JHU CSSE COVID-19 Daily Updates
Dataset published by xristosk
Pull with Qri Desktop
Use this dataset reference to quickly pull down the latest version of this dataset in Qri Desktop
Pull with Qri CLI
Pull this version of the dataset from your terminal
Global daily updates (aggregated)
25 commits1,667,487 rows243.8MBcsv
HEAD 29um1dR updated structure, readme, and body
4 days ago
Readme
JHU CSSE COVID-19 Daily Reports (aggregated)
Taken from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, this dataset is an aggregation of all the CSV files in the daily updates folder.
Meta
Standard Metadata
| description | Global daily updates (aggregated) |
| keywords | covid-19 |
| title | JHU CSSE COVID-19 Daily Updates |
Additional Metadata
| describe | Global daily updates (aggregated) |
Structure
CSV
232.48 MB
1,667,487
13,339,896
2
CSV Configuration
This table contains a header row
This table has rows with different lengths
This table uses different quotes to indicate strings
Schema
| title | type | description |
|---|---|---|
| fips | string | |
| admin2 | string | |
| confirmed | number | |
| deaths | number | |
| recovered | number | |
| active | number | |
| combined_key | string | |
| incidence_rate | number | |
| case-fatality_ratio | number | |
| lat | number | |
| long_ | number | |
| province_state | string | |
| country_region | string | |
| last_update | string |
Body Preview
| qri-row-number | fips | admin2 | confirmed | deaths | recovered | active | combined_key | incidence_rate | case-fatality_ratio | lat | long_ | province_state | country_region | last_update |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 56517.0 | 2489.0 | 51788.0 | 2240.0 | Afghanistan | 145.18214377540517 | 4.403984641789196 | 33.93911 | 67.709953 | nan | ||||
| 2 | 125506.0 | 2241.0 | 91875.0 | 31390.0 | Albania | 4361.178678156925 | 1.78557200452568 | 41.1533 | 20.1683 | nan | ||||
| 3 | 117304.0 | 3096.0 | 81632.0 | 32576.0 | Algeria | 267.5056098437613 | 2.6392961876832843 | 28.0339 | 1.6596 | nan | ||||
| 4 | 12053.0 | 115.0 | 11365.0 | 573.0 | Andorra | 15599.5599559956 | 0.9541193063967476 | 42.5063 | 1.5218 | nan | ||||
| 5 | 22399.0 | 538.0 | 20508.0 | 1353.0 | Angola | 68.1519422892797 | 2.4018929416491805 | -11.2027 | 17.8739 | nan | ||||
| 6 | 1147.0 | 28.0 | 857.0 | 262.0 | Antigua and Barbuda | 1171.2686871987582 | 2.4411508282476024 | 17.0608 | -61.7964 | nan | ||||
| 7 | 2363251.0 | 55941.0 | 2112266.0 | 195044.0 | Argentina | 5228.919949755483 | 2.367120547076887 | -38.4161 | -63.6167 | nan | ||||
| 8 | 193736.0 | 3533.0 | 173681.0 | 16522.0 | Armenia | 6537.991937187547 | 1.8236156419044476 | 40.0691 | 45.0382 | nan | ||||
| 9 | 123.0 | 3.0 | 120.0 | 0.0 | Australian Capital Territory, Australia | 28.73160476524177 | 2.4390243902439024 | -35.4735 | 149.0124 | Australian Capital Territory | ||||
| 10 | 5296.0 | 54.0 | 0.0 | 5242.0 | New South Wales, Australia | 65.23774328652378 | 1.0196374622356494 | -33.8688 | 151.2093 | New South Wales | ||||
| 11 | 109.0 | 0.0 | 106.0 | 3.0 | Northern Territory, Australia | 44.38110749185668 | 0.0 | -12.4634 | 130.8456 | Northern Territory | ||||
