COVID-19 short-term forecasts Deaths 2020-09-21 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 2020-09-21

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date --09-0707-2107-1708-21 -- --09-0708-0706-0507-1007-3006-2407-23 --07-23 --
Peak daily increment 1315 1065 785 340 3467 11 43 6 35 683 28 2947
Days from 100 to peak 122 115 93 131 159 46 5 12 83 79 98 107
Days from peak/2 to peak 82 106 63 124 122 116 29 83 114 72 116 78
Last total 13482 7654 137272 12298 24397 745 2054 11095 812 3124 221 2204 73697 2272 676 31369 555
Last daily increment 429 37 377 12 189 39 7 5 1 5 0 20 204 15 17 0 8
Last week 1630 207 4153 258 1109 112 56 132 20 140 1 117 2019 85 124 557 53
Days since peak 14 62 66 31 14 45 108 73 53 89 60 60

Deaths count forecast Latin America (bold red line in graphs) 2020-09-22 to 2020-09-28

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-21 13482 7654 137272 12298 24397 745 2054 11095 812 3124 2204 73697 2272 676 31369 555
2020-09-22 13770 7702 138200 12370 24600 773 2067 11150 814 3126 2218 74550 2284 680 31480 564
2020-09-23 14060 7746 139200 12390 24790 795 2081 11200 817 3145 2230 74850 2296 693 31600 573
2020-09-24 14350 7788 139900 12480 24990 821 2094 11240 819 3168 2243 75100 2308 711 31710 582
2020-09-25 14660 7828 140700 12540 25190 846 2108 11290 822 3197 2256 75610 2320 734 31820 591
2020-09-26 14970 7868 141300 12590 25390 870 2122 11330 824 3219 2268 76020 2331 755 31930 599
2020-09-27 15290 7908 141600 12630 25590 874 2135 11380 827 3230 2280 76180 2343 775 32050 608
2020-09-28 15610 7947 142000 12660 25790 918 2149 11430 829 3239 2293 76340 2355 792 32160 617

Deaths count average forecast Latin America (bold black line in graphs) 2020-09-22 to 2020-09-28

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-21 13482 7654 137272 12298 24397 745 2054 11095 812 3124 2204 73697 2272 676 31369 555
2020-09-22 13640 7693 138000 12330 24520 756 2064 11120 814 3133 2208 74200 2280 685 31470 563
2020-09-23 13940 7778 138900 12370 24670 775 2080 11170 817 3151 2217 74520 2292 699 31600 572
2020-09-24 14270 7863 139600 12440 24820 797 2096 11220 821 3172 2228 74770 2303 716 31740 581
2020-09-25 14590 7941 140400 12490 24990 818 2112 11270 824 3196 2238 75260 2315 733 31870 590
2020-09-26 14870 8058 141000 12550 25220 839 2128 11320 828 3216 2249 75830 2326 751 32010 600
2020-09-27 15180 8149 141500 12600 25390 850 2144 11380 831 3230 2261 76020 2337 769 32140 609
2020-09-28 15540 8252 142000 12660 25540 882 2160 11430 835 3243 2274 76280 2349 786 32260 619

Deaths count scenario forecast (bold purple line in graphs) 2020-09-22 to 2020-09-30

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-21 13482 7654 137272 12298 24397 745 2054 11095 812 3124 2204 73697 2272 676 31369 555
2020-09-22 13720 7745 138300 12370 24550 760 2084 11280 815 3145 2204 74350 2280 689 31580 563
2020-09-23 14000 7790 139000 12410 24680 775 2097 11280 817 3166 2210 74690 2293 702 31680 572
2020-09-24 14220 7841 139600 12450 24820 792 2107 11330 819 3183 2218 75030 2305 717 31760 580
2020-09-25 14450 7878 140300 12490 24950 806 2111 11330 821 3201 2226 75420 2316 733 31830 587
2020-09-26 14750 7917 140900 12530 25060 820 2122 11360 823 3216 2234 75800 2327 750 31900 595
2020-09-27 15040 7917 141500 12560 25170 834 2131 11380 825 3227 2240 76190 2336 772 31960 603
2020-09-28 15260 7933 142000 12600 25280 848 2138 11390 827 3236 2246 76530 2345 795 32020 609
2020-09-29 15530 7958 142500 12640 25370 859 2150 11410 829 3247 2252 76870 2353 814 32090 615
2020-09-30 15820 7984 143200 12670 25460 872 2159 11430 830 3251 2258 77240 2363 830 32160 621

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