HOME/Articles/

韓国と日本のPCR検査実施人数等比較 (新型コロナウイルス:Coronavirus)

Article Outline

韓国と日本のPCR検査実施人数等比較 (新型コロナウイルス:Coronavirus)

">Hits

(使用するデータ)
日本 : PCR検査実施人数は、厚生労働省の報道発表資料から抜き出した。
韓国 : Coronavirus Disease-19, Republic of KoreaまたはKCDC「News Room」「Press Release」
人口は世界の人口 (世銀)直近データ2018年を使う。日本:126,529,000、韓国:51,635,000(日本の約41%)

新型コロナウイルスのPCR検査実施人数と感染状況(韓国)「累計」

pcr04

日本と韓国の新型コロナウイルスによる死亡者数推移(累計で計算)

pcr04_2

日本と韓国のPCR検査の検査陽性率(%)「累計」

pcr05

日本と韓国のPCR検査の検査陽性率(%)の7日移動平均

  • 直近の状況を知るために7日移動平均のグラフを作成しました。

pcr07_3

日本と韓国のPCR検査の暫定致死率(%)「累計」

pcr06

ここからは差分をとって、日別のデータをグラフにした。

週単位の陽性者増加比(日本、韓国)

pcr06_2

韓国のPCR検査の結果(日別)

pcr08

日本と韓国の検査陽性者数(日別)

pcr07

日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)

pcr07_2

日本のデータでありえない箇所(検査者数がマイナス!)がある。

韓国の(報告された)陽性者数 対数表示(日別)

pcr08_2

日本の(報告された)感染者数 対数表示(日別)

pcr08_3

主な地点の平年値(日本、韓国、オーストラリア)

ひと足早く冬を終えた南半球のオーストラリアと北半球の日本、韓国を比較するため気温のグラフを載せておきます。

Ctemp01

回復された方、亡くなった方、療養中(入院、隔離、自宅療養)の方の推移

Rコードは、記事「東アジアの感染者の状況(新型コロナウイルス:Coronavirus)」にのせています。

日本

Japan

韓国

KoreaSouth

オーストラリア(南半球)

Australia まず、注目していただきたいのは、感染率(Infection rates)です。たった0.3%くらいしかありません。徹底的に検査をしている証拠です。 (日本:4.9%弱、韓国:1.3%弱)
また、上の気温のグラフを見てもわかるとおり、冬の気温についてもオーストラリアは日本、韓国より有利です。
それでも、冬場は感染者数、死亡者数、致死率ともに急上昇しました。(下の図参照)
よって、冬の日本のコロナの感染者数、死者数が急上昇してもなんら不思議ではありません。(韓国ですらヤバイ)

ニュージーランド(南半球)

NewZealand

日本、韓国、オーストラリアの検査陽性者数の推移

Ddata04

日本、韓国、オーストラリアの新型コロナによる死亡者数の推移

Ddata01

日本、韓国、オーストラリアの新型コロナによる人口100万人あたり死亡者数の推移

Ddata02

日本、韓国、オーストラリアの致死率(%)の7日移動平均

Ddata03

北海道、埼玉、東京、神奈川、愛知、大阪、兵庫の致死率7日移動平均(データ:NHK)

北海道(約550万人)、埼玉(約719万人)、東京(約1316万人)、神奈川(約905万人)、愛知(約741万人)、大阪(約886万人)、兵庫(約559万人)

  • 案の定、寒さの厳しい北海道の致死率が急上昇しています。

covOsaka11

北海道、埼玉、東京、神奈川、愛知、大阪、兵庫の月別死者数と月別人口100万人あたりの死者数(データ:NHK)

北海道(約550万人)、埼玉(約719万人)、東京(約1316万人)、神奈川(約905万人)、愛知(約741万人)、大阪(約886万人)、兵庫(約559万人)

  • 大阪の気温は他と比べて低いわけではないのに8月以降の死者数は比較的多い。 (注)「人口100万人あたり新型コロナウイルス月別死亡者」のグラフを直しました。(2020-12-12)

covOsaka12

日本、韓国、台湾、シンガポール、香港の面積、人口、人口密度

country area pop Population.density
Japan 377,915 127,103,388 336
Korea, South 99,720 49,039,986 492
Taiwan 35,980 23,359,928 649
Hong Kong 1,104 7,112,688 6,443
Singapore 697 5,567,301 7,988

Confirmed、 Deaths、Deaths/Confirmed (%)の表(米ジョンズ・ホプキンス大学のデータを使った。)

人口100万人あたりの死亡者数を計算して表に入れた。

現在、日本の死亡者数にずれが生じている。

Confirmed Deaths Deaths/Confirmed (%) Deaths/millionpeople
Japan 499,831 9325 1.87 73.37
Korea, South 108,945 1765 1.62 35.99
Taiwan 1,054 10 0.95 0.43
Hong Kong 11,563 207 1.79 29.10
Singapore 60,601 30 0.05 5.39

日本、韓国、台湾、シンガポール、香港の人口100万人あたりの新型コロナウイルスによる死者数

DperMil01

日本、韓国、台湾、シンガポール、香港のReported Confirmed

pcr11

日本、韓国、台湾、シンガポール、香港のPCR検査の暫定致死率(%)

Coronavirus01_1_2

新型コロナウイルスによる死者数 in アジア

(注意)日本語名は手打ちしているのでもしかしたら間違いがあるかもしれません。

CdeathsA01

新型コロナウイルスによる人口100万人あたりの死者数 in アジア

CdeathsA02

韓国のPCR検査実施人数とその結果(陽性率、暫定致死率を計算し、表を作成した)

