# Copyright (c) 2020 Graphcore Ltd. All rights reserved. # Copyright Johns Hopkins University 2020. """Load Covid-19 data extracted from the JHU CSSE COVID-19 Data. https://github.com/CSSEGISandData/COVID-19 Terms of Use: #. This data set is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020. https://creativecommons.org/licenses/by/4.0/legalcode #. Attribute the data as the 'COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University' or 'JHU CSSE COVID-19 Data' for short, and the url: https://github.com/CSSEGISandData/COVID-19. #. For publications that use the data, please cite the following publication: 'Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1' Only the data numbers in the COUNTRY_DATA_TRAIN and COUNTRY_DATA_TEST variables were extracted from the dataset. Graphcore contributed the respective code. """ import tensorflow as tf COUNTRY_DATA_TRAIN = {} COUNTRY_DATA_TEST = {} POPULATION = {} def get_data(name="Italy"): """Interface for Covid data extraction. Supported countries: ``Italy``, ``New_Zealand``, ``US``. """ COUNTRY_DATA_TRAIN["Italy"] = tf.constant( [ [ 155, 229, 322, 453, 655, 888, 1128, 1694, 2036, 2502, 3089, 3858, 4636, 5883, 7375, 9172, 10149, 12462, 15113, 17660, 21157, 24747, 27980, 31506, 35713, 41035, 47021, 53578, 59138, 63927, 69176, 74386, 80589, 86498, 92472, 97689, 101739, 105792, 110574, 115242, 119827, 124632, 128948, 132547, 135586, 139422, 143626, 147577, 152271, ], [ 2, 1, 1, 3, 45, 46, 46, 83, 149, 160, 276, 414, 523, 589, 622, 724, 724, 1045, 1045, 1439, 1966, 2335, 2749, 2941, 4025, 4440, 4440, 6072, 7024, 7024, 8326, 9362, 10361, 10950, 12384, 13030, 14620, 15729, 16847, 18278, 19758, 20996, 21815, 22837, 24392, 26491, 28470, 30455, 32534, ], [ 3, 7, 10, 12, 17, 21, 29, 34, 52, 79, 107, 148, 197, 233, 366, 463, 631, 827, 1016, 1266, 1441, 1809, 2158, 2503, 2978, 3405, 4032, 4825, 5476, 6077, 6820, 7503, 8215, 9134, 10023, 10779, 11591, 12428, 13155, 13915, 14681, 15362, 15887, 16523, 17127, 17669, 18279, 18849, 19468, ], ], dtype=tf.float32, ) COUNTRY_DATA_TEST["Italy"] = tf.constant( [ [ 155, 229, 322, 453, 655, 888, 1128, 1694, 2036, 2502, 3089, 3858, 4636, 5883, 7375, 9172, 10149, 12462, 15113, 17660, 21157, 24747, 27980, 31506, 35713, 41035, 47021, 53578, 59138, 63927, 69176, 74386, 80589, 86498, 92472, 97689, 101739, 105792, 110574, 115242, 119827, 124632, 128948, 132547, 135586, 139422, 143626, 147577, 152271, 156363, 159516, 162488, 165155, 168941, 172434, 175925, 178972, 181228, 183957, 187327, 189973, 192994, 195351, 197675, 199414, 201505, 203591, 205463, 207428, 209328, 210717, 211938, 213013, 214457, 215858, 217185, 218268, 219070, 219814, 221216, 222104, 223096, 223885, 224760, 225435, 225886, 226699, 227364, 228006, 228658, 229327, 229858, 230158, 230555, 231139, 231732, 232248, 232664, 232997, 233197, 233515, 233836, 234013, 234531, 234801, 234998, 235278, 235561, 235763, 236142, 236305, 236651, 236989, 237290, 237500, 237828, 238159, 238011, 238275, 238499, 238720, 238833, 239410, 239706, 239961, 240136, 240310, ], [ 2, 1, 1, 3, 45, 46, 46, 83, 149, 160, 276, 414, 523, 589, 622, 724, 724, 1045, 1045, 1439, 1966, 2335, 2749, 2941, 4025, 4440, 4440, 6072, 7024, 7024, 8326, 9362, 10361, 10950, 12384, 13030, 14620, 15729, 16847, 18278, 19758, 20996, 21815, 22837, 24392, 26491, 28470, 30455, 32534, 34211, 35435, 37130, 38092, 40164, 42727, 44927, 47055, 48877, 51600, 54543, 57576, 60498, 63120, 64928, 66624, 68941, 71252, 75945, 78249, 79914, 81654, 82879, 85231, 93245, 96276, 99023, 103031, 105186, 106587, 109039, 112541, 115288, 120205, 122810, 125176, 127326, 129401, 132282, 134560, 