COVID-19 short-term forecasts Deaths 2020-09-24 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:
    [2020-07-01] 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.

Peak increase in estimated trend of Deaths in Latin America 2020-09-24

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date --09-0707-2107-1708-25 --04-1209-0708-0706-0507-1007-3006-2407-23 --07-23 --
Peak daily increment 1360 1065 785 329 22 3439 11 43 6 35 683 28 2947
Days from 100 to peak 122 115 93 135 5 159 46 5 12 83 79 98 107
Days from peak/2 to peak 81 106 63 128 17 122 116 29 83 114 72 116 78
Last total 14766 7765 139808 12469 24746 795 2076 11213 823 3170 227 2249 75439 2297 743 31870 581
Last daily increment 390 34 1703 124 0 14 2 42 4 16 2 27 490 6 16 302 7
Last week 2110 215 4015 270 1081 109 42 169 19 94 6 103 2636 68 132 724 51
Days since peak 17 65 69 30 165 17 48 111 76 56 92 63 63

Deaths count forecast Latin America (bold red line in graphs) 2020-09-25 to 2020-10-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-24 14766 7765 139808 12469 24746 795 2076 11213 3170 2249 75439 2297 743 31870 581
2020-09-25 15110 7765 140700 12530 24930 804 2087 11210 3194 2264 75890 2308 756 32020 588
2020-09-26 15470 7765 141400 12580 25100 821 2098 11210 3219 2277 76290 2319 779 32160 596
2020-09-27 15820 7765 141800 12620 25270 822 2109 11210 3230 2290 76490 2330 802 32300 603
2020-09-28 16190 7772 142100 12650 25440 858 2120 11210 3237 2303 76660 2341 820 32420 610
2020-09-29 16560 7787 143000 12680 25600 871 2131 11210 3251 2315 77280 2352 848 32550 618
2020-09-30 16940 7801 143200 12700 25770 891 2141 11210 3268 2328 77770 2363 869 32670 625
2020-10-01 17330 7816 144500 12820 25940 905 2152 11210 3285 2340 78210 2374 889 32790 632

Deaths count average forecast Latin America (bold black line in graphs) 2020-09-25 to 2020-10-01

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-24 14766 7765 139808 12469 24746 795 2076 11213 3170 2249 75439 2297 743 31870 581
2020-09-25 15030 7767 140400 12510 24830 809 2084 11210 3192 2254 75670 2306 756 31920 589
2020-09-26 15350 7835 141000 12550 25060 827 2096 11260 3213 2264 76040 2318 776 32000 598
2020-09-27 15700 7895 141400 12600 25220 843 2109 11290 3228 2276 76180 2329 797 32100 607
2020-09-28 16120 7958 141900 12640 25370 864 2121 11320 3242 2289 76330 2341 819 32210 616
2020-09-29 16510 8022 142600 12690 25490 883 2134 11350 3258 2302 76940 2352 842 32340 625
2020-09-30 16900 8060 143300 12730 25740 903 2146 11380 3275 2315 77360 2364 865 32460 635
2020-10-01 17340 8231 144200 12810 25930 923 2159 11500 3295 2328 77610 2376 889 32590 645

Deaths count scenario forecast (bold purple line in graphs) 2020-09-25 to 2020-10-03

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-24 14766 7765 139808 12469 24746 795 2076 11213 3170 2249 75439 2297 743 31870 581
2020-09-25 14910 7841 140300 12510 25090 810 2093 11340 3189 2249 75740 2313 755 31890 589
2020-09-26 15250 7899 140700 12540 25220 824 2104 11450 3204 2257 76110 2324 771 31970 598
2020-09-27 15570 7930 141100 12580 25310 842 2119 11480 3220 2270 76460 2335 801 32040 607
2020-09-28 15900 7931 141600 12610 25450 858 2128 11510 3234 2280 76790 2346 818 32090 617
2020-09-29 16280 7931 142100 12640 25590 878 2136 11530 3247 2291 77140 2355 844 32160 625
2020-09-30 16620 7938 142400 12670 25690 894 2141 11550 3260 2301 77490 2365 860 32210 635
2020-10-01 17020 7950 142900 12690 25750 921 2150 11570 3271 2311 77830 2374 877 32260 644
2020-10-02 17410 7962 143400 12720 25870 942 2153 11590 3280 2318 78170 2382 894 32330 652
2020-10-03 17720 7979 143800 12750 25950 959 2159 11590 3288 2326 78500 2390 911 32380 659

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

[2020-07-01] 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