COVID-19 short-term forecasts Confirmed 2021-04-20 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 2021-04-20

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd) --10-172021-02-1812-032021-01-222021-03-202021-04-10 --09-142021-01-182021-01-162021-04-1107-18 --2021-01-232021-02-032021-03-182021-01-2005-262021-01-07 --2021-04-092021-01-10 --2021-04-09 --
Peak daily increment 104 106 1122 2113 75325 7111 1226 1589 1869 748 2590 63 1356 686 16981 177 3354 8680 81 5033
Days since peak 185 61 138 88 31 10 218 92 94 9 276 87 76 33 90 329 103 11 100 11
Last total 2743620 9791 3793 12568 291675 14043076 1136189 2684101 231967 261848 361154 67851 214700 11972 12918 202413 44254 2311172 6835 361319 255046 1719088 9687 9135 169327 185736
Last daily increment 29145 0 9 10 1313 69381 4849 16965 1130 317 591 294 1651 71 0 736 118 4262 57 275 2603 11301 106 171 2294 1141
Last week 139463 286 49 46 6384 369569 41922 98300 6624 2588 10615 895 7573 598 63 4199 1200 19926 57 1803 12885 51351 270 539 17238 7642
Previous peak date10-21 -- -- --07-1708-0406-062021-01-16 --07-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-0208-1409-18 --09-05
Previous peak daily increment 14882 1578 45270 7349 17013 1405 7778 420 77 177 795 160 22833 8380 89 117 1106
Low between peaks 93 19229 1343 400 -4305 52 6 305 50 4599 1490 1

Confirmed count forecast Latin America (bold red line in graphs) 2021-04-21 to 2021-04-27

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-20 2743620 9791 291675 14043076 1136189 2684101 231967 261848 361154 67851 214700 11972 202413 44254 2311172 361319 255046 1719088 9687 9135 169327 185736
2021-04-21 2773000 9790 292100 14123000 1142000 2700000 232000 262700 362800 67990 214700 12100 203100 44330 2316000 361700 258300 1725000 9770 9135 169300 186900
2021-04-22 2811000 9800 292700 14200000 1150000 2716000 232700 263300 366000 68120 215200 12260 203400 44470 2320000 362100 261000 1730000 9810 9240 173000 188300
2021-04-23 2845000 9880 293700 14281000 1157000 2732000 233700 263900 366600 68250 216200 12400 203900 44580 2324000 362500 263000 1741000 9870 9345 176200 189500
2021-04-24 2876000 9880 293900 14340000 1165000 2747000 233700 264400 368300 68380 217100 12520 204400 44670 2328000 362800 265900 1754000 9930 9443 179100 190500
2021-04-25 2907000 9880 295000 14378000 1172000 2763000 233700 265000 370300 68520 217500 12640 205000 44760 2330000 363000 267700 1757000 9990 9541 182000 191400
2021-04-26 2936000 9990 296100 14400000 1178000 2778000 234800 265400 370400 68660 217700 12750 205600 44840 2331000 363200 269900 1764000 10060 9639 184700 192300
2021-04-27 2966000 10010 297200 14471000 1182000 2794000 236100 265600 371000 68790 219100 12860 206200 44910 2335000 363500 272000 1772000 10120 9736 187400 193100

Confirmed count average forecast Latin America (bold black line in graphs) 2021-04-21 to 2021-04-27

DateArgentinaBahamasBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHondurasJamaicaMexicoPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
2021-04-20 2743620 9791 291675 14043076 1136189 2684101 231967 261848 361154 67851 214700 11972 202413 44254 2311172 361319 255046 1719088 9687 9135 169327 185736
2021-04-21 2769000 9826 292800 14115000 1141000 2701000 232500 262300 362600 68020 215800 12050 203100 44390 2315000 361600 257400 1728000 9733 9214 171900 187000
2021-04-22 2793000 9847 293300 14187000 1149000 2717000 233000 262700 364700 68140 216800 12130 203600 44580 2319000 361800 259600 1734000 9758 9269 175600 188100
2021-04-23 2816000 9881 294100 14265000 1157000 2733000 233600 263100 365400 68270 217900 12210 204200 44770 2323000 362100 261200 1743000 9784 9325 179000 189100
2021-04-24 2838000 9905 294300 14323000 1164000 2749000 233900 263500 366600 68390 218900 12290 204800 44960 2327000 362200 263600 1754000 9811 9381 182100 190200
2021-04-25 2861000 9931 294900 14359000 1172000 2765000 234200 263900 367800 68520 219500 12370 205400 45140 2329000 362400 265300 1758000 9838 9438 185700 191100
2021-04-26 2884000 9971 295400 14389000 1178000 2781000 234700 264300 368200 68650 220200 12460 206000 45330 2331000 362500 267200 1765000 9865 9495 188700 192200
2021-04-27 2906000 10000 296100 14464000 1183000 2797000 235800 264700 368800 68770 221100 12540 206600 45480 2335000 362700 269300 1771000 9893 9553 191700 193100

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