JHU CSSE COVID-19 Daily Updates
Dataset published by xristosk
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Global daily updates (aggregated)
25 commits906,923 rows129.1MBcsv
HEAD K3NGJHh updated structure, readme, and body
a day 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
123.09 MB
906,923
7,255,384
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 | 37894.0 | 1385.0 | 28016.0 | 8493.0 | Afghanistan | 97.34296151998873 | 3.6549321792368175 | 33.93911 | 67.709953 | nan | ||||
| 2 | 8119.0 | 240.0 | 4096.0 | 3783.0 | Albania | 282.1252345541733 | 2.9560290676191654 | 41.1533 | 20.1683 | nan | ||||
| 3 | 40667.0 | 1418.0 | 28587.0 | 10662.0 | Algeria | 92.73895720108642 | 3.4868566651093023 | 28.0339 | 1.6596 | nan | ||||
| 4 | 1045.0 | 53.0 | 875.0 | 117.0 | Andorra | 1352.48818999547 | 5.071770334928231 | 42.5063 | 1.5218 | nan | ||||
| 5 | 2068.0 | 94.0 | 804.0 | 1170.0 | Angola | 6.292165572312622 | 4.545454545454546 | -11.2027 | 17.8739 | nan | ||||
| 6 | 94.0 | 3.0 | 89.0 | 2.0 | Antigua and Barbuda | 95.98888979658524 | 3.191489361702128 | 17.0608 | -61.7964 | nan | ||||
| 7 | 329043.0 | 6730.0 | 239806.0 | 82507.0 | Argentina | 728.0392590661734 | 2.045325383004653 | -38.4161 | -63.6167 | nan | ||||
| 8 | 42477.0 | 842.0 | 35693.0 | 5942.0 | Armenia | 1433.467623549136 | 1.9822492172234385 | 40.0691 | 45.0382 | nan | ||||
| 9 | 113.0 | 3.0 | 110.0 | 0.0 | Australian Capital Territory, Australia | 26.395701938799345 | 2.6548672566371683 | -35.4735 | 149.0124 | Australian Capital Territory | ||||
| 10 | 3981.0 | 52.0 | 2994.0 | 935.0 | New South Wales, Australia | 49.03917220990392 | 1.3062044712383822 | -33.8688 | 151.2093 | New South Wales | ||||
| 11 | 33.0 | 0.0 | 33.0 | 0.0 | Northern Territory, Australia | 13.436482084690551 | 0.0 | -12.4634 | 130.8456 | Northern Territory | ||||
| 12 | 1103.0 | 6.0 | 1082.0 | 15.0 | Queensland, Australia | 21.56191965594761 | 0.5439709882139621 | -27.4698 | 153.0251 | Queensland | ||||
| 13 | 462.0 | 4.0 | 452.0 | 6.0 | South Australia, Australia | 26.302305721605464 | 0.8658008658008658 | -34.9285 | 138.6007 | South Australia | ||||
| 14 | 230.0 | 13.0 | 216.0 | 1.0 | Tasmania, Australia | 42.95051353874883 | 5.652173913043479 | -42.8821 | 147.3272 | Tasmania | ||||
| 15 | 18029.0 | 398.0 | 12940.0 | 4691.0 | Victoria, Australia | 271.9347199806935 | 2.2075544955349717 | -37.8136 | 144.9631 | Victoria | ||||
| 16 | 651.0 | 9.0 | 633.0 | 9.0 | Western Australia, Australia | 24.74720596061735 | 1.3824884792626728 | -31.9505 | 115.8605 | Western Australia | ||||
| 17 | 24762.0 | 730.0 | 21260.0 | 2772.0 | Austria | 274.93782199324926 | 2.9480655843631367 | 47.5162 | 14.5501 | nan | ||||
| 18 | 34921.0 | 512.0 | 32682.0 | 1727.0 | Azerbaijan | 344.416582216995 | 1.4661664900776037 | 40.1431 | 47.5769 | nan | ||||