| 12 | 1485.0 | 6.0 | 1344.0 | 135.0 | Queensland, Australia | 29.029420389013787 | 0.404040404040404 | -27.4698 | 153.0251 | Queensland | ||||
| 13 | 658.0 | 4.0 | 641.0 | 13.0 | South Australia, Australia | 37.460859664104746 | 0.60790273556231 | -34.9285 | 138.6007 | South Australia | ||||
| 14 | 234.0 | 13.0 | 221.0 | 0.0 | Tasmania, Australia | 43.69747899159664 | 5.555555555555555 | -42.8821 | 147.3272 | Tasmania | ||||
| 15 | 20484.0 | 820.0 | 19663.0 | 1.0 | Victoria, Australia | 308.96393610763363 | 4.003124389767623 | -37.8136 | 144.9631 | Victoria | ||||
| 16 | 944.0 | 9.0 | 919.0 | 16.0 | Western Australia, Australia | 35.88534934995818 | 0.9533898305084746 | -31.9505 | 115.8605 | Western Australia | ||||
| 17 | 549592.0 | 9368.0 | 503729.0 | 36495.0 | Austria | 6102.238408243027 | 1.7045371839473646 | 47.5162 | 14.5501 | nan | ||||
| 18 | 263961.0 | 3593.0 | 238985.0 | 21383.0 | Azerbaijan | 2603.377493730999 | 1.3611859327703717 | 40.1431 | 47.5769 | nan | ||||
| 19 | 9171.0 | 188.0 | 8676.0 | 307.0 | Bahamas | 2332.116120107413 | 2.0499400283502345 | 25.025885 | -78.035889 | nan | ||||
| 20 | 145380.0 | 523.0 | 136064.0 | 8793.0 | Bahrain | 8543.808912054246 | 0.3597468702710139 | 26.0275 | 50.55 | nan | ||||
| 21 | 617764.0 | 9105.0 | 544938.0 | 63721.0 | Bangladesh | 375.1085763676703 | 1.4738638055956643 | 23.685 | 90.3563 | nan | ||||
| 22 | 3659.0 | 42.0 | 3506.0 | 111.0 | Barbados | 1273.2669615236057 | 1.147854605083356 | 13.1939 | -59.5432 | nan | ||||
| 23 | 323043.0 | 2257.0 | 313628.0 | 7158.0 | Belarus | 3418.690083657863 | 0.6986685982980594 | 53.7098 | 27.9534 | nan | ||||
| 24 | 112704.0 | 0.0 | 0.0 | 112704.0 | Antwerp, Belgium | 6065.922994037629 | 0.0 | 51.2195 | 4.4024 | Antwerp | ||||
| 25 | 110226.0 | 0.0 | 0.0 | 110226.0 | Brussels, Belgium | 9120.576694893516 | 0.0 | 50.8503 | 4.3517 | Brussels | ||||
| 26 | 100629.0 | 0.0 | 0.0 | 100629.0 | East Flanders, Belgium | 6641.897636007456 | 0.0 | 51.0362 | 3.7373 | East Flanders | ||||
| 27 | 68473.0 | 0.0 | 0.0 | 68473.0 | Flemish Brabant, Belgium | 5974.04410321286 | 0.0 | 50.9167 | 4.5833 | Flemish Brabant | ||||
| 28 | 132025.0 | 0.0 | 0.0 | 132025.0 | Hainaut, Belgium | 9821.52753858869 | 0.0 | 50.5257 | 4.0621 | Hainaut | ||||
| 29 | 112600.0 | 0.0 | 0.0 | 112600.0 | Liege, Belgium | 10171.708557966092 | 0.0 | 50.4496 | 5.8492 | Liege | ||||
| 30 | 45802.0 | 0.0 | 0.0 | 45802.0 | Limburg, Belgium | 5240.215640330966 | 0.0 | 50.9739 | 5.3420000000000005 | Limburg | ||||
| 31 | 25410.0 | 0.0 | 0.0 | 25410.0 | Luxembourg, Belgium | 8927.128493033257 | 0.0 | 50.0547 | 5.4677 | Luxembourg | ||||
| 32 | 48679.0 | 0.0 | 0.0 | 48679.0 | Namur, Belgium | 9847.569918575837 | 0.0 | 50.331 | 4.8221 | Namur | ||||
| 33 | 34323.0 | 0.0 | 0.0 | 34323.0 | Walloon Brabant, Belgium | 8504.233162123792 | 0.0 | 50.4 | 4.35 | Walloon Brabant | ||||
| 34 | 80420.0 | 0.0 | 0.0 | 80420.0 | West Flanders, Belgium | 6725.22737992099 | 0.0 | 51.0536 | 3.1458 | West Flanders | ||||
| 35 | 12456.0 | 317.0 | 12090.0 | 49.0 | Belize | 3132.631324804273 | 2.5449582530507384 | 17.1899 | -88.4976 | nan | ||||