Row.names 検査を受けた人 感染者数 死者 陰性 検査中 結果判明 陽性率(%) 暫定致死率(%)
2020-02-01 371 12 0 289 70 301 3.99 0.000
2020-02-02 429 15 0 327 87 342 4.39 0.000
2020-02-03 429 15 0 414 0 429 3.50 0.000
2020-02-04 607 16 0 462 129 478 3.35 0.000
2020-02-05 714 18 0 522 174 540 3.33 0.000
2020-02-06 885 23 0 693 169 716 3.21 0.000
2020-02-07 1130 24 0 842 264 866 2.77 0.000
2020-02-08 1701 24 0 1057 620 1081 2.22 0.000
2020-02-09 2340 25 0 1355 960 1380 1.81 0.000
2020-02-10 2776 27 0 1940 809 1967 1.37 0.000
2020-02-11 3629 28 0 2736 865 2764 1.01 0.000
2020-02-12 5074 28 0 4054 992 4082 0.69 0.000
2020-02-13 5797 28 0 5099 670 5127 0.55 0.000
2020-02-14 6854 28 0 6134 692 6162 0.45 0.000
2020-02-15 7519 28 0 6853 638 6881 0.41 0.000
2020-02-16 7919 29 0 7313 577 7342 0.39 0.000
2020-02-17 8171 30 0 7733 408 7763 0.39 0.000
2020-02-18 9265 31 0 8277 957 8308 0.37 0.000
2020-02-19 10411 46 0 9335 1030 9381 0.49 0.000
2020-02-20 12161 82 0 10446 1633 10528 0.78 0.000
2020-02-21 14816 156 1 11953 2707 12109 1.29 0.641
2020-02-22 19621 346 2 13794 5481 14140 2.45 0.578
2020-02-23 22633 556 4 16038 6039 16594 3.35 0.719
2020-02-24 28615 763 7 19127 8725 19890 3.84 0.917
2020-02-25 36716 893 8 22550 13273 23443 3.81 0.896
2020-02-26 46127 1146 11 28247 16734 29393 3.90 0.960
2020-02-27 57990 1595 12 35298 21097 36893 4.32 0.752
2020-02-28 70940 2022 13 44167 24751 46189 4.38 0.643
2020-02-29 85693 2931 16 53608 29154 56539 5.18 0.546
2020-03-01 96985 3526 17 61037 32422 64563 5.46 0.482
2020-03-02 109591 4212 22 71580 33799 75792 5.56 0.522
2020-03-03 125851 4812 28 85484 35555 90296 5.33 0.582
2020-03-04 136707 5328 32 102965 28414 108293 4.92 0.601
2020-03-05 146541 5766 35 118965 21810 124731 4.62 0.607
2020-03-06 164740 6284 42 136624 21832 142908 4.40 0.668
2020-03-07 178189 6767 44 151802 19620 158569 4.27 0.650
2020-03-08 188518 7134 50 162008 19376 169142 4.22 0.701
2020-03-09 196618 7382 51 171778 17458 179160 4.12 0.691
2020-03-10 210144 7513 54 184179 18452 191692 3.92 0.719
2020-03-11 222395 7755 60 196100 18540 203855 3.80 0.774
2020-03-12 234998 7869 66 209402 17727 217271 3.62 0.839
2020-03-13 248647 7979 67 222728 17940 230707 3.46 0.840
2020-03-14 261335 8086 72 235615 17634 243701 3.32 0.890
2020-03-15 268212 8162 75 243778 16272 251940 3.24 0.919
2020-03-16 274504 8236 75 251297 14971 259533 3.17 0.911
2020-03-17 286716 8320 81 261105 17291 269425 3.09 0.974
2020-03-18 295647 8413 84 270888 16346 279301 3.01 0.998
2020-03-19 307024 8565 91 282555 15904 291120 2.94 1.062
2020-03-20 316664 8652 94 292487 15525 301139 2.87 1.086
2020-03-21 327509 8799 102 303006 15704 311805 2.82 1.159
2020-03-22 331780 8897 104 308343 14540 317240 2.80 1.169
2020-03-23 338036 8961 111 315447 13628 324408 2.76 1.239
2020-03-24 348582 9037 120 324105 15440 333142 2.71 1.328
2020-03-25 357896 9137 126 334481 14278 343618 2.66 1.379
2020-03-26 364942 9241 131 341332 14369 350573 2.64 1.418
2020-03-27 376961 9332 139 352410 15219 361742 2.58 1.489
2020-03-28 387925 9478 144 361883 16564 371361 2.55 1.519
2020-03-29 394141 9583 152 369530 15028 379113 2.53 1.586
2020-03-30 395194 9661 158 372002 13531 381663 2.53 1.635
2020-03-31 410564 9786 162 383886 16892 393672 2.49 1.655
2020-04-01 421547 9887 165 395075 16585 404962 2.44 1.669
2020-04-02 431743 9976 169 403882 17885 413858 2.41 1.694
2020-04-03 443273 10062 174 414303 18908 424365 2.37 1.729
2020-04-04 455032 10156 177 424732 20144 434888 2.34 1.743
2020-04-05 461233 10237 183 431425 19571 441662 2.32 1.788
2020-04-06 466804 10284 186 437225 19295 447509 2.30 1.809
2020-04-07 477304 10331 192 446323 20650 456654 2.26 1.858
2020-04-08 486003 10384 200 457761 17858 468145 2.22 1.926
2020-04-09 494711 10423 204 468779 15509 479202 2.18 1.957
2020-04-10 503051 10450 208 477303 15298 487753 2.14 1.990
2020-04-11 510479 10480 211 485929 14070 496409 2.11 2.013
2020-04-12 514621 10512 214 490321 13788 500833 2.10 2.036
2020-04-13 518743 10537 217 494815 13391 505352 2.09 2.059
2020-04-14 527438 10564 222 502223 14651 512787 2.06 2.101
2020-04-15 534552 10591 225 508935 15026 519526 2.04 2.124
2020-04-16 538775 10613 229 513894 14268 524507 2.02 2.158
2020-04-17 546463 10635 230 521642 14186 532277 2.00 2.163
2020-04-18 554834 10653 232 530631 13550 541284 1.97 2.178
2020-04-19 559109 10661 234 536205 12243 546866 1.95 2.195
2020-04-20 563035 10674 236 540380 11981 551054 1.94 2.211
2020-04-21 571014 10683 237 547610 12721 558293 1.91 2.218
2020-04-22 577959 10694 238 555144 12121 565838 1.89 2.226
2020-04-23 583971 10702 240 563130 10139 573832 1.87 2.243
2020-04-24 589520 10708 240 569212 9600 579920 1.85 2.241
2020-04-25 595161 10718 240 575184 9259 585902 1.83 2.239
2020-04-26 598285 10728 242 578558 8999 589286 1.82 2.256
2020-04-27 601660 10738 243 582027 8895 592765 1.81 2.263
2020-04-28 608514 10752 244 588559 9203 599311 1.79 2.269
2020-04-29 614197 10761 246 595129 8307 605890 1.78 2.286
2020-04-30 619881 10765 247 600482 8634 611247 1.76 2.294
2020-05-01 623069 10774 248 603610 8685 614384 1.75 2.302
2020-05-02 627562 10780 250 608286 8496 619066 1.74 2.319
2020-05-03 630973 10793 250 611592 8588 622385 1.73 2.316
2020-05-04 633921 10801 252 614944 8176 625745 1.73 2.333
2020-05-05 640237 10804 254 620575 8858 631379 1.71 2.351
2020-05-06 643095 10806 255 624280 8009 635086 1.70 2.360