| 19 | 1703.0 | 27.0 | 221.0 | 1455.0 | Bahamas | 433.06005370656686 | 1.5854374633000587 | 25.025885 | -78.035889 | nan | ||||
| 20 | 48661.0 | 181.0 | 45166.0 | 3314.0 | Bahrain | 2859.7488338799812 | 0.3719611187604036 | 26.0275 | 50.55 | nan | ||||
| 21 | 290360.0 | 3861.0 | 172615.0 | 113884.0 | Bangladesh | 176.30766155702946 | 1.329728612756578 | 23.685 | 90.3563 | nan | ||||
| 22 | 157.0 | 7.0 | 124.0 | 26.0 | Barbados | 54.63320933566713 | 4.4585987261146505 | 13.1939 | -59.5432 | nan | ||||
| 23 | 70111.0 | 632.0 | 68256.0 | 1223.0 | Belarus | 741.9686557372746 | 0.9014277360185992 | 53.7098 | 27.9534 | nan | ||||
| 24 | 80894.0 | 9985.0 | 18165.0 | 52744.0 | Belgium | 697.9868875724617 | 12.343313471950948 | 50.8333 | 4.469936 | nan | ||||
| 25 | 648.0 | 5.0 | 43.0 | 600.0 | Belize | 162.96925967189858 | 0.7716049382716049 | 17.1899 | -88.4976 | nan | ||||
| 26 | 2095.0 | 39.0 | 1705.0 | 351.0 | Benin | 17.28091878067157 | 1.8615751789976132 | 9.3077 | 2.3158 | nan | ||||
| 27 | 154.0 | 0.0 | 110.0 | 44.0 | Bhutan | 19.958217342394878 | 0.0 | 27.5142 | 90.4336 | nan | ||||
| 28 | 107435.0 | 4366.0 | 42141.0 | 60928.0 | Bolivia | 920.3695116323278 | 4.063852562014241 | -16.2902 | -63.5887 | nan | ||||
| 29 | 17396.0 | 526.0 | 11157.0 | 5713.0 | Bosnia and Herzegovina | 530.2341034163767 | 3.023683605426535 | 43.9159 | 17.6791 | nan | ||||
| 30 | 1308.0 | 3.0 | 136.0 | 1169.0 | Botswana | 55.621113060118006 | 0.22935779816513766 | -22.3285 | 24.6849 | nan | ||||
| 31 | 23337.0 | 598.0 | 18601.0 | 4138.0 | Acre, Brazil | 2646.113375702291 | 2.5624544714402018 | -9.0238 | -70.812 | Acre | ||||
| 32 | 74839.0 | 1802.0 | 68736.0 | 4301.0 | Alagoas, Brazil | 2242.463122764511 | 2.4078354868450944 | -9.5713 | -36.782 | Alagoas | ||||
| 33 | 40875.0 | 627.0 | 28684.0 | 11564.0 | Amapa, Brazil | 4833.097048588736 | 1.5339449541284405 | 0.902 | -52.003 | Amapa | ||||
| 34 | 114792.0 | 3556.0 | 97012.0 | 14224.0 | Amazonas, Brazil | 2769.6782099683032 | 3.0977768485608754 | -3.4168 | -65.8561 | Amazonas | ||||
| 35 | 229743.0 | 4757.0 | 208932.0 | 16054.0 | Bahia, Brazil | 1544.6917998873666 | 2.070574511519393 | -12.5797 | -41.7007 | Bahia | ||||
| 36 | 202999.0 | 8268.0 | 173556.0 | 21175.0 | Ceara, Brazil | 2222.9223184471266 | 4.072926467618067 | -5.4984 | -39.3206 | Ceara | ||||
| 37 | 145452.0 | 2242.0 | 124864.0 | 18346.0 | Distrito Federal, Brazil | 4823.849820314479 | 1.5414019745345544 | -15.7998 | -47.8645 | Distrito Federal | ||||
| 38 | 104911.0 | 3000.0 | 89656.0 | 12255.0 | Espirito Santo, Brazil | 2610.6030632177467 | 2.8595666803290407 | -19.1834 | -40.3089 | Espirito Santo | ||||
| 39 | 113051.0 | 2647.0 | 103525.0 | 6879.0 | Goias, Brazil | 1610.790792256988 | 2.3414211285172177 | -15.827 | -49.8362 | Goias | ||||