| 36 | 7313.0 | 93.0 | 6452.0 | 768.0 | Benin | 60.32236708498864 | 1.271707917407357 | 9.3077 | 2.3158 | nan | ||||
| 37 | 886.0 | 1.0 | 869.0 | 16.0 | Bhutan | 114.82454912572642 | 0.1128668171557562 | 27.5142 | 90.4336 | nan | ||||
| 38 | 273327.0 | 12280.0 | 222924.0 | 38123.0 | Bolivia | 2341.525922706095 | 4.492787027992112 | -16.2902 | -63.5887 | nan | ||||
| 39 | 171324.0 | 6675.0 | 131166.0 | 33483.0 | Bosnia and Herzegovina | 5221.9951444991575 | 3.8961266372487215 | 43.9159 | 17.6791 | nan | ||||
| 40 | 40878.0 | 591.0 | 36958.0 | 3329.0 | Botswana | 1738.2873544889171 | 1.4457654484074562 | -22.3285 | 24.6849 | nan | ||||
| 41 | 69988.0 | 1269.0 | 54045.0 | 14674.0 | Acre, Brazil | 7935.732225163985 | 1.8131679716522835 | -9.0238 | -70.812 | Acre | ||||
| 42 | 154199.0 | 3576.0 | 146077.0 | 4546.0 | Alagoas, Brazil | 4620.3927239429295 | 2.3190811872969346 | -9.5713 | -36.782 | Alagoas | ||||
| 43 | 98044.0 | 1311.0 | 72266.0 | 24467.0 | Amapa, Brazil | 11592.811425855269 | 1.3371547468483538 | 0.902 | -52.003 | Amapa | ||||
| 44 | 350288.0 | 12031.0 | 298476.0 | 39781.0 | Amazonas, Brazil | 8451.678172811495 | 3.4346023843237563 | -3.4168 | -65.8561 | Amazonas | ||||
| 45 | 808461.0 | 15472.0 | 776831.0 | 16158.0 | Bahia, Brazil | 5435.739401107936 | 1.9137596000301809 | -12.5797 | -41.7007 | Bahia | ||||
| 46 | 541227.0 | 14167.0 | 383785.0 | 143275.0 | Ceara, Brazil | 5926.657656669161 | 2.617570815942294 | -5.4984 | -39.3206 | Ceara | ||||
| 47 | 345682.0 | 6150.0 | 321374.0 | 18158.0 | Distrito Federal, Brazil | 11464.387245180194 | 1.7790917664211616 | -15.7998 | -47.8645 | Distrito Federal | ||||
| 48 | 384362.0 | 7553.0 | 352521.0 | 24288.0 | Espirito Santo, Brazil | 9564.455725181342 | 1.9650745911406435 | -19.1834 | -40.3089 | Espirito Santo | ||||
| 49 | 486646.0 | 11725.0 | 462167.0 | 12754.0 | Goias, Brazil | 6933.905015335506 | 2.4093488901583493 | -15.827 | -49.8362 | Goias | ||||
| 50 | 243720.0 | 6108.0 | 220903.0 | 16709.0 | Maranhao, Brazil | 3444.717527367852 | 2.5061546036435254 | -4.9609 | -45.2744 | Maranhao | ||||
| 51 | 310773.0 | 7629.0 | 286889.0 | 16255.0 | Mato Grosso, Brazil | 8918.812810915646 | 2.4548464634958638 | -12.6819 | -56.9211 | Mato Grosso | ||||
| 52 | 217457.0 | 4334.0 | 196702.0 | 16421.0 | Mato Grosso do Sul, Brazil | 7825.048416940566 | 1.9930377040058491 | -20.7722 | -54.7852 | Mato Grosso do Sul | ||||
| 53 | 1135856.0 | 24728.0 | 1016078.0 | 95050.0 | Minas Gerais, Brazil | 5365.710304381578 | 2.177036525756786 | -18.5122 | -44.555 | Minas Gerais | ||||
| 54 | 420372.0 | 10503.0 | 391866.0 | 18003.0 | Para, Brazil | 4886.418652390802 | 2.498501327395735 | -1.9981 | -54.9306 | Para | ||||
| 55 | 260484.0 | 5791.0 | 186084.0 | 68609.0 | Paraiba, Brazil | 6482.721924916757 | 2.223169177377497 | -7.24 | -36.782 | Paraiba | ||||
| 56 | 851339.0 | 16941.0 | 598367.0 | 236031.0 | Parana, Brazil | 7445.707553386812 | 1.9899241077878496 | -25.2521 | -52.0215 | Parana | ||||
| 57 | 352218.0 | 12249.0 | 298206.0 | 41763.0 | Pernambuco, Brazil | 3685.417844023551 | 3.4776757576273787 | -8.8137 | -36.9541 | Pernambuco | ||||
