COVID-19 short-term forecasts Confirmed 2021-03-01 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 2021-03-01

ArgentinaBahamasBarbadosBelizeBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaGuyanaHaitiHondurasJamaicaMexicoNicaraguaPanamaParaguayPeruSurinameTrinidad and TobagoUruguayVenezuela
Peak date (mm-dd)2021-01-1410-172021-02-1812-032021-01-292021-01-202021-01-222021-01-1609-142021-01-182021-01-162021-01-1407-182021-01-292021-01-232021-02-03 --2021-01-2005-262021-01-07 --2021-02-202021-01-1011-22 --09-08
Peak daily increment 11293 104 92 1122 2152 54453 4180 17013 1226 1589 1869 313 2590 55 63 1374 16981 177 3354 6967 81 55 1085
Days since peak 46 135 11 88 31 40 38 44 168 42 44 46 226 31 37 26 40 279 53 9 50 99 174
Last total 2112023 8519 3115 12313 249767 10587001 829770 2255260 205086 239998 286367 59866 174653 8595 12448 170985 23599 2089281 6445 341420 160448 1329805 8933 7716 58589 139545
Last daily increment 4658 0 47 20 757 35742 5145 3570 745 381 212 0 111 10 18 681 336 2343 0 505 974 5942 4 3 595 429
Last week 34795 42 263 49 5387 329126 24453 21671 1989 3788 10587 631 2581 143 139 4438 1773 37015 0 3615 6658 43048 53 30 4616 2559
Previous peak date10-19 -- -- --07-1708-0406-06 -- --07-2604-2408-05 --09-2106-0406-2809-2210-05 -- -- --08-0208-1409-19 -- --
Previous peak daily increment 14378 1578 45272 7349 1405 7778 420 66 177 795 160 22833 8380 89 119
Low between peaks 5479 93 19229 1343 400 -4305 90 13 6 305 4599 1490 1 23

Confirmed count forecast Latin America (bold red line in graphs) 2021-03-02 to 2021-03-08

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-01 2112023 3115 249767 10587001 829770 2255260 205086 239998 286367 59866 174653 170985 23599 2089281 341420 160448 1329805 58589 139545
2021-03-02 2118000 3115 249800 10621000 833200 2258000 205500 240600 286400 60170 175700 171700 23890 2102000 342100 161900 1335000 59490 140000
2021-03-03 2126000 3217 250300 10670000 836300 2261000 205900 241900 287900 60400 176500 172500 24120 2112000 342900 163100 1341000 60390 140400
2021-03-04 2133000 3297 250700 10723000 841100 2264000 206300 242800 289700 60580 177100 173300 24380 2120000 343600 164500 1348000 61270 140800
2021-03-05 2138000 3366 251200 10774000 845900 2266000 206700 243700 290800 60730 177800 174000 24650 2129000 344100 165900 1360000 62120 141100
2021-03-06 2143000 3434 251400 10820000 850700 2269000 206800 244400 292200 60890 178500 174700 24920 2137000 344800 167100 1367000 62970 141500
2021-03-07 2146000 3496 251400 10852000 854600 2271000 206800 245200 293800 61040 178800 175400 25200 2139000 345200 168200 1372000 63830 141900
2021-03-08 2150000 3557 251900 10880000 858400 2274000 207500 245800 294000 61180 178800 176000 25480 2139000 345600 169000 1375000 64680 142300

Confirmed count average forecast Latin America (bold black line in graphs) 2021-03-02 to 2021-03-08

DateArgentinaBarbadosBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasJamaicaMexicoPanamaParaguayPeruUruguayVenezuela
2021-03-01 2112023 3115 249767 10587001 829770 2255260 205086 239998 286367 59866 174653 170985 23599 2089281 341420 160448 1329805 58589 139545
2021-03-02 2117000 3183 250400 10651000 832500 2259000 205400 240400 287100 59880 175200 171600 23890 2096000 341900 161500 1334000 59200 139900
2021-03-03 2124000 3278 251100 10711000 834900 2262000 205700 241000 288200 59980 175700 172200 24120 2103000 342400 162600 1340000 59830 140200
2021-03-04 2130000 3359 251700 10768000 838900 2265000 206000 241600 289400 60100 176200 172800 24370 2109000 342700 163800 1346000 60480 140600
2021-03-05 2135000 3433 252300 10824000 843100 2268000 206300 242200 290300 60370 176700 173400 24610 2115000 343100 164900 1355000 61130 140900
2021-03-06 2141000 3503 252800 10870000 846800 2271000 206400 242900 291200 60460 177200 174000 24890 2122000 343500 165900 1361000 61810 141200
2021-03-07 2145000 3571 253000 10900000 850200 2275000 206500 243600 292100 60540 177500 174600 25210 2126000 343800 166900 1368000 62460 141400
2021-03-08 2150000 3639 253400 10921000 853700 2278000 207000 244100 292400 60820 177700 175100 25480 2129000 344100 167700 1371000 63100 141700

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