COVID-19 short-term forecasts Deaths 2020-10-06 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 2020-10-06

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date (mm-dd)10-0109-0707-2107-1708-2109-2909-0309-0708-0706-0507-1007-30 --07-2309-3007-2309-17
Peak daily increment 2494 1429 1065 785 322 19 24 3346 11 43 6 35 28 20 2947 9
Days since peak 5 29 77 81 46 7 33 29 60 123 88 68 75 6 75 19
Last total 21827 8156 147494 13070 27017 1004 2149 11702 869 3310 229 2447 82348 2440 966 32834 665
Last daily increment 359 27 819 33 173 17 5 21 4 8 0 14 471 10 19 92 7
Last week 4890 191 3542 329 1019 100 44 347 26 64 0 94 4702 68 109 438 37
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) 2020-10-07 to 2020-10-13

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-06 21827 8156 147494 13070 27017 1004 2149 11702 869 3310 2447 82348 2440 966 32834 665
2020-10-07 22280 8266 148300 13090 27200 1024 2157 11740 873 3338 2463 82350 2452 985 32940 671
2020-10-08 22630 8301 149200 13180 27370 1038 2163 11770 876 3351 2479 83050 2463 1005 33040 677
2020-10-09 22990 8341 149400 13230 27530 1053 2170 11800 879 3360 2494 83760 2474 1024 33140 683
2020-10-10 23230 8393 150400 13290 27680 1071 2176 11830 883 3379 2510 84260 2485 1043 33240 689
2020-10-11 23430 8393 150800 13340 27840 1071 2183 11860 886 3387 2525 84420 2496 1062 33340 695
2020-10-12 23840 8393 151000 13390 28000 1108 2189 11900 889 3391 2541 87410 2507 1081 33440 701
2020-10-13 24160 8406 151800 13430 28160 1123 2195 11930 893 3398 2557 87590 2518 1101 33540 707

Deaths count average forecast Latin America (bold black line in graphs) 2020-10-07 to 2020-10-13

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-06 21827 8156 147494 13070 27017 1004 2149 11702 869 3310 2447 82348 2440 966 32834 665
2020-10-07 22270 8195 148200 13110 27210 1024 2155 11740 873 3319 2463 83280 2450 985 32930 671
2020-10-08 23190 8239 148900 13180 27360 1040 2159 11800 877 3331 2480 83880 2461 1005 33030 678
2020-10-09 23620 8287 149300 13230 27550 1057 2162 11850 881 3340 2497 84490 2472 1025 33120 685
2020-10-10 24000 8344 150200 13290 27730 1077 2166 11920 885 3355 2515 85010 2483 1046 33210 692
2020-10-11 24340 8373 150600 13340 27890 1087 2170 11980 889 3367 2533 85380 2495 1067 33310 699
2020-10-12 24770 8396 151000 13390 28060 1115 2173 12010 893 3378 2551 86390 2506 1089 33410 706
2020-10-13 25200 8419 151800 13440 28210 1135 2177 12040 898 3389 2569 86970 2517 1111 33500 714

Deaths count scenario forecast (bold purple line in graphs) 2020-10-07 to 2020-10-15

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-10-06 21827 8156 147494 13070 27017 1004 2149 11702 869 3310 2447 82348 2440 966 32834 665
2020-10-07 22670 8165 148100 13120 27190 1016 2149 11720 871 3322 2469 82440 2452 988 32910 670
2020-10-08 23300 8183 148600 13170 27340 1028 2152 11730 875 3333 2486 82860 2463 1008 32990 675
2020-10-09 23860 8197 149200 13210 27500 1040 2154 11750 878 3344 2502 83080 2474 1025 33060 680
2020-10-10 24340 8212 149700 13260 27620 1049 2156 11770 881 3354 2518 83490 2483 1048 33120 684
2020-10-11 24950 8223 150200 13300 27730 1058 2158 11810 883 3364 2534 83670 2491 1070 33190 688
2020-10-12 25380 8236 150800 13340 27840 1069 2160 11820 885 3371 2549 83850 2499 1092 33250 692
2020-10-13 25820 8247 151200 13390 27960 1078 2163 11840 887 3377 2563 84140 2506 1111 33300 696
2020-10-14 26270 8257 151600 13430 28030 1088 2165 11860 889 3382 2575 84400 2512 1130 33350 699
2020-10-15 26710 8267 151900 13470 28120 1095 2167 11870 891 3386 2586 84660 2518 1149 33400 702

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