2020-05-07 649388 10810 256 630149 8429 640959 1.69 2.368
2020-05-08 654863 10822 256 635174 8867 645996 1.68 2.366
2020-05-09 660030 10840 256 640037 9153 650877 1.67 2.362
2020-05-10 663886 10874 256 642884 10128 653758 1.66 2.354
2020-05-11 668492 10909 256 646661 10922 657570 1.66 2.347
2020-05-12 680890 10936 258 653624 16330 664560 1.65 2.359
2020-05-13 695920 10962 259 665379 19579 676341 1.62 2.363
2020-05-14 711484 10991 260 679771 20722 690762 1.59 2.366
2020-05-15 726747 11018 260 695854 19875 706872 1.56 2.360
2020-05-16 740645 11037 262 711265 18343 722302 1.53 2.374
2020-05-17 747653 11050 262 718943 17660 729993 1.51 2.371
2020-05-18 753211 11065 263 726053 16093 737118 1.50 2.377
2020-05-19 765574 11078 263 737571 16925 748649 1.48 2.374
2020-05-20 776433 11110 263 748972 16351 760082 1.46 2.367
2020-05-21 788684 11122 264 759473 18089 770595 1.44 2.374
2020-05-22 802418 11142 264 770990 20286 782132 1.42 2.369
2020-05-23 814420 11165 266 781686 21569 792851 1.41 2.382
2020-05-24 820289 11190 266 788766 20333 799956 1.40 2.377
2020-05-25 826437 11206 267 796142 19089 807348 1.39 2.383
2020-05-26 839475 11225 269 806206 22044 817431 1.37 2.396
2020-05-27 852876 11265 269 820550 21061 831815 1.35 2.388
2020-05-28 868666 11344 269 834952 22370 846296 1.34 2.371
2020-05-29 885120 11402 269 849161 24557 860563 1.32 2.359
2020-05-30 902901 11441 269 865162 26298 876603 1.31 2.351
2020-05-31 910822 11468 270 876060 23294 887528 1.29 2.354
2020-06-01 921391 11503 271 885830 24058 897333 1.28 2.356
2020-06-02 939851 11541 272 899388 28922 910929 1.27 2.357
2020-06-03 956852 11590 273 917397 27865 928987 1.25 2.355
2020-06-04 973858 11629 273 934030 28199 945659 1.23 2.348
2020-06-05 990960 11668 273 950526 28766 962194 1.21 2.340
2020-06-06 1005305 11719 273 965632 27954 977351 1.20 2.330
2020-06-07 1012769 11776 273 974512 26481 986288 1.19 2.318
2020-06-08 1018214 11814 273 982026 24374 993840 1.19 2.311
2020-06-09 1035997 11852 274 996686 27459 1008538 1.18 2.312
2020-06-10 1051972 11902 276 1013847 26223 1025749 1.16 2.319
2020-06-11 1066888 11947 276 1029447 25494 1041394 1.15 2.310
2020-06-12 1081486 12002 277 1045240 24244 1057242 1.14 2.308
2020-06-13 1094704 12051 277 1059301 23352 1071352 1.12 2.299
2020-06-14 1100327 12084 277 1066887 21356 1078971 1.12 2.292
2020-06-15 1105719 12121 277 1072805 20793 1084926 1.12 2.285
2020-06-16 1119767 12155 278 1084980 22632 1097135 1.11 2.287
2020-06-17 1132823 12198 279 1099136 21489 1111334 1.10 2.287
2020-06-18 1145712 12257 280 1111741 21714 1123998 1.09 2.284
2020-06-19 1158063 12306 280 1124567 21190 1136873 1.08 2.275
2020-06-20 1170901 12373 280 1137058 21470 1149431 1.08 2.263
2020-06-21 1176463 12421 280 1143971 20071 1156392 1.07 2.254
2020-06-22 1182066 12438 280 1150225 19403 1162663 1.07 2.251
2020-06-23 1196012 12484 281 1161250 22278 1173734 1.06 2.251
2020-06-24 1208597 12535 281 1175817 20245 1188352 1.05 2.242
2020-06-25 1220478 12563 282 1189015 18900 1201578 1.05 2.245
2020-06-26 1232315 12602 282 1200885 18828 1213487 1.04 2.238
2020-06-27 1243780 12653 282 1211261 19866 1223914 1.03 2.229
2020-06-28 1251695 12715 282 1219975 19005 1232690 1.03 2.218
2020-06-29 1259954 12757 282 1228698 18499 1241455 1.03 2.211
2020-06-30 1273766 12799 282 1240157 20810 1252956 1.02 2.203
2020-07-01 1285231 12850 282 1252855 19526 1265705 1.02 2.195
2020-07-02 1295962 12904 282 1263276 19782 1276180 1.01 2.185
2020-07-03 1307761 12967 282 1273234 21560 1286201 1.01 2.175
2020-07-04 1319523 13030 283 1284172 22321 1297202 1.00 2.172
2020-07-05 1326055 13089 283 1291315 21651 1304404 1.00 2.162
2020-07-06 1331796 13137 284 1297367 21292 1310504 1.00 2.162
2020-07-07 1346194 13181 285 1309338 23675 1322519 1.00 2.162
2020-07-08 1359735 13243 285 1322479 24013 1335722 0.99 2.152
2020-07-09 1371771 13293 287 1334566 23912 1347859 0.99 2.159
2020-07-10 1384890 13338 288 1348025 23527 1361363 0.98 2.159
2020-07-11 1396941 13373 288 1360618 22950 1373991 0.97 2.154
2020-07-12 1402144 13417 289 1366897 21830 1380314 0.97 2.154
2020-07-13 1408312 13479 289 1372988 21845 1386467 0.97 2.144
2020-07-14 1420616 13512 289 1382815 24289 1396327 0.97 2.139
2020-07-15 1431316 13551 289 1394468 23297 1408019 0.96 2.133
2020-07-16 1441348 13612 291 1404332 23404 1417944 0.96 2.138
2020-07-17 1451017 13672 293 1414235 23110 1427907 0.96 2.143
2020-07-18 1460204 13711 294 1423570 22923 1437281 0.95 2.144
2020-07-19 1465299 13745 295 1429601 21953 1443346 0.95 2.146
2020-07-20 1470193 13771 296 1435120 21302 1448891 0.95 2.149
2020-07-21 1482390 13816 296 1444710 23864 1458526 0.95 2.142
2020-07-22 1492071 13879 297 1456441 21751 1470320 0.94 2.140
2020-07-23 1500854 13938 297 1465498 21418 1479436 0.94 2.131
2020-07-24 1510327 13979 298 1475789 20559 1489768 0.94 2.132
2020-07-25 1518634 14092 298 1484861 19681 1498953 0.94 2.115
2020-07-26 1522926 14150 298 1489562 19214 1503712 0.94 2.106
2020-07-27 1526974 14175 299 1494029 18770 1508204 0.94 2.109
2020-07-28 1537704 14203 300 1503057 20444 1517260 0.94 2.112
2020-07-29 1547307 14251 300 1513730 19326 1527981 0.93 2.105
2020-07-30 1556215 14269 300 1522928 19018 1537197 0.93 2.102
2020-07-31 1563796 14305 301 1531161 18330 1545466 0.93 2.104
2020-08-01 1571830 14336 301 1539216 18278 1553552 0.92 2.100