| 40 | 143103.0 | 3328.0 | 130415.0 | 9360.0 | Maranhao, Brazil | 2022.6054994211456 | 2.325597646450458 | -4.9609 | -45.2744 | Maranhao | ||||
| 41 | 80134.0 | 2492.0 | 58278.0 | 19364.0 | Mato Grosso, Brazil | 2299.749803843688 | 3.1097910999076546 | -12.6819 | -56.9211 | Mato Grosso | ||||
| 42 | 40711.0 | 696.0 | 33946.0 | 6069.0 | Mato Grosso do Sul, Brazil | 1464.958801519691 | 1.7096116528702316 | -20.7722 | -54.7852 | Mato Grosso do Sul | ||||
| 43 | 188623.0 | 4663.0 | 155339.0 | 28621.0 | Minas Gerais, Brazil | 891.0428564389908 | 2.4721269410411244 | -18.5122 | -44.555 | Minas Gerais | ||||
| 44 | 186646.0 | 6037.0 | 170299.0 | 10310.0 | Para, Brazil | 2169.579552858263 | 3.2344652443663406 | -1.9981 | -54.9306 | Para | ||||
| 45 | 100290.0 | 2268.0 | 60655.0 | 37367.0 | Paraiba, Brazil | 2495.939028308463 | 2.2614418187256957 | -7.24 | -36.782 | Paraiba | ||||
| 46 | 114588.0 | 2928.0 | 70329.0 | 41331.0 | Parana, Brazil | 1002.1727386240826 | 2.555241386532621 | -25.2521 | -52.0215 | Parana | ||||
| 47 | 117431.0 | 7335.0 | 96220.0 | 13876.0 | Pernambuco, Brazil | 1228.7342010957125 | 6.2462211852066325 | -8.8137 | -36.9541 | Pernambuco | ||||
| 48 | 70536.0 | 1672.0 | 68178.0 | 686.0 | Piaui, Brazil | 2154.9376196640196 | 2.3704207780424182 | -7.7183 | -42.7289 | Piaui | ||||
| 49 | 59191.0 | 2154.0 | 34186.0 | 22851.0 | Rio Grande do Norte, Brazil | 1687.8665857964386 | 3.639066750012671 | -5.4026 | -36.9541 | Rio Grande do Norte | ||||
| 50 | 104335.0 | 2993.0 | 94351.0 | 6991.0 | Rio Grande do Sul, Brazil | 917.0502614913864 | 2.8686442708582933 | -30.0346 | -51.2177 | Rio Grande do Sul | ||||
| 51 | 207036.0 | 15202.0 | 184641.0 | 7193.0 | Rio de Janeiro, Brazil | 1199.1699016903792 | 7.34268436407195 | -22.9068 | -43.1729 | Rio de Janeiro | ||||
| 52 | 50514.0 | 1050.0 | 41786.0 | 7678.0 | Rondonia, Brazil | 2842.2962764984736 | 2.0786316664687017 | -11.5057 | -63.5806 | Rondonia | ||||
| 53 | 41191.0 | 576.0 | 10625.0 | 29990.0 | Roraima, Brazil | 6799.876518957147 | 1.398363720230147 | -2.7376 | -62.0751 | Roraima | ||||
| 54 | 130349.0 | 1995.0 | 117156.0 | 11198.0 | Santa Catarina, Brazil | 1819.3001663133648 | 1.530506563149698 | -27.2423 | -50.2189 | Santa Catarina | ||||
| 55 | 735960.0 | 28155.0 | 530780.0 | 177025.0 | Sao Paulo, Brazil | 1602.7335409319994 | 3.8256155225827486 | -23.5505 | -46.6333 | Sao Paulo | ||||
| 56 | 69845.0 | 1761.0 | 59036.0 | 9048.0 | Sergipe, Brazil | 3038.461806171847 | 2.521297157992698 | -10.5741 | -37.3857 | Sergipe | ||||
| 57 | 41848.0 | 556.0 | 25772.0 | 15520.0 | Tocantins, Brazil | 2660.620803043616 | 1.3286178550946282 | -10.1753 | -48.2982 | Tocantins | ||||
| 58 | 143.0 | 3.0 | 139.0 | 1.0 | Brunei | 32.68698440853701 | 2.097902097902098 | 4.5353 | 114.7277 | nan | ||||