136720, 138840, 140479, 141981, 144658, 147101, 150604, 152844, 155633, 157507, 158355, 160092, 160938, 161895, 163781, 165078, 165837, 166584, 168646, 169939, 171338, 173085, 174865, 176370, 177010, 178526, 179455, 180544, 181907, 182453, 182893, 183426, 184585, 186111, 186725, 187615, 188584, 188891, ], [ 3, 7, 10, 12, 17, 21, 29, 34, 52, 79, 107, 148, 197, 233, 366, 463, 631, 827, 1016, 1266, 1441, 1809, 2158, 2503, 2978, 3405, 4032, 4825, 5476, 6077, 6820, 7503, 8215, 9134, 10023, 10779, 11591, 12428, 13155, 13915, 14681, 15362, 15887, 16523, 17127, 17669, 18279, 18849, 19468, 19899, 20465, 21067, 21645, 22170, 22745, 23227, 23660, 24114, 24648, 25085, 25549, 25969, 26384, 26644, 26977, 27359, 27682, 27967, 28236, 28710, 28884, 29079, 29315, 29684, 29958, 30201, 30395, 30560, 30739, 30911, 31106, 31368, 31610, 31763, 31908, 32007, 32169, 32330, 32486, 32616, 32735, 32785, 32877, 32955, 33072, 33142, 33229, 33340, 33415, 33475, 33530, 33601, 33689, 33774, 33846, 33899, 33964, 34043, 34114, 34167, 34223, 34301, 34345, 34371, 34405, 34448, 34514, 34561, 34610, 34634, 34657, 34675, 34644, 34678, 34708, 34716, 34738, ], ], dtype=tf.float32, ) COUNTRY_DATA_TRAIN["New_Zealand"] = tf.constant( [ [ 102, 102, 155, 205, 283, 368, 451, 514, 589, 647, 708, 797, 868, 950, 1039, 1106, 1160, 1210, 1239, 1283, 1312, 1330, 1349, 1366, 1386, 1401, 1409, 1422, 1431, 1440, 1445, 1451, 1456, 1461, 1470, 1469, 1472, 1474, 1476, 1479, 1485, 1487, 1487, 1486, 1488, 1489, 1490, 1492, 1494, ], [ 0, 0, 12, 22, 27, 37, 50, 56, 63, 74, 83, 92, 103, 127, 156, 176, 241, 282, 317, 373, 422, 471, 546, 628, 728, 770, 816, 867, 912, 974, 1006, 1036, 1095, 1118, 1142, 1180, 1214, 1229, 1241, 1252, 1263, 1266, 1276, 1302, 1316, 1332, 1347, 1368, 1371, ], [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 5, 9, 9, 9, 11, 11, 12, 12, 13, 14, 17, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 21, ], ], dtype=tf.float32, ) COUNTRY_DATA_TEST["New_Zealand"] = tf.constant( [ [ 102, 102, 155, 205, 283, 368, 451, 514, 589, 647, 708, 797, 868, 950, 1039, 1106, 1160, 1210, 1239, 1283, 1312, 1330, 1349, 1366, 1386, 1401, 1409, 1422, 1431, 1440, 1445, 1451, 1456, 1461, 1470, 1469, 1472, 1474, 1476, 1479, 1485, 1487, 1487, 1486, 1488, 1489, 1490, 1492, 1494, 1497, 1497, 1497, 1497, 1498, 1498, 1499, 1499, 1499, 1503, 1503, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1504, 1506, 1506, 1507, 1507, 1509, 1511, 1513, 1515, 1516, 1519, 1520, 1522, 1526, 1528, 1528, 1528, 1530, 1530, 1530, 1533, 1534, 1536, 1537, 1540, 1542, 1543, 1544, 1544, 1545, 1547, 1548, 1549, 1550, 1553, 1554, ], [ 0, 0, 12, 22, 27, 37, 50, 56, 63, 74, 83, 92, 103, 127, 156, 176, 241, 282, 317, 373, 422, 471, 546, 628, 728, 770, 816, 867, 912, 974, 1006, 1036, 1095, 1118, 1142, 1180, 1214, 1229, 1241, 1252, 1263, 1266, 1276, 1302, 1316, 1332, 1347, 1368, 1371, 1386, 1398, 1402, 1411, 1421, 1428, 1433, 1433, 1433, 1447, 1452, 1455, 1455, 1456, 1456, 1461, 1462, 1474, 1481, 1481, 1481, 1481, 1481, 1481, 1481, 1481, 1481, 1481, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1482, 1483, 1483, 1484, 1484, 1484, 1484, 1484, 1484, 1484, 1490, 1490, 1490, 1490, 1490, 1492, 1492, 1494, 1497, 1497, 1497, 1497, 1498, 1498, 1499, 1506, 1506, 1506, 1506, ], [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 5, 9, 9, 9, 11, 11, 12, 12, 13, 14, 17, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, ], ], dtype=tf.float32, ) COUNTRY_DATA_TRAIN["US"] = tf.constant( [ [ 107, 184, 237, 403, 519, 594, 782, 1147, 1586, 2219, 2978, 3212, 4679, 6512, 9169, 13663, 20030, 26025, 34832, 46052, 56629, 68663, 86556, 105196, 124793, 