2020-08-02 1576246 14366 301 1544112 17768 1558478 0.92 2.095
2020-08-03 1579757 14389 301 1547967 17401 1562356 0.92 2.092
2020-08-04 1589780 14423 301 1556633 18724 1571056 0.92 2.087
2020-08-05 1598187 14456 302 1565241 18490 1579697 0.92 2.089
2020-08-06 1606487 14499 302 1573957 18031 1588456 0.91 2.083
2020-08-07 1613652 14519 303 1582065 17068 1596584 0.91 2.087
2020-08-08 1620514 14562 304 1589847 16105 1604409 0.91 2.088
2020-08-09 1624650 14598 305 1593249 16803 1607847 0.91 2.089
2020-08-10 1628303 14626 305 1597281 16396 1611907 0.91 2.085
2020-08-11 1637844 14660 305 1605695 17489 1620355 0.90 2.080
2020-08-12 1646652 14714 305 1614563 17375 1629277 0.90 2.073
2020-08-13 1654898 14770 305 1622330 17798 1637100 0.90 2.065
2020-08-14 1665084 14873 305 1630079 20132 1644952 0.90 2.051
2020-08-15 1675296 15039 305 1638639 21618 1653678 0.91 2.028
2020-08-16 1681787 15318 305 1644464 22005 1659782 0.92 1.991
2020-08-17 1688470 15515 305 1649991 22964 1665506 0.93 1.966
2020-08-18 1697042 15761 306 1656062 25219 1671823 0.94 1.942
2020-08-19 1715064 16058 306 1667984 31022 1684042 0.95 1.906
2020-08-20 1734083 16346 307 1682739 34998 1699085 0.96 1.878
2020-08-21 1754123 16670 309 1699408 38045 1716078 0.97 1.854
2020-08-22 1775800 17002 309 1716371 42427 1733373 0.98 1.817
2020-08-23 1791186 17399 309 1726223 47564 1743622 1.00 1.776
2020-08-24 1804422 17665 309 1738762 47995 1756427 1.01 1.749
2020-08-25 1825837 17945 310 1757530 50362 1775475 1.01 1.728
2020-08-26 1849506 18265 312 1778446 52795 1796711 1.02 1.708
2020-08-27 1869579 18706 313 1798832 52041 1817538 1.03 1.673
2020-08-28 1887717 19077 316 1817929 50711 1837006 1.04 1.656
2020-08-29 1909329 19400 321 1835883 54046 1855283 1.05 1.655
2020-08-30 1924170 19699 323 1846450 58021 1866149 1.06 1.640
2020-08-31 1937689 19947 324 1859866 57876 1879813 1.06 1.624
2020-09-01 1959080 20182 324 1882155 56743 1902337 1.06 1.605
2020-09-02 1980295 20449 326 1903098 56748 1923547 1.06 1.594
2020-09-03 2000552 20644 329 1924384 55524 1945028 1.06 1.594
2020-09-04 2018906 20842 331 1945798 52266 1966640 1.06 1.588
2020-09-05 2037045 21010 333 1963958 52077 1984968 1.06 1.585
2020-09-06 2045935 21177 334 1975137 49621 1996314 1.06 1.577
2020-09-07 2051297 21296 336 1982892 47109 2004188 1.06 1.578
2020-09-08 2066078 21432 341 2001276 43370 2022708 1.06 1.591
2020-09-09 2082234 21588 344 2024113 36533 2045701 1.06 1.593
2020-09-10 2099591 21743 346 2044830 33018 2066573 1.05 1.591
2020-09-11 2119211 21919 350 2067869 29423 2089788 1.05 1.597
2020-09-12 2135457 22055 355 2085576 27826 2107631 1.05 1.610
2020-09-13 2143270 22176 358 2093389 27705 2115565 1.05 1.614
2020-09-14 2151002 22285 363 2101241 27476 2123526 1.05 1.629
2020-09-15 2164578 22391 367 2114877 27310 2137268 1.05 1.639
2020-09-16 2178832 22504 367 2130486 25842 2152990 1.05 1.631
2020-09-17 2191892 22657 372 2143727 25508 2166384 1.05 1.642
2020-09-18 2206365 22783 377 2158179 25403 2180962 1.04 1.655
2020-09-19 2219162 22893 378 2171564 24705 2194457 1.04 1.651
2020-09-20 2226701 22975 383 2179452 24274 2202427 1.04 1.667
2020-09-21 2231589 23045 385 2186008 22536 2209053 1.04 1.671
2020-09-22 2245112 23106 388 2198784 23222 2221890 1.04 1.679
2020-09-23 2256899 23216 388 2213156 20527 2236372 1.04 1.671
2020-09-24 2268999 23341 393 2224876 20782 2248217 1.04 1.684
2020-09-25 2280276 23455 395 2237246 19575 2260701 1.04 1.684
2020-09-26 2290345 23516 399 2248321 18508 2271837 1.04 1.697
2020-09-27 2296517 23611 401 2254028 18878 2277639 1.04 1.698
2020-09-28 2301303 23661 406 2259055 18587 2282716 1.04 1.716
2020-09-29 2313044 23699 407 2269289 20056 2292988 1.03 1.717
2020-09-30 2322999 23812 413 2278591 20596 2302403 1.03 1.734
2020-10-01 2328435 23889 415 2284517 20029 2308406 1.03 1.737
2020-10-02 2333777 23952 416 2289830 19995 2313782 1.04 1.737
2020-10-03 2339859 24027 420 2294545 21287 2318572 1.04 1.748
2020-10-04 2346345 24091 421 2300138 22116 2324229 1.04 1.748
2020-10-05 2352378 24164 422 2305767 22447 2329931 1.04 1.746
2020-10-06 2365433 24239 422 2318457 22737 2342696 1.03 1.741
2020-10-07 2378073 24353 425 2331582 22138 2355935 1.03 1.745
2020-10-08 2388844 24422 427 2343444 20978 2367866 1.03 1.748
2020-10-09 2400233 24476 428 2355058 20699 2379534 1.03 1.749
2020-10-10 2404684 24548 430 2361154 18982 2385702 1.03 1.752
2020-10-11 2410483 24605 432 2366574 19304 2391179 1.03 1.756
2020-10-12 2415610 24703 433 2371715 19192 2396418 1.03 1.753
2020-10-13 2428771 24805 434 2383842 20124 2408647 1.03 1.750
2020-10-14 2441443 24878 438 2398169 18396 2423047 1.03 1.761
2020-10-15 2450739 24988 439 2407489 18262 2432477 1.03 1.757
2020-10-16 2459426 25035 441 2417036 17355 2442071 1.03 1.762
2020-10-17 2468527 25108 443 2426101 17318 2451209 1.02 1.764
2020-10-18 2474529 25199 444 2431549 17781 2456748 1.03 1.762
2020-10-19 2479226 25275 444 2437188 16763 2462463 1.03 1.757
2020-10-20 2491311 25333 447 2446599 19379 2471932 1.02 1.764
2020-10-21 2503489 25422 450 2458574 19493 2483996 1.02 1.770
2020-10-22 2515325 25543 453 2469969 19813 2495512 1.02 1.773
2020-10-23 2528621 25698 455 2482493 20430 2508191 1.02 1.771
2020-10-24 2540679 25775 457 2493016 21888 2518791 1.02 1.773
2020-10-25 2546146 25836 457 2498628 21682 2524464 1.02 1.769
