COVID-19 short-term forecasts Confirmed 2020-12-23 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 Confirmed in Latin America 2020-12-23

ArgentinaBahamasBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)10-1910-1712-0307-1712-1606-06 --09-1407-2609-2311-1207-1809-2112-1406-2809-2212-1005-26 -- --08-0208-1311-22 --09-08
Peak daily increment 14377 104 1071 1578 47815 7361 1225 1408 1225 187 2699 66 47 795 160 10476 145 8364 89 60 1086
Days since peak 65 67 20 159 7 200 100 150 91 41 158 93 9 178 92 13 211 143 132 31 106
Last total 1563865 7788 10370 152064 7365517 590914 1544826 161942 162496 207084 43772 134256 6258 9805 118036 12423 1350079 5991 220261 102371 1000153 5734 7071 14710 111024
Last daily increment 8586 16 113 1005 46696 1725 14233 1138 566 720 0 655 42 34 448 69 11653 53 3059 827 1678 65 47 709 196
Last week 39493 63 579 3399 255083 12182 76031 5554 5191 2835 1375 2821 216 178 2562 455 60781 53 16966 5343 10696 306 131 3274 1943
Previous peak date -- -- -- --08-04 -- -- -- --04-2408-05 -- --06-06 -- --10-05 -- -- -- -- --09-19 -- --
Previous peak daily increment 45353 7756 420 179 23279 119
Low between peaks 19229 -4346 90 5 4836 23

Confirmed count forecast Latin America (bold red line in graphs) 2020-12-24 to 2020-12-30

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2020-12-23 1563865 10370 152064 7365517 590914 1544826 161942 162496 207084 43772 134256 6258 118036 12423 1350079 220261 102371 1000153 5734 14710 111024
2020-12-24 1567000 10500 152900 7419000 591700 1558000 163100 163200 207700 43950 134900 6293 118500 12500 1360000 222900 103100 1001000 5795 15430 111400
2020-12-25 1574000 10630 153700 7469000 593800 1572000 164200 163800 208300 44070 135500 6327 118900 12570 1371000 225300 104300 1003000 5850 16140 111700
2020-12-26 1579000 10760 154500 7516000 595800 1585000 164200 164900 208800 44660 136000 6361 119200 12640 1382000 227600 105100 1004000 5907 16880 112100
2020-12-27 1583000 10880 155200 7539000 597700 1598000 164200 165900 209400 44860 136100 6394 119600 12710 1388000 229800 105700 1006000 5961 17640 112400
2020-12-28 1588000 11010 155900 7560000 599600 1611000 166400 166500 209900 45130 136200 6427 120000 12780 1392000 232000 106400 1007000 6015 18430 112800
2020-12-29 1595000 11140 156700 7611000 600900 1624000 167300 167200 210500 45240 136900 6460 120400 12850 1403000 234100 107300 1009000 6069 19250 113100
2020-12-30 1602000 11270 157400 7659000 602300 1637000 168300 167800 211100 45300 137500 6493 120800 12920 1414000 236200 108100 1010000 6123 20120 113500

Confirmed count average forecast Latin America (bold black line in graphs) 2020-12-24 to 2020-12-30

DateArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameUruguayVenezuela
2020-12-23 1563865 10370 152064 7365517 590914 1544826 161942 162496 207084 43772 134256 6258 118036 12423 1350079 220261 102371 1000153 5734 14710 111024
2020-12-24 1571000 10520 152700 7423000 592800 1558000 162900 163300 207400 43840 134800 6296 118400 12490 1361000 223200 103200 1002000 5790 15320 111300
2020-12-25 1577000 10650 153100 7473000 594900 1570000 163800 164100 207700 44010 135300 6329 118800 12560 1372000 225800 104200 1003000 5828 15870 111700
2020-12-26 1583000 10780 153500 7518000 596800 1581000 164200 165000 208100 44430 135800 6361 119100 12630 1383000 228400 105000 1004000 5867 16430 112000
2020-12-27 1587000 10910 153900 7540000 598800 1592000 164600 166000 208400 44650 136000 6394 119500 12700 1391000 230700 105700 1006000 5904 17010 112300
2020-12-28 1592000 11070 154200 7559000 600600 1603000 166100 166700 208600 44880 136300 6427 119900 12770 1398000 232700 106400 1008000 5942 17580 112600
2020-12-29 1598000 11210 154600 7604000 602000 1613000 166900 167400 208900 45080 136900 6460 120200 12840 1408000 234900 107300 1009000 5980 18170 113000
2020-12-30 1604000 11370 154900 7661000 603200 1625000 167800 168100 209300 45280 137400 6494 120600 12910 1419000 237400 108100 1010000 6017 18810 113300

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 Confirmed