| 58 | 206631.0 | 4141.0 | 201169.0 | 1321.0 | Piaui, Brazil | 6312.761076454522 | 2.0040555386171484 | -7.7183 | -42.7289 | Piaui | ||||
| 59 | 197076.0 | 4548.0 | 142776.0 | 49752.0 | Rio Grande do Norte, Brazil | 5619.739407383201 | 2.3077391463191863 | -5.4026 | -36.9541 | Rio Grande do Norte | ||||
| 60 | 850220.0 | 20063.0 | 808641.0 | 21516.0 | Rio Grande do Sul, Brazil | 7472.990591126722 | 2.3597421843758086 | -30.0346 | -51.2177 | Rio Grande do Sul | ||||
| 61 | 651914.0 | 37114.0 | 605442.0 | 9358.0 | Rio de Janeiro, Brazil | 3775.940644576701 | 5.693082216365962 | -22.9068 | -43.1729 | Rio de Janeiro | ||||
| 62 | 188336.0 | 4196.0 | 164607.0 | 19533.0 | Rondonia, Brazil | 10597.19506534063 | 2.227933055815139 | -11.5057 | -63.5806 | Rondonia | ||||
| 63 | 89853.0 | 1346.0 | 84118.0 | 4389.0 | Roraima, Brazil | 14833.077731976802 | 1.4980022926335237 | -2.7376 | -62.0751 | Roraima | ||||
| 64 | 810787.0 | 11023.0 | 769659.0 | 30105.0 | Santa Catarina, Brazil | 11316.273419394964 | 1.359543258587027 | -27.2423 | -50.2189 | Santa Catarina | ||||
| 65 | 2496416.0 | 75734.0 | 2120478.0 | 300204.0 | Sao Paulo, Brazil | 5436.558583780775 | 3.0337091254021766 | -23.5505 | -46.6333 | Sao Paulo | ||||
| 66 | 175582.0 | 3525.0 | 158658.0 | 13399.0 | Sergipe, Brazil | 7638.330601349635 | 2.007608980419405 | -10.5741 | -37.3857 | Sergipe | ||||
| 67 | 141913.0 | 2057.0 | 122028.0 | 17828.0 | Tocantins, Brazil | 9022.574078147789 | 1.4494796107474297 | -10.1753 | -48.2982 | Tocantins | ||||
| 68 | 213.0 | 3.0 | 195.0 | 15.0 | Brunei | 48.68760614698172 | 1.408450704225352 | 4.5353 | 114.7277 | nan | ||||
| 69 | 346327.0 | 13313.0 | 262095.0 | 70919.0 | Bulgaria | 4984.237480472249 | 3.844054896095309 | 42.7339 | 25.4858 | nan | ||||
| 70 | 12774.0 | 149.0 | 12420.0 | 205.0 | Burkina Faso | 61.110032598714895 | 1.1664318146234538 | 12.2383 | -1.5616 | nan | ||||
| 71 | 142466.0 | 3206.0 | 131802.0 | 7458.0 | Burma | 261.8388887853536 | 2.25036148975896 | 21.9162 | 95.956 | nan | ||||
| 72 | 2842.0 | 6.0 | 773.0 | 2063.0 | Burundi | 23.90086908505001 | 0.211118930330753 | -3.3731 | 29.9189 | nan | ||||
| 73 | 17587.0 | 169.0 | 16340.0 | 1078.0 | Cabo Verde | 3163.1977668582776 | 0.9609370557798372 | 16.5388 | -23.0418 | nan | ||||
| 74 | 2477.0 | 14.0 | 1240.0 | 1223.0 | Cambodia | 14.815505092986882 | 0.5651998385143319 | 11.55 | 104.9167 | nan | ||||
| 75 | 57337.0 | 851.0 | 35261.0 | 21225.0 | Cameroon | 215.9922163392384 | 1.4842074053403562 | 3.848 | 11.5021 | nan | ||||
| 76 | 149207.0 | 1994.0 | 138560.0 | 8653.0 | Alberta, Canada | 3380.9667751758043 | 1.3363984263472892 | 53.9333 | -116.5765 | Alberta | ||||
| 77 | 100880.0 | 1463.0 | 91732.0 | 7685.0 | British Columbia, Canada | 1973.8140924612944 | 1.4502379064234734 | 53.7267 | -127.6476 | British Columbia | ||||
| 78 | 34174.0 | 937.0 | 32058.0 | 1179.0 | Manitoba, Canada | 2480.840526831974 | 2.7418505296424183 | 53.7609 | -98.8139 | Manitoba | ||||
| 79 | 1623.0 | 30.0 | 1451.0 | 142.0 | New Brunswick, Canada | 208.0787904506836 | 1.8484288354898335 | 46.5653 | -66.4619 | New Brunswick | ||||