2020-10-26 2552264 25955 457 2505546 20763 2531501 1.03 1.761
2020-10-27 2567587 26043 460 2518541 23003 2544584 1.02 1.766
2020-10-28 2582960 26146 461 2533910 22904 2560056 1.02 1.763
2020-10-29 2597978 26271 462 2545559 26148 2571830 1.02 1.759
2020-10-30 2612231 26384 463 2559474 26373 2585858 1.02 1.755
2020-10-31 2624492 26511 464 2572303 25678 2598814 1.02 1.750
2020-11-01 2630630 26635 466 2579157 24838 2605792 1.02 1.750
2020-11-02 2636650 26732 468 2584394 25524 2611126 1.02 1.751
2020-11-03 2649859 26807 472 2597237 25815 2624044 1.02 1.761
2020-11-04 2662260 26925 474 2611080 24255 2638005 1.02 1.760
2020-11-05 2673706 27050 475 2621594 25062 2648644 1.02 1.756
2020-11-06 2686314 27195 476 2633490 25629 2660685 1.02 1.750
2020-11-07 2697249 27284 477 2643748 26217 2671032 1.02 1.748
2020-11-08 2702880 27427 478 2649805 25648 2677232 1.02 1.743
2020-11-09 2709199 27553 480 2655844 25802 2683397 1.03 1.742
2020-11-10 2723960 27653 485 2668452 27855 2696105 1.03 1.754
2020-11-11 2736534 27799 487 2680047 28688 2707846 1.03 1.752
2020-11-12 2749772 27942 487 2692546 29284 2720488 1.03 1.743
2020-11-13 2761411 28133 488 2703159 30119 2731292 1.03 1.735
2020-11-14 2777289 28338 492 2714259 34692 2742597 1.03 1.736
2020-11-15 2786878 28546 493 2721954 36378 2750500 1.04 1.727
2020-11-16 2797691 28768 494 2730609 38314 2759377 1.04 1.717
2020-11-17 2815755 28998 494 2745555 41202 2774553 1.05 1.704
2020-11-18 2834362 29311 496 2762363 42688 2791674 1.05 1.692
2020-11-19 2853843 29654 498 2778664 45525 2808318 1.06 1.679
2020-11-20 2873443 30017 501 2795283 48143 2825300 1.06 1.669
2020-11-21 2896746 30403 503 2814998 51345 2845401 1.07 1.654
2020-11-22 2908890 30733 505 2824128 54029 2854861 1.08 1.643
2020-11-23 2922135 31004 509 2834676 56455 2865680 1.08 1.642
2020-11-24 2946399 31353 510 2857277 57769 2888630 1.09 1.627
2020-11-25 2966405 31735 513 2878832 55838 2910567 1.09 1.617
2020-11-26 2988046 32318 515 2900367 55361 2932685 1.10 1.594
2020-11-27 3009562 32871 516 2920054 56637 2952925 1.11 1.570
2020-11-28 3032003 33374 522 2939835 58794 2973209 1.12 1.564
2020-11-29 3046910 33763 523 2950318 62829 2984081 1.13 1.549
2020-11-30 3061172 34201 526 2963606 63365 2997807 1.14 1.538
2020-12-01 3083997 34652 526 2984453 64892 3019105 1.15 1.518
2020-12-02 3106970 35163 526 3006653 65154 3041816 1.16 1.496
2020-12-03 3131886 35703 529 3030395 65788 3066098 1.16 1.482
2020-12-04 3157410 36332 536 3057061 64017 3093393 1.17 1.475
2020-12-05 3180496 36915 540 3077314 66267 3114229 1.19 1.463
2020-12-06 3194867 37546 545 3089605 67716 3127151 1.20 1.452
2020-12-07 3209376 38161 549 3103205 68010 3141366 1.21 1.439
2020-12-08 3221317 38746 552 3111297 71274 3150043 1.23 1.425
2020-12-09 3253220 39416 556 3138724 75080 3178140 1.24 1.411
2020-12-10 3277948 40097 564 3165087 72764 3205184 1.25 1.407
2020-12-11 3311213 40786 572 3192739 77688 3233525 1.26 1.402
2020-12-12 3349864 41736 578 3221386 86742 3263122 1.28 1.385
2020-12-13 3374595 42766 580 3241700 90129 3284466 1.30 1.356
2020-12-14 3397039 43484 587 3264308 89247 3307792 1.31 1.350
2020-12-15 3441220 44364 600 3303383 93473 3347747 1.33 1.352
2020-12-16 3488769 45439 612 3348546 94784 3393985 1.34 1.347
2020-12-17 3538840 46453 634 3393129 99258 3439582 1.35 1.365
2020-12-18 3589797 47517 645 3431662 110618 3479179 1.37 1.357
2020-12-19 3646247 48568 659 3471446 126233 3520014 1.38 1.357
2020-12-20 3683094 49665 674 3493954 139475 3543619 1.40 1.357
2020-12-21 3713861 50591 698 3516832 146438 3567423 1.42 1.380
2020-12-22 3772430 51458 722 3569843 151129 3621301 1.42 1.403
2020-12-23 3826570 52548 739 3620913 153109 3673461 1.43 1.406
2020-12-24 3882210 53529 756 3678148 150533 3731677 1.43 1.412
2020-12-25 3939357 54770 773 3727798 156789 3782568 1.45 1.411
2020-12-26 3969415 55902 793 3756501 157012 3812403 1.47 1.419
2020-12-27 4006412 56872 808 3782564 166976 3839436 1.48 1.421
2020-12-28 4038307 57679 819 3810749 169879 3868428 1.49 1.420
2020-12-29 4098179 58723 859 3868494 170962 3927217 1.50 1.463
2020-12-30 4159522 59773 879 3927978 171771 3987751 1.50 1.471
2020-12-31 4213880 60740 900 3982802 170338 4043542 1.50 1.482
2021-01-01 4269318 61769 917 4030622 176927 4092391 1.51 1.485
2021-01-02 4302799 62593 942 4060819 179387 4123412 1.52 1.505
2021-01-03 4340838 63244 962 4091122 186472 4154366 1.52 1.521
2021-01-04 4376608 64264 981 4121612 190732 4185876 1.54 1.527
2021-01-05 4439360 64978 1007 4180631 193751 4245609 1.53 1.550
2021-01-06 4504866 65816 1027 4246968 192082 4312784 1.53 1.560
2021-01-07 4569808 66684 1046 4311362 191762 4378046 1.52 1.569
2021-01-08 4630004 67358 1081 4372194 190452 4439552 1.52 1.605
2021-01-09 4689616 67999 1100 4435694 185923 4503693 1.51 1.618
2021-01-10 4723464 68664 1125 4463346 191454 4532010 1.52 1.638
2021-01-11 4751685 69114 1140 4492808 189763 4561922 1.52 1.649
2021-01-12 4814085 69650 1165 4557665 186770 4627315 1.51 1.673
2021-01-13 4872312 70204 1185 4619194 182914 4689398 1.50 1.688
2021-01-14 4925359 70728 1195 4684889 169742 4755617 1.49 1.690
2021-01-15 4978074 71240 1217 4744511 162323 4815751 1.48 1.708
2021-01-16 5032270 71820 1236 4804811 155639 4876631 1.47 1.721
2021-01-17 5061290 72340 1249 4829565 159385 4901905 1.48 1.727
2021-01-18 5087220 72729 1264 4856456 158035 4929185 1.48 1.738