| 59 | 15131.0 | 539.0 | 10282.0 | 4310.0 | Bulgaria | 217.76095227061595 | 3.5622232502808804 | 42.7339 | 25.4858 | nan | ||||
| 60 | 1297.0 | 55.0 | 1034.0 | 208.0 | Burkina Faso | 6.204768457846661 | 4.240555127216654 | 12.2383 | -1.5616 | nan | ||||
| 61 | 435.0 | 6.0 | 337.0 | 92.0 | Burma | 0.7994884156334059 | 1.3793103448275863 | 21.9162 | 95.956 | nan | ||||
| 62 | 426.0 | 1.0 | 336.0 | 89.0 | Burundi | 3.582607399799896 | 0.2347417840375587 | -3.3731 | 29.9189 | nan | ||||
| 63 | 3412.0 | 37.0 | 2498.0 | 877.0 | Cabo Verde | 613.6823096901372 | 1.0844079718640094 | 16.5388 | -23.0418 | nan | ||||
| 64 | 273.0 | 0.0 | 263.0 | 10.0 | Cambodia | 1.6328756117825671 | 0.0 | 11.55 | 104.9167 | nan | ||||
| 65 | 18762.0 | 408.0 | 16540.0 | 1814.0 | Cameroon | 70.67767694432548 | 2.1746082507195394 | 3.8480000000000003 | 11.5021 | nan | ||||
| 66 | 12748.0 | 230.0 | 11374.0 | 1144.0 | Alberta, Canada | 288.86422520351687 | 1.8042045811107623 | 53.9333 | -116.5765 | Alberta | ||||
| 67 | 4915.0 | 202.0 | 3889.0 | 824.0 | British Columbia, Canada | 96.16669572211796 | 4.109867751780264 | 53.7267 | -127.6476 | British Columbia | ||||
| 68 | 830.0 | 12.0 | 553.0 | 265.0 | Manitoba, Canada | 60.25333988618653 | 1.4457831325301205 | 53.7609 | -98.8139 | Manitoba | ||||
| 69 | 188.0 | 2.0 | 178.0 | 8.0 | New Brunswick, Canada | 24.10278040956777 | 1.0638297872340423 | 46.5653 | -66.4619 | New Brunswick | ||||
| 70 | 268.0 | 3.0 | 265.0 | 0.0 | Newfoundland and Labrador, Canada | 51.40352727935323 | 1.1194029850746268 | 53.1355 | -57.6604 | Newfoundland and Labrador | ||||
| 71 | 5.0 | 0.0 | 5.0 | 0.0 | Northwest Territories,Canada | 11.134865490824867 | 0.0 | 64.8255 | -124.8457 | Northwest Territories | ||||
| 72 | 1077.0 | 64.0 | 1008.0 | 5.0 | Nova Scotia, Canada | 110.1838750962958 | 5.942432683379757 | 44.681999999999995 | -63.7443 | Nova Scotia | ||||
| 73 | 0.0 | 0.0 | 0.0 | 0.0 | Nunavut, Canada | 70.2998 | -83.1076 | Nunavut | ||||||
| 74 | 43126.0 | 2841.0 | 39130.0 | 1155.0 | Ontario, Canada | 293.13830294497075 | 6.587673329314103 | 51.2538 | -85.3232 | Ontario | ||||
| 75 | 44.0 | 0.0 | 40.0 | 4.0 | Prince Edward Island, Canada | 27.820280984837947 | 0.0 | 46.5107 | -63.4168 | Prince Edward Island | ||||
| 76 | 61495.0 | 5733.0 | 54472.0 | 1290.0 | Quebec, Canada | 720.278146014945 | 9.322709163346614 | 52.9399 | -73.5491 | Quebec | ||||
| 77 | 1595.0 | 22.0 | 1439.0 | 134.0 | Saskatchewan, Canada | 134.97891959318454 | 1.3793103448275863 | 52.9399 | -106.4509 | Saskatchewan | ||||
| 78 | 15.0 | 0.0 | 15.0 | 0.0 | Yukon, Canada | 36.515896586980865 | 0.0 | 64.2823 | -135.0 | Yukon | ||||
| 79 | 4679.0 | 61.0 | 1755.0 | 2863.0 | Central African Republic | 96.87843960905752 | 1.3036973712331694 | 6.6111 | 20.9394 | nan | ||||
| 80 | 981.0 | 76.0 | 869.0 | 36.0 | Chad | 5.9722903989374325 | 7.747196738022426 | 15.4542 | 18.7322 | nan | ||||