| 80 | 1020.0 | 6.0 | 1006.0 | 8.0 | Newfoundland and Labrador, Canada | 195.6402903915683 | 0.5882352941176471 | 53.1355 | -57.6604 | Newfoundland and Labrador | ||||
| 81 | 47.0 | 0.0 | 47.0 | 0.0 | Northwest Territories,Canada | 104.6677356137538 | 0.0 | 64.8255 | -124.8457 | Northwest Territories | ||||
| 82 | 1719.0 | 66.0 | 1629.0 | 24.0 | Nova Scotia, Canada | 175.86451373308492 | 3.8394415357766154 | 44.682 | -63.7443 | Nova Scotia | ||||
| 83 | 395.0 | 4.0 | 391.0 | 0.0 | Nunavut, Canada | 1018.5662712738524 | 1.0126582278481011 | 70.2998 | -83.1076 | Nunavut | ||||
| 84 | 359074.0 | 7384.0 | 331464.0 | 20226.0 | Ontario, Canada | 2440.7165744947924 | 2.0564006305107023 | 51.2538 | -85.3232 | Ontario | ||||
| 85 | 159.0 | 0.0 | 145.0 | 14.0 | Prince Edward Island, Canada | 100.53237901339168 | 0.0 | 46.5107 | -63.4168 | Prince Edward Island | ||||
| 86 | 312362.0 | 10676.0 | 292648.0 | 9038.0 | Quebec, Canada | 3658.631144735674 | 3.417829313424809 | 52.9399 | -73.5491 | Quebec | ||||
| 87 | 33789.0 | 436.0 | 31404.0 | 1949.0 | Saskatchewan, Canada | 2859.4374383285967 | 1.2903607682973748 | 52.9399 | -106.4509 | Saskatchewan | ||||
| 88 | 74.0 | 1.0 | 71.0 | 2.0 | Yukon, Canada | 180.1450898291056 | 1.351351351351351 | 64.2823 | -135.0 | Yukon | ||||
| 89 | 5222.0 | 72.0 | 4957.0 | 193.0 | Central African Republic | 108.12122497082672 | 1.3787820758330145 | 6.6111 | 20.9394 | nan | ||||
| 90 | 4552.0 | 164.0 | 4149.0 | 239.0 | Chad | 27.712401524936983 | 3.602811950790861 | 15.4542 | 18.7322 | nan | ||||
| 91 | 43473.0 | 874.0 | 41317.0 | 1282.0 | Antofagasta, Chile | 7155.648901954459 | 2.0104432636349 | -23.6509 | -70.3975 | Antofagasta | ||||
| 92 | 55409.0 | 688.0 | 51691.0 | 3030.0 | Araucania, Chile | 5788.509272646737 | 1.241675540074717 | -38.9489 | -72.3311 | Araucania | ||||
| 93 | 17547.0 | 352.0 | 16558.0 | 637.0 | Arica y Parinacota, Chile | 7761.82387600191 | 2.006040918675557 | -18.594 | -69.4785 | Arica y Parinacota | ||||
| 94 | 14337.0 | 172.0 | 13648.0 | 517.0 | Atacama, Chile | 4961.861121878288 | 1.1996931017646648 | -27.5661 | -70.0503 | Atacama | ||||
| 95 | 3563.0 | 28.0 | 3401.0 | 134.0 | Aysen, Chile | 3453.9250470152588 | 0.7858546168958742 | -45.9864 | -73.7669 | Aysen | ||||
| 96 | 96422.0 | 1598.0 | 89745.0 | 5079.0 | Biobio, Chile | 6193.582369018599 | 1.6572981269834686 | -37.4464 | -72.1416 | Biobio | ||||
| 97 | 27060.0 | 518.0 | 25435.0 | 1107.0 | Coquimbo, Chile | 3571.871708294504 | 1.9142645971914265 | -29.959 | -71.3389 | Coquimbo | ||||
| 98 | 61246.0 | 807.0 | 58656.0 | 1783.0 | Los Lagos, Chile | 7390.54045574557 | 1.3176370701760114 | -41.9198 | -72.1416 | Los Lagos | ||||
| 99 | 27740.0 | 311.0 | 25759.0 | 1670.0 | Los Rios, Chile | 7208.246608304295 | 1.1211247296323 | -40.231 | -72.3311 | Los Rios | ||||
| 100 | 23136.0 | 349.0 | 22509.0 | 278.0 | Magallanes, Chile | 13892.741979067212 | 1.508471645919779 | -52.368 | -70.9863 | Magallanes |
Previewing 100 of 1,667,487 rows
Use the dataset reference to create a copy of this Qri Dataset on your computer