2021-01-19 5140325 73114 1283 4912866 154345 4985980 1.47 1.755
2021-01-20 5192129 73518 1300 4970470 148141 5043988 1.46 1.768
2021-01-21 5237606 73916 1316 5022620 141070 5096536 1.45 1.780
2021-01-22 5282223 74261 1328 5074830 133132 5149091 1.44 1.788
2021-01-23 5329707 74692 1337 5118386 136629 5193078 1.44 1.790
2021-01-24 5354349 75084 1349 5143500 135765 5218584 1.44 1.797
2021-01-25 5376086 75521 1360 5166016 134549 5241537 1.44 1.801
2021-01-26 5422763 75870 1371 5208269 138624 5284139 1.44 1.807
2021-01-27 5469247 76429 1378 5254391 138427 5330820 1.43 1.803
2021-01-28 5522189 76926 1386 5305839 139424 5382765 1.43 1.802
2021-01-29 5569264 77395 1399 5349116 142753 5426511 1.43 1.808
2021-01-30 5616530 77850 1414 5392885 145795 5470735 1.42 1.816
2021-01-31 5640818 78203 1420 5413065 149550 5491268 1.42 1.816
2021-02-01 5661842 78508 1425 5433878 149456 5512386 1.42 1.815
2021-02-02 5711413 78844 1435 5478304 154265 5557148 1.42 1.820
2021-02-03 5756714 79311 1441 5538554 138849 5617865 1.41 1.817
2021-02-04 5803095 79761 1448 5602796 120538 5682557 1.40 1.815
2021-02-05 5847178 80131 1459 5659590 107457 5739721 1.40 1.821
2021-02-06 5893353 80524 1464 5715152 97677 5795676 1.39 1.818
2021-02-07 5916975 80896 1471 5745328 90751 5826224 1.39 1.818
2021-02-08 5938197 81184 1474 5772170 84843 5853354 1.39 1.816
2021-02-09 5987405 81486 1482 5823289 82630 5904775 1.38 1.819
2021-02-10 6030023 81930 1486 5868017 80076 5949947 1.38 1.814
2021-02-11 6070008 82434 1496 5905960 81614 5988394 1.38 1.815
2021-02-12 6093369 82837 1507 5929671 80861 6012508 1.38 1.819
2021-02-13 6115337 83199 1514 5961887 70251 6045086 1.38 1.820
2021-02-14 6140086 83525 1522 5978361 78200 6061886 1.38 1.822
2021-02-15 6162860 83868 1527 5998846 80146 6082714 1.38 1.821
2021-02-16 6213490 84325 1534 6052268 76897 6136593 1.37 1.819
2021-02-17 6260567 84946 1538 6103211 72410 6188157 1.37 1.811
2021-02-18 6303214 85567 1544 6146927 70720 6232494 1.37 1.804
2021-02-19 6345992 86128 1550 6188748 71116 6274876 1.37 1.800
2021-02-20 6390631 86576 1553 6227918 76137 6314494 1.37 1.794
2021-02-21 6411340 86992 1557 6250992 73356 6337984 1.37 1.790
2021-02-22 6429144 87324 1562 6269359 72461 6356683 1.37 1.789
2021-02-23 6472679 87680 1573 6310934 74065 6398614 1.37 1.794
2021-02-24 6510988 88120 1576 6347880 74988 6436000 1.37 1.788
2021-02-25 6551214 88516 1581 6394026 68672 6482542 1.37 1.786
2021-02-26 6590066 88906 1585 6432218 68942 6521124 1.36 1.783
2021-02-27 6627215 89320 1595 6468906 68989 6558226 1.36 1.786
2021-02-28 6649006 89674 1603 6486443 72889 6576117 1.36 1.788
2021-03-01 6665755 90029 1605 6501981 73745 6592010 1.37 1.783
2021-03-01 6665755 90028 1605 6501982 73745 6592010 1.37 1.783
2021-03-02 6681976 90372 1606 6518172 73432 6608544 1.37 1.777
2021-03-03 6716203 90816 1612 6559520 65867 6650336 1.37 1.775
2021-03-04 6751900 91240 1619 6600571 60089 6691811 1.36 1.774
2021-03-05 6789011 91637 1627 6633667 63707 6725304 1.36 1.775
2021-03-06 6821943 92055 1632 6664717 65171 6756772 1.36 1.773
2021-03-07 6843126 92471 1634 6684259 66396 6776730 1.36 1.767
2021-03-08 6861809 92817 1642 6701598 67394 6794415 1.37 1.769
2021-03-09 6902984 93263 1645 6739220 70501 6832483 1.36 1.764
2021-03-10 6938884 93733 1648 6774873 70278 6868606 1.36 1.758
2021-03-11 6976985 94198 1652 6814539 68248 6908737 1.36 1.754
2021-03-12 7012664 94686 1662 6849240 68738 6943926 1.36 1.755
2021-03-13 7046782 95176 1667 6883732 67874 6978908 1.36 1.751
2021-03-14 7066401 95635 1669 6900923 69843 6996558 1.37 1.745
2021-03-15 7084940 96017 1675 6917333 71590 7013350 1.37 1.744
2021-03-16 7126077 96380 1678 6951653 78044 7048033 1.37 1.741
2021-03-17 7171510 96849 1686 6991199 83462 7088048 1.37 1.741
2021-03-18 7218087 97294 1688 7031071 89722 7128365 1.36 1.735
2021-03-19 7264941 97757 1690 7078843 88341 7176600 1.36 1.729
2021-03-20 7308950 98209 1693 7117723 93018 7215932 1.36 1.724
2021-03-21 7332714 98665 1696 7135741 98308 7234406 1.36 1.719
2021-03-22 7355964 99075 1697 7153757 103132 7252832 1.37 1.713
2021-03-23 7400990 99418 1704 7201400 100172 7300818 1.36 1.714
2021-03-24 7441210 99846 1707 7247038 94326 7346884 1.36 1.710
2021-03-25 7485859 100276 1709 7298600 86983 7398876 1.36 1.704
2021-03-26 7529403 100770 1716 7351224 77409 7451994 1.35 1.703
2021-03-27 7572568 101275 1721 7396119 75174 7497394 1.35 1.699
2021-03-28 7595596 101757 1722 7415234 78605 7516991 1.35 1.692
2021-03-29 7616330 102135 1726 7435257 78938 7537392 1.36 1.690
2021-03-30 7663999 102582 1729 7482993 78424 7585575 1.35 1.685
2021-03-31 7707800 103088 1731 7533410 71302 7636498 1.35 1.679
2021-04-01 7747303 103636 1735 7575008 68659 7678644 1.35 1.674
2021-04-02 7788295 104193 1737 7613350 70752 7717543 1.35 1.667
2021-04-03 7829601 104736 1740 7649577 75288 7754313 1.35 1.661
2021-04-04 7849476 105279 1744 7664398 79799 7769677 1.35 1.657
2021-04-05 7868820 105752 1748 7682571 80497 7788323 1.36 1.653
2021-04-06 7921290 106230 1752 7735905 79155 7842135 1.35 1.649
2021-04-07 7966167 106898 1756 7784627 74642 7891525 1.35 1.643
2021-04-08 8012421 107598 1758 7826829 77994 7934427 1.36 1.634
2021-04-09 8059113 108268 1764 7868933 81912 7977201 1.36 1.629
2021-04-10 8106630 108945 1765 7907671 90014 8016616 1.36 1.620