| 81 | 17249.0 | 416.0 | 15946.0 | 887.0 | Antofagasta, Chile | 2839.1826630279133 | 2.411734013566004 | -23.6509 | -70.3975 | Antofagasta | ||||
| 82 | 4833.0 | 88.0 | 4423.0 | 322.0 | Araucania, Chile | 504.8974952571185 | 1.8208152286364576 | -38.9489 | -72.3311 | Araucania | ||||
| 83 | 6083.0 | 95.0 | 5364.0 | 624.0 | Arica y Parinacota, Chile | 2690.783304138578 | 1.5617294098306758 | -18.594 | -69.4785 | Arica y Parinacota | ||||
| 84 | 4983.0 | 37.0 | 4159.0 | 787.0 | Atacama, Chile | 1724.555623234952 | 0.7425245835841863 | -27.5661 | -70.0503 | Atacama | ||||
| 85 | 99.0 | 1.0 | 95.0 | 3.0 | Aysen, Chile | 95.96928982725528 | 1.0101010101010102 | -45.9864 | -73.7669 | Aysen | ||||
| 86 | 14994.0 | 170.0 | 13110.0 | 1714.0 | Biobio, Chile | 963.1264031140704 | 1.1337868480725624 | -37.4464 | -72.1416 | Biobio | ||||
| 87 | 8743.0 | 109.0 | 7713.0 | 921.0 | Coquimbo, Chile | 1154.0603971034313 | 1.2467116550383164 | -29.959 | -71.3389 | Coquimbo | ||||
| 88 | 5894.0 | 61.0 | 5094.0 | 739.0 | Los Lagos, Chile | 711.2275976580412 | 1.0349507974211063 | -41.9198 | -72.1416 | Los Lagos | ||||
| 89 | 1029.0 | 15.0 | 959.0 | 55.0 | Los Rios, Chile | 267.3859322258515 | 1.4577259475218658 | -40.231 | -72.3311 | Los Rios | ||||
| 90 | 2541.0 | 32.0 | 2010.0 | 499.0 | Magallanes, Chile | 1525.823710615914 | 1.2593467138921683 | -52.368 | -70.9863 | Magallanes | ||||
| 91 | 10824.0 | 235.0 | 9791.0 | 798.0 | Maule, Chile | 1035.8390353605437 | 2.1711012564671104 | -35.5183 | -71.6885 | Maule | ||||
| 92 | 268476.0 | 8250.0 | 255486.0 | 4740.0 | Metropolitana, Chile | 3774.5430496647737 | 3.072900370982881 | -33.4376 | -70.6505 | Metropolitana | ||||
| 93 | 4140.0 | 75.0 | 3782.0 | 283.0 | Nuble, Chile | 861.4070897548735 | 1.8115942028985508 | -36.7226 | -71.7622 | Nuble | ||||
| 94 | 12547.0 | 313.0 | 11498.0 | 736.0 | OHiggins, Chile | 1371.9240504945028 | 2.4946202279429346 | -34.5755 | -71.0022 | OHiggins | ||||
| 95 | 10213.0 | 191.0 | 9420.0 | 602.0 | Tarapaca, Chile | 3089.624211182304 | 1.8701654753745227 | -19.9232 | -69.5132 | Tarapaca | ||||
| 96 | 21057.0 | 634.0 | 18985.0 | 1438.0 | Valparaiso, Chile | 1159.589008657956 | 3.010875243386997 | -33.0472 | -71.6127 | Valparaiso | ||||
| 97 | 991.0 | 6.0 | 985.0 | 0.0 | Anhui, China | 1.5670461733080334 | 0.6054490413723511 | 31.8257 | 117.2264 | Anhui | ||||
| 98 | 935.0 | 9.0 | 924.0 | 2.0 | Beijing, China | 4.340761374187558 | 0.962566844919786 | 40.1824 | 116.4142 | Beijing | ||||
| 99 | 583.0 | 6.0 | 577.0 | 0.0 | Chongqing, China | 1.8794326241134751 | 1.0291595197255574 | 30.0572 | 107.874 | Chongqing | ||||
| 100 | 371.0 | 1.0 | 365.0 | 5.0 | Fujian, China | 0.9413854351687388 | 0.2695417789757412 | 26.0789 | 117.9874 | Fujian |
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