COVID-19 short-term forecasts Deaths 2021-01-02 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:
    [2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.

Peak increase in estimated trend of Deaths in Latin America 2021-01-02

ArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
Peak date (mm-dd)10-0112-0309-0707-2107-1708-2309-2809-0309-0711-2611-1807-1007-3009-2910-05 --09-2607-23 --09-20
Peak daily increment 2439 6 1427 1065 785 318 18 22 3440 6 31 6 35 4 1724 20 2947 9
Days since peak 93 30 117 165 169 132 96 121 117 37 45 176 156 95 89 98 163 104
Last total 43375 249 9186 195725 16724 43765 2185 2416 14059 1327 4827 236 3173 303 126851 4103 2279 37680 201 1032
Last daily increment 56 1 11 314 64 270 0 0 8 0 7 0 13 0 344 39 17 0 8 2
Last week 725 16 88 4586 281 1594 99 12 67 22 59 1 108 8 4425 263 91 312 43 18
Previous peak date -- -- -- -- -- -- --04-1205-1008-0706-05 -- -- -- --07-23 -- -- -- --
Previous peak daily increment 22 167 11 43 28
Low between peaks 4 14 3 12

Deaths count forecast Latin America (bold red line in graphs) 2021-01-03 to 2021-01-09

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruUruguay
2021-01-02 43375 9186 195725 16724 43765 2185 14059 1327 4827 3173 126851 4103 2279 37680 201
2021-01-03 43540 9200 196200 16750 44010 2185 14070 1328 4834 3186 127100 4146 2294 37780 207
2021-01-04 43740 9215 196600 16770 44250 2235 14090 1330 4835 3198 127500 4188 2308 37840 215
2021-01-05 43910 9228 197600 16810 44490 2247 14100 1331 4839 3210 128500 4230 2323 37900 222
2021-01-06 44040 9241 198700 16820 44720 2259 14110 1332 4846 3221 129400 4272 2337 37960 230
2021-01-07 44130 9255 199600 16910 44950 2272 14120 1333 4866 3232 130100 4314 2351 38020 237
2021-01-08 44230 9268 200000 16960 45180 2272 14130 1334 4871 3244 130800 4356 2366 38070 245
2021-01-09 44300 9282 200300 17020 45410 2272 14150 1336 4878 3255 131100 4398 2381 38130 254

Deaths count average forecast Latin America (bold black line in graphs) 2021-01-03 to 2021-01-09

DateArgentinaBoliviaBrazilChileColombiaCosta RicaEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruUruguay
2021-01-02 43375 9186 195725 16724 43765 2185 14059 1327 4827 3173 126851 4103 2279 37680 201
2021-01-03 43470 9197 196100 16770 44020 2190 14070 1329 4830 3189 127200 4146 2292 37720 210
2021-01-04 43630 9208 196600 16800 44230 2222 14070 1333 4841 3199 127600 4190 2307 37760 218
2021-01-05 43760 9219 197500 16830 44470 2235 14080 1338 4851 3209 128500 4233 2323 37800 227
2021-01-06 43890 9228 198400 16860 44710 2249 14090 1343 4860 3218 129400 4277 2338 37840 236
2021-01-07 43990 9237 199100 16920 44960 2265 14090 1348 4878 3228 130200 4321 2354 37870 245
2021-01-08 44100 9247 199700 16950 45210 2275 14100 1353 4889 3237 130900 4365 2370 37910 254
2021-01-09 44200 9258 200200 16990 45450 2283 14110 1358 4902 3247 131200 4410 2387 37950 264

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

[2020-10-11]Short-term forecasting of the coronavirus pandemic (with Jennie Castle and David Hendry) is now in press at the International Journal of Forecasting.
[2020-10-10]Temporarily removed forecasts from the Chinese scenarios, while investigating possibility to use own history from the first wave.
Added information on the previous peak (if present) to the peak tables.
Local forecasts for England: now dropping last four observations.
[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