Rコード

データから検査陽性率、暫定致死率を計算し、表を作成

library(knitr)
date<- seq(as.Date("2020-02-01"), as.Date("2021-04-10"), by = "day")
検査を受けた人<-c(371,429,429,607,714,885,1130,1701,2340,2776,3629,5074,5797,6854,7519,7919,8171,
    9265,10411,12161,14816,19621,22633,28615,36716,46127,57990,70940,85693,96985,109591,125851,
    136707,146541,164740,178189,188518,196618,210144,222395,234998,    248647,261335,268212,274504,
    286716,295647,307024,316664,327509,331780,338036,348582,357896,364942,376961,387925,394141,
    395194,410564,421547,431743,443273,455032,461233,466804,477304,486003,494711,503051,510479,
    514621,518743,527438,534552,538775,546463,554834,559109,563035,571014,577959,583971,589520,
    595161,598285,601660,608514,614197,619881,623069,627562,630973,633921,640237,643095,649388,
    654863,660030,663886,668492,680890,695920,711484,726747,740645,747653,753211,765574,776433,
    788684,802418,814420,820289,826437,839475,852876,868666,885120,902901,910822,921391,939851,
    956852,973858,990960,1005305,1012769,1018214,1035997,1051972,1066888,1081486,1094704,1100327,
    1105719,1119767,1132823,1145712,1158063,1170901,1176463,1182066,1196012,1208597,1220478,1232315,
    1243780,1251695,1259954,1273766,1285231,1295962,1307761,1319523,1326055,1331796,1346194,1359735,
    1371771,1384890,1396941,1402144,1408312,1420616,1431316,1441348,1451017,1460204,1465299,1470193,
    1482390,1492071,1500854,1510327,1518634,1522926,1526974,1537704,1547307,1556215,1563796,1571830,
    1576246,1579757,1589780,1598187,1606487,1613652,1620514,1624650,1628303,1637844,1646652,1654898,
    1665084,1675296,1681787,1688470,1697042,1715064,1734083,1754123,1775800,1791186,1804422,1825837,
    1849506,1869579,1887717,1909329,1924170,1937689,1959080,1980295,2000552,2018906,2037045,2045935,
    2051297,2066078,2082234,2099591,2119211,2135457,2143270,2151002,2164578,2178832,2191892,2206365,
    2219162,2226701,2231589,2245112,2256899,2268999,2280276,2290345,2296517,2301303,2313044,2322999,
    2328435,2333777,2339859,2346345,2352378,2365433,2378073,2388844,2400233,2404684,2410483,2415610,
    2428771,2441443,2450739,2459426,2468527,2474529,2479226,2491311,2503489,2515325,2528621,2540679,
    2546146,2552264,2567587,2582960,2597978,2612231,2624492,2630630,2636650,2649859,2662260,2673706,
    2686314,2697249,2702880,2709199,2723960,2736534,2749772,2761411,2777289,2786878,2797691,2815755,
    2834362,2853843,2873443,2896746,2908890,2922135,2946399,2966405,2988046,3009562,3032003,3046910,
    3061172,3083997,3106970,3131886,3157410,3180496,3194867,3209376,3221317,3253220,3277948,3311213,
    3349864,3374595,3397039,3441220,3488769,3538840,3589797,3646247,3683094,3713861,3772430,3826570,
    3882210,3939357,3969415,4006412,4038307,4098179,4159522,4213880,4269318,4302799,4340838,4376608,
    4439360,4504866,4569808,4630004,4689616,4723464,4751685,4814085,4872312,4925359,4978074,5032270,
    5061290,5087220,5140325,5192128,5237606,5282223,5329707,5354349,5376086,5422763,5469247,5522189,
    5569264,5616530,5640818,5661842,5711413,5756714,5803095,5847178,5893353,5916975,5938197,5987405,
    6030023,6070008,6093369,6115337,6140086,6162860,6213490,6260567,6303214,6345992,6390631,6411340,
    6429144,6472679,6510988,6551214,6590066,6627215,6649006,6665755,6681976,6716203,6751900,6789011,
    6821943,6843126,6861809,6902984,6938884,6976985,7012664,7046782,7066401,7084940,7126077,7171510,
    7218087,7264941,7308950,7332714,7355964,7400990,7441210,7485859,7529403,7572568,7595596,7616330,
    7663999,7707800,7747303,7788295,7829601,7849476,7868820,7921290,7966167,8012421,8059113,8106630)
感染者数<-c(12,15,15,16,18,23,24,24,25,27,28,28,28,28,28,29,30,31,46,82,156,346,556,763,893,1146,
    1595,2022,2931,3526,4212,4812,5328,5766,6284,6767,7134,7382,7513,7755,7869,7979,8086,8162,
    8236,8320,8413,8565,8652,8799,8897,8961,9037,9137,9241,9332,9478,9583,9661,9786,9887,9976,
    10062,10156,10237,10284,10331,10384,10423,10450,10480,10512,10537,10564,10591,10613,10635,
    10653,10661,10674,10683,10694,10702,10708,10718,10728,10738,10752,10761,10765,10774,10780,
    10793,10801,10804,10806,10810,10822,10840,10874,10909,10936,10962,10991,11018,11037,11050,
    11065,11078,11110,11122,11142,11165,11190,11206,11225,11265,11344,11402,11441,11468,11503,
    11541,11590,11629,11668,11719,11776,11814,11852,11902,11947,12002,12051,12084,12121,12155,
    12198,12257,12306,12373,12421,12438,12484,12535,12563,12602,12653,12715,12757,12799,12850,
    12904,12967,13030,13089,13137,13181,13243,13293,13338,13373,13417,13479,13512,13551,13612,
    13672,13711,13745,13771,13816,13879,13938,13979,14092,14150,14175,14203,14251,14269,14305,
    14336,14366,14389,14423,14456,14499,14519,14562,14598,14626,14660,14714,14770,14873,15039,
    15318,15515,15761,16058,16346,16670,17002,17399,17665,17945,18265,18706,19077,19400,19699,
    19947,20182,20449,20644,20842,21010,21177,21296,21432,21588,21743,21919,22055,22176,22285,
    22391,22504,22657,22783,22893,22975,23045,23106,23216,23341,23455,23516,23611,23661,23699,
    23812,23889,23952,24027,24091,24164,24239,24353,24422,24476,24548,24605,24703,24805,24878,
    24988,25035,25108,25199,25275,25333,25422,25543,25698,25775,25836,25955,26043,26146,26271,
    26384,26511,26635,26732,26807,26925,27050,27195,27284,27427,27553,27653,27799,27942,28133,
    28338,28546,28768,28998,29311,29654,30017,30403,30733,31004,31353,31735,32318,32871,33374,
    33763,34201,34652,35163,35703,36332,36915,37546,38161,38746,39416,40097,40786,41736,42766,
    43484,44364,45439,46453,47517,48568,49665,50591,51458,52548,53529,54770,55902,56872,57679,
    58723,59773,60740,61769,62593,63244,64264,64978,65816,66684,67358,67999,68664,69114,69650,
    70204,70728,71240,71820,72340,72729,73114,73517,73916,74261,74692,75084,75521,75870,76429,
    76926,77395,77850,78203,78508,78844,79311,79761,80131,80524,80896,81184,81486,81930,82434,
    82837,83199,83525,83868,84325,84946,85567,86128,86576,86992,87324,87680,88120,88516,88906,
    89320,89674,90028,90372,90816,91240,91637,92055,92471,92817,93263,93733,94198,94686,95176,
