 1582 13050 1053 3954 2851 99300 2901 1605 35240 859
2020-11-18 36430 8886 167100 14910 34510 1594 13070 1058 3965 2861 99900 2912 1617 35290 863
2020-11-19 36720 8899 167500 14970 34680 1609 13090 1062 3978 2871 100400 2923 1630 35330 866
2020-11-20 37030 8915 168000 15010 34860 1622 13100 1067 3994 2882 100900 2933 1642 35380 870
2020-11-21 37300 8929 168300 15050 35030 1636 13120 1071 4009 2892 101400 2944 1655 35420 874

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 Deaths