COVID-19 short-term forecasts Deaths 2020-10-03 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 2020-10-03

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date (mm-dd) --09-0707-2107-1708-21 --09-0309-0708-0706-0507-1007-3006-2407-23 --07-2309-22
Peak daily increment 1474 1065 785 322 25 3493 11 43 6 35 683 28 2947 9
Days since peak 26 74 78 43 30 26 57 120 85 65 101 72 72 11
Last total 20795 8073 145987 12919 26556 950 2128 11597 857 3285 229 2399 78880 2414 913 32609 649
Last daily increment 196 28 1307 52 159 20 11 102 4 18 0 13 388 8 23 74 6
Last week 5046 215 4246 278 1068 122 33 318 31 56 2 111 2450 74 110 467 43
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) 2020-10-04 to 2020-10-10

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-03 20795 8073 145987 12919 26556 950 2128 11597 857 3285 2399 78880 2414 913 32609 649
2020-10-04 21510 8073 146600 12970 26730 950 2134 11700 861 3320 2415 79220 2426 935 32720 657
2020-10-05 22190 8130 146900 13020 26900 985 2140 11780 864 3324 2430 79380 2437 957 32830 665
2020-10-06 22860 8153 147700 13050 27070 1002 2147 11870 867 3329 2445 79940 2449 980 32940 672
2020-10-07 23540 8198 148500 13070 27240 1023 2153 11940 870 3339 2460 80380 2461 1004 33050 680
2020-10-08 24230 8254 149400 13170 27410 1036 2159 12020 874 3355 2475 80780 2472 1029 33150 688
2020-10-09 24950 8285 149600 13220 27580 1051 2165 12100 877 3365 2491 81190 2484 1054 33260 695
2020-10-10 25690 8377 150700 13270 27750 1069 2171 12180 880 3384 2506 81540 2495 1079 33370 703

Deaths count average forecast Latin America (bold black line in graphs) 2020-10-04 to 2020-10-10

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-03 20795 8073 145987 12919 26556 950 2128 11597 857 3285 2399 78880 2414 913 32609 649
2020-10-04 21410 8111 146500 12970 26730 960 2134 11650 861 3296 2414 79070 2426 934 32700 656
2020-10-05 22120 8157 146900 13020 26890 989 2138 11680 865 3304 2431 79240 2438 957 32810 665
2020-10-06 22800 8198 147600 13060 27030 1009 2143 11710 868 3314 2449 79780 2449 982 32910 673
2020-10-07 23510 8244 148300 13090 27220 1031 2147 11740 872 3325 2466 80230 2461 1005 33020 682
2020-10-08 24240 8291 149000 13160 27370 1050 2151 11780 876 3342 2484 80680 2473 1030 33130 690
2020-10-09 25010 8339 149600 13220 27560 1071 2155 11820 880 3358 2502 81090 2484 1056 33240 699
2020-10-10 25770 8392 150400 13270 27760 1093 2159 11850 884 3378 2521 81520 2496 1086 33350 708

Deaths count scenario forecast (bold purple line in graphs) 2020-10-04 to 2020-10-12

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-03 20795 8073 145987 12919 26556 950 2128 11597 857 3285 2399 78880 2414 913 32609 649
2020-10-04 21300 8095 146500 12960 26710 970 2132 11600 859 3297 2420 79340 2426 940 32710 660
2020-10-05 21710 8115 147200 13000 26870 990 2133 11630 861 3308 2438 79740 2438 962 32800 669
2020-10-06 22600 8132 147700 13050 27010 1008 2135 11640 864 3318 2461 80190 2449 988 32890 677
2020-10-07 23880 8149 148300 13080 27160 1025 2137 11660 867 3328 2477 80600 2460 1014 32990 686
2020-10-08 24820 8162 148900 13120 27290 1040 2139 11670 869 3337 2495 80970 2471 1039 33050 695
2020-10-09 25710 8178 149400 13160 27400 1055 2141 11680 871 3348 2513 81340 2481 1060 33110 704
2020-10-10 26780 8191 149900 13190 27500 1069 2144 11700 873 3356 2527 81720 2491 1081 33160 712
2020-10-11 27670 8202 150400 13220 27600 1082 2146 11710 874 3364 2542 82030 2499 1108 33220 720
2020-10-12 28640 8214 150800 13250 27710 1096 2149 11720 876 3373 2555 82360 2507 1127 33270 728

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