    95635,96017,96380,96849,97294,97757,98209,98665,99075,99418,99846,100276,100770,101275,101757,
    102135,102582,103088,103636,104193,104736,105279,105752,106230,106898,107598,108268,108945)
死者<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,4,7,8,11,12,13,16,17,22,28,32,35,42,44,50,51,
    54,60,66,67,72,75,75,81,84,91,94,102,104,111,120,126,131,139,144,152,158,162,165,169,174,
    177,183,186,192,200,204,208,211,214,217,222,225,229,230,232,234,236,237,238,240,240,240,242,
    243,244,246,247,248,250,250,252,254,255,256,256,256,256,256,258,259,260,260,262,262,263,263,
    263,264,264,266,266,267,269,269,269,269,269,270,271,272,273,273,273,273,273,273,274,276,276,
    277,277,277,277,278,279,280,280,280,280,280,281,281,282,282,282,282,282,282,282,282,282,283,
    283,284,285,285,287,288,288,289,289,289,289,291,293,294,295,296,296,297,297,298,298,298,299,
    300,300,300,301,301,301,301,301,302,302,303,304,305,305,305,305,305,305,305,305,305,306,306,
    307,309,309,309,309,310,312,313,316,321,323,324,324,326,329,331,333,334,336,341,344,346,350,
    355,358,363,367,367,372,377,378,383,385,388,388,393,395,399,401,406,407,413,415,416,420,421,
    422,422,425,427,428,430,432,433,434,438,439,441,443,444,444,447,450,453,455,457,457,457,460,
    461,462,463,464,466,468,472,474,475,476,477,478,480,485,487,487,488,492,493,494,494,496,498,
    501,503,505,509,510,513,515,516,522,523,526,526,526,529,536,540,545,549,552,556,564,572,578,
    580,587,600,612,634,645,659,674,698,722,739,756,773,793,808,819,859,879,900,917,942,962,981,
    1007,1027,1046,1081,1100,1125,1140,1165,1185,1195,1217,1236,1249,1264,1283,1300,1316,1328,1337,
    1349,1360,1371,1378,1386,1399,1414,1420,1425,1435,1441,1448,1459,1464,1471,1474,1482,1486,1496,
    1507,1514,1522,1527,1534,1538,1544,1550,1553,1557,1562,1573,1576,1581,1585,1595,1603,1605,1606,
    1612,1619,1627,1632,1634,1642,1645,1648,1652,1662,1667,1669,1675,1678,1686,1688,1690,1693,1696,
    1697,1704,1707,1709,1716,1721,1722,1726,1729,1731,1735,1737,1740,1744,1748,1752,1756,1758,1764,
    1765)
陰性<-c(289,327,414,462,522,693,842,1057,1355,1940,2736,4054,5099,6134,6853,7313,7733,8277,9335,10446,
    11953,13794,16038,19127,22550,28247,35298,44167,53608,61037,71580,85484,102965,118965,136624,
    151802,162008,171778,184179,196100,209402,222728,235615,243778,251297,261105,270888,282555,
    292487,303006,308343,315447,324105,334481,341332,352410,361883,369530,372002,383886,395075,
    403882,414303,424732,431425,437225,446323,457761,468779,477303,485929,490321,494815,502223,
    508935,513894,521642,530631,536205,540380,547610,555144,563130,569212,575184,578558,582027,
    588559,595129,600482,603610,608286,611592,614944,620575,624280,630149,635174,640037,642884,
    646661,653624,665379,679771,695854,711265,718943,726053,737571,748972,759473,770990,781686,
    788766,796142,806206,820550,834952,849161,865162,876060,885830,899388,917397,934030,950526,
    965632,974512,982026,996686,1013847,1029447,1045240,1059301,1066887,1072805,1084980,1099136,
    1111741,1124567,1137058,1143971,1150225,1161250,1175817,1189015,1200885,1211261,1219975,1228698,
    1240157,1252855,1263276,1273234,1284172,1291315,1297367,1309338,1322479,1334566,1348025,1360618,
    1366897,1372988,1382815,1394468,1404332,1414235,1423570,1429601,1435120,1444710,1456441,1465498,
    1475789,1484861,1489562,1494029,1503057,1513730,1522928,1531161,1539216,1544112,1547967,1556633,
    1565241,1573957,1582065,1589847,1593249,1597281,1605695,1614563,1622330,1630079,1638639,1644464,
    1649991,1656062,1667984,1682739,1699408,1716371,1726223,1738762,1757530,1778446,1798832,1817929,
    1835883,1846450,1859866,1882155,1903098,1924384,1945798,1963958,1975137,1982892,2001276,2024113,
    2044830,2067869,2085576,2093389,2101241,2114877,2130486,2143727,2158179,2171564,2179452,2186008,
    2198784,2213156,2224876,2237246,2248321,2254028,2259055,2269289,2278591,2284517,2289830,2294545,
    2300138,2305767,2318457,2331582,2343444,2355058,2361154,2366574,2371715,2383842,2398169,2407489,
    2417036,2426101,2431549,2437188,2446599,2458574,2469969,2482493,2493016,2498628,2505546,2518541,
    2533910,2545559,2559474,2572303,2579157,2584394,2597237,2611080,2621594,2633490,2643748,2649805,
    2655844,2668452,2680047,2692546,2703159,2714259,2721954,2730609,2745555,2762363,2778664,2795283,
    2814998,2824128,2834676,2857277,2878832,2900367,2920054,2939835,2950318,2963606,2984453,3006653,
    3030395,3057061,3077314,3089605,3103205,3111297,3138724,3165087,3192739,3221386,3241700,3264308,
    3303383,3348546,3393129,3431662,3471446,3493954,3516832,3569843,3620913,3678148,3727798,3756501,
    3782564,3810749,3868494,3927978,3982802,4030622,4060819,4091122,4121612,4180631,4246968,4311362,
    4372194,4435694,4463346,4492808,4557665,4619194,4684889,4744511,4804811,4829565,4856456,4912866,
    4970470,5022620,5074830,5118386,5143500,5166016,5208269,5254391,5305839,5349116,5392885,5413065,
    5433878,5478304,5538554,5602796,5659590,5715152,5745328,5772170,5823289,5868017,5905960,5929671,
    5961887,5978361,5998846,6052268,6103211,6146927,6188748,6227918,6250992,6269359,6310934,6347880,
    6394026,6432218,6468906,6486443,6501982,6518172,6559520,6600571,6633667,6664717,6684259,6701598,
    6739220,6774873,6814539,6849240,6883732,6900923,6917333,6951653,6991199,7031071,7078843,7117723,
    7135741,7153757,7201400,7247038,7298600,7351224,7396119,7415234,7435257,7482993,7533410,7575008,
    7613350,7649577,7664398,7682571,7735905,7784627,7826829,7868933,7907671)
検査中<- 検査を受けた人- (陰性+感染者数)
#df<- data.frame(date,感染者数,死者,検査を受けた人_感染者除く,陰性,検査中)
#kable(df,row.names=F)
df<- data.frame(検査を受けた人,感染者数,死者,陰性,検査中)
rownames(df)<- date
#
# 陽性率、暫定致死率を計算
結果判明 <- 感染者数 + 陰性
陽性率 <- round(感染者数/結果判明*100,2)
暫定致死率 <- round(死者/感染者数*100,3)
#
df2 <- data.frame(結果判明,感染者数,陰性,陽性率,死者,暫定致死率)
colnames(df2)<- c("結果判明","感染者数","陰性","陽性率(%)","死者","暫定致死率(%)")
rownames(df2)<- date
#kable(df2)
kable(merge(df,df2[,c(1,4,6)],by=0))

新型コロナウイルスのPCR検査実施人数と感染状況(韓国)

#日本のPCR検査実施人数と結果(結果判明した数)
Jpcr1<- c(rep(NA,6),151,NA,NA,174,NA,190,200,214,NA,NA,487,523,532)+c(rep(NA,6),566,NA,NA,764,NA