143724, 165733, 192101, 217920, 248309, 280306, 313304, 341489, 371675, 403073, 435089, 469738, 503274, 532632, 559591, 585382, 613973, 643981, 675504, 708310, 736161, 761842, 790264, 816302, ], [ 7, 7, 7, 7, 7, 7, 8, 8, 12, 12, 12, 12, 17, 17, 105, 121, 147, 176, 178, 178, 348, 361, 681, 869, 1072, 2665, 5644, 7024, 8474, 9001, 9707, 14652, 17448, 19581, 21763, 23559, 25410, 28790, 31270, 32988, 43482, 47763, 52096, 54703, 58545, 64840, 70337, 72329, 75204, ], [ 11, 12, 14, 17, 21, 22, 28, 33, 43, 51, 58, 70, 97, 132, 191, 265, 364, 464, 574, 763, 1002, 1326, 1734, 2254, 2886, 3472, 4164, 5252, 6422, 7924, 9313, 10843, 12426, 14196, 16767, 18913, 21141, 23359, 25488, 27294, 29240, 31692, 34301, 36477, 38570, 40530, 42461, 44647, 47195, ], ], dtype=tf.float32, ) COUNTRY_DATA_TEST["US"] = tf.constant( [ [ 107, 184, 237, 403, 519, 594, 782, 1147, 1586, 2219, 2978, 3212, 4679, 6512, 9169, 13663, 20030, 26025, 34832, 46052, 56629, 68663, 86556, 105196, 124793, 143724, 165733, 192101, 217920, 248309, 280306, 313304, 341489, 371675, 403073, 435089, 469738, 503274, 532632, 559591, 585382, 613973, 643981, 675504, 708310, 736161, 761842, 790264, 816302, 845599, 879030, 912596, 944397, 971497, 994670, 1019344, 1047189, 1076694, 1110914, 1138916, 1163418, 1186820, 1211383, 1236530, 1264321, 1291203, 1316172, 1335028, 1354477, 1377233, 1398200, 1425513, 1450808, 1474826, 1492945, 1514960, 1535992, 1559501, 1585166, 1608604, 1629802, 1649916, 1668248, 1687774, 1706364, 1729313, 1753665, 1777509, 1796684, 1814048, 1835422, 1855400, 1877139, 1902308, 1924146, 1941934, 1959462, 1977834, 1998661, 2021868, 2046739, 2072004, 2091381, 2111306, 2135103, 2161012, 2189204, 2220529, 2252785, 2278894, 2309382, 2345179, 2379638, 2420194, 2465517, 2507875, 2547230, 2587919, 2634093, 2685417, ], [ 7, 7, 7, 7, 7, 7, 8, 8, 12, 12, 12, 12, 17, 17, 105, 121, 147, 176, 178, 178, 348, 361, 681, 869, 1072, 2665, 5644, 7024, 8474, 9001, 9707, 14652, 17448, 19581, 21763, 23559, 25410, 28790, 31270, 32988, 43482, 47763, 52096, 54703, 58545, 64840, 70337, 72329, 75204, 77366, 80203, 99079, 100372, 106988, 111424, 115936, 120720, 153947, 164015, 175382, 180152, 187180, 189791, 189910, 195036, 198993, 212534, 216169, 232733, 230287, 243430, 246414, 250747, 268376, 272265, 283178, 289392, 294312, 298418, 350135, 361239, 366736, 379157, 384902, 391508, 399991, 406446, 416461, 444758, 458231, 463868, 479258, 485002, 491706, 500849, 506367, 518522, 524855, 533504, 540292, 547386, 556606, 561816, 576334, 583503, 592191, 599115, 606715, 617460, 622133, 640198, 647548, 656161, 663562, 670809, 679308, 685164, 705203, 720631, 729994, ], [ 11, 12, 14, 17, 21, 22, 28, 33, 43, 51, 58, 70, 97, 132, 191, 265, 364, 464, 574, 763, 1002, 1326, 1734, 2254, 2886, 3472, 4164, 5252, 6422, 7924, 9313, 10843, 12426, 14196, 16767, 18913, 21141, 23359, 25488, 27294, 29240, 31692, 34301, 36477, 38570, 40530, 42461, 44647, 47195, 49629, 52101, 54255, 55953, 57282, 58748, 60989, 63510, 65930, 67808, 69495, 70591, 71903, 74207, 76556, 78484, 80231, 81781, 82670, 83676, 85289, 87035, 88812, 90482, 91756, 92504, 93669, 95195, 96715, 97922, 99166, 100297, 100908, 101456, 102107, 103625, 104803, 105963, 106926, 107614, 108388, 109444, 110441, 111450, 112348, 113054, 113492, 113984, 114925, 115822, 116682, 117500, 118270, 118578, 118954, 119778, 120511, 121222, 121892, 122510, 122795, 123175, 124019, 124776, 125327, 125944, 126448, 126721, 127098, 127753, 128441, ], ], dtype=tf.float32, ) POPULATION["Italy"] = 60.36e6 POPULATION["New_Zealand"] = 5094780 POPULATION["US"] = 328239523 return COUNTRY_DATA_TRAIN[name], POPULATION[name]