COVID-19 short-term forecasts Deaths 2020-10-01 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 2020-10-01

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date (mm-dd) --09-0707-2107-1708-21 --09-0309-0708-0706-0507-1007-3006-2407-23 --07-23 --
Peak daily increment 1457 1065 785 322 24 3502 11 43 6 35 683 28 2947
Days since peak 24 72 76 41 28 24 55 118 83 63 99 70 70
Last total 20288 8001 144680 12822 26196 917 2108 11433 848 3261 229 2380 78078 2387 869 32463 635
Last daily increment 3351 36 728 81 198 13 3 78 5 15 0 27 432 15 12 67 7
Last week 5080 201 4143 295 1093 105 21 197 22 75 2 109 2234 76 108 426 44
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) 2020-10-02 to 2020-10-08

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-01 20288 8001 144680 12822 26196 917 2108 11433 848 3261 2380 78078 2387 869 32463 635
2020-10-02 21210 8036 145500 12870 26370 934 2115 11470 851 3294 2397 78610 2399 892 32580 643
2020-10-03 21910 8067 146300 12930 26540 951 2121 11500 855 3318 2412 78970 2410 917 32700 651
2020-10-04 22520 8098 146700 12980 26710 951 2128 11530 858 3329 2428 79130 2421 941 32810 659
2020-10-05 23100 8126 147000 13030 26880 984 2134 11560 861 3334 2443 79290 2432 967 32930 666
2020-10-06 23690 8155 147800 13060 27040 1001 2140 11590 864 3341 2458 79850 2444 993 33040 674
2020-10-07 24290 8184 148500 13070 27210 1023 2147 11620 867 3350 2473 80310 2455 1021 33150 682
2020-10-08 24910 8212 149400 13170 27380 1037 2153 11650 870 3366 2487 80710 2466 1050 33260 690

Deaths count average forecast Latin America (bold black line in graphs) 2020-10-02 to 2020-10-08

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-01 20288 8001 144680 12822 26196 917 2108 11433 848 3261 2380 78078 2387 869 32463 635
2020-10-02 21740 8026 145400 12870 26410 936 2112 11450 852 3276 2403 78500 2399 887 32560 643
2020-10-03 22340 8075 146100 12930 26620 956 2117 11490 856 3296 2419 78870 2411 910 32660 652
2020-10-04 22920 8130 146600 12980 26790 968 2123 11530 859 3310 2436 79030 2422 934 32770 661
2020-10-05 23550 8178 147000 13030 26950 997 2129 11550 863 3320 2452 79180 2433 957 32880 670
2020-10-06 24200 8268 147800 13070 27080 1017 2134 11650 866 3334 2469 79730 2445 984 32990 679
2020-10-07 24860 8328 148400 13110 27280 1041 2140 11700 870 3350 2486 80220 2456 1010 33100 688
2020-10-08 25560 8388 149300 13180 27400 1061 2146 11740 873 3369 2503 80670 2468 1036 33240 697

Deaths count scenario forecast (bold purple line in graphs) 2020-10-02 to 2020-10-10

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-01 20288 8001 144680 12822 26196 917 2108 11433 848 3261 2380 78078 2387 869 32463 635
2020-10-02 20290 8034 145300 12860 26320 942 2116 11450 848 3277 2384 78460 2397 896 32570 643
2020-10-03 20290 8067 145900 12900 26440 963 2121 11470 850 3292 2401 78830 2408 915 32650 650
2020-10-04 21350 8097 146500 12940 26560 984 2125 11490 853 3305 2421 79200 2418 934 32740 658
2020-10-05 21800 8124 147100 12980 26650 1005 2126 11510 854 3317 2439 79540 2428 953 32820 665
2020-10-06 22360 8154 147600 13020 26750 1025 2130 11530 856 3329 2455 79860 2437 972 32910 672
2020-10-07 22870 8169 148200 13060 26830 1045 2133 11540 858 3342 2470 80170 2446 990 32990 679
2020-10-08 23430 8182 148700 13090 26930 1064 2136 11550 860 3353 2489 80460 2454 1006 33050 686
2020-10-09 23890 8193 149200 13120 27000 1085 2139 11570 861 3363 2506 80740 2461 1024 33110 695
2020-10-10 24410 8216 149800 13160 27110 1104 2141 11580 863 3373 2526 80960 2467 1041 33160 701

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