COVID-19 short-term forecasts Confirmed 2021-03-31 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:
    [2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.

Peak increase in estimated trend of Confirmed in Latin America 2021-03-31

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-22 -- --2021-01-1609-142021-01-182021-01-162021-01-1407-182021-03-282021-01-232021-02-032021-03-152021-01-2005-262021-01-07 -- --2021-01-1011-22 --09-08
Peak daily increment 104 106 1122 2113 17013 1226 1589 1869 313 2590 75 63 1356 635 16981 177 3354 81 55 1085
Days since peak 165 41 118 68 74 198 72 74 76 256 3 67 56 16 70 309 83 80 129 204
Last total 2348821 9119 3652 12456 272411 12748747 995538 2406377 216764 252727 328755 64431 193834 10249 12758 189043 39237 2238887 6677 355051 214667 1548807 9122 8026 105549 160497
Last daily increment 16056 0 10 41 992 90638 6046 8646 0 343 1430 0 0 57 0 529 389 5977 0 447 1976 15686 13 22 3088 1348
Last week 70706 184 59 45 4578 428578 40695 46435 3326 1759 10099 665 2627 351 26 3559 2109 24345 48 2472 11967 48342 37 102 16091 6332
Previous peak date10-16 -- -- --07-1707-2906-06 -- --07-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-0208-1409-19 -- --
Previous peak daily increment 14331 1578 48660 7792 1405 7778 420 66 177 795 160 22833 8380 89 119
Low between peaks 93 400 -4305 90 13 6 305 50 4599 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-01 to 2021-04-07

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-31 2348821 272411 12748747 995538 2406377 216764 252727 328755 64431 193834 10249 189043 39237 2238887 355051 214667 1548807 105549 160497
2021-04-01 2361000 273000 12854000 1002000 2415000 216800 253500 329700 64610 195200 10310 189700 39710 2245000 355500 216600 1556000 108600 161400
2021-04-02 2376000 273900 12950000 1010000 2423000 218000 254000 331400 64760 196200 10380 190400 40750 2250000 356000 218000 1561000 111800 161400
2021-04-03 2388000 274400 13037000 1017000 2431000 218000 254300 332300 64890 197100 10440 191000 41520 2255000 356400 219700 1567000 114900 161400
2021-04-04 2399000 274700 13089000 1025000 2438000 218000 254700 333700 65020 197400 10500 191700 42140 2257000 356700 221400 1574000 117800 161800
2021-04-05 2413000 275300 13134000 1032000 2446000 218000 255000 333900 65150 197600 10560 192300 42740 2258000 356900 223200 1581000 120700 162400
2021-04-06 2426000 276300 13215000 1037000 2454000 218100 255200 335500 65280 198000 10620 192900 43260 2262000 357300 225000 1588000 123600 163000
2021-04-07 2438000 277200 13301000 1042000 2461000 218200 255600 337000 65410 198400 10680 193500 43750 2268000 357700 226900 1596000 126500 163800

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-01 to 2021-04-07

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-31 2348821 272411 12748747 995538 2406377 216764 252727 328755 64431 193834 10249 189043 39237 2238887 355051 214667 1548807 105549 160497
2021-04-01 2361000 273200 12836000 1003000 2414000 217000 253100 330000 64510 194400 10300 189600 39650 2244000 355400 216600 1560000 108200 161400
2021-04-02 2369000 273800 12918000 1010000 2420000 217800 253600 331400 64610 195100 10370 190300 40150 2248000 355700 218400 1569000 110500 161900
2021-04-03 2377000 274200 12992000 1017000 2427000 218000 253900 332200 64710 195800 10440 190800 40580 2253000 356000 220200 1576000 112900 162400
2021-04-04 2385000 274400 13033000 1024000 2433000 218100 254300 333300 64840 196200 10500 191400 41040 2255000 356200 222100 1584000 115200 163000
2021-04-05 2394000 274700 13068000 1030000 2439000 218300 254600 333700 65020 196500 10560 192000 41620 2258000 356300 223900 1589000 117500 163600
2021-04-06 2402000 275200 13140000 1034000 2446000 218800 254900 334400 65140 197000 10630 192600 42000 2262000 356600 225800 1596000 119900 164200
2021-04-07 2409000 275700 13219000 1037000 2453000 219100 255300 335700 65300 197600 10690 193100 42400 2267000 356800 227800 1602000 122300 164800

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

[2021-01-07]Slideshow of forecasts, errors, and actuals 2020-06-30 to 2021-01-02: how England lost the battle.
[2020-10-27]Statistical short-term forecasting of the COVID-19 Pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now published at the Journal of Clinical Immunology and Immunotherapy. open access
[2020-10-11]Short-term forecasting of the coronavirus pandemic (Jurgen Doornik, Jennie Castle, and David Hendry) is now in press at the International Journal of Forecasting. open access
[2020-10-10]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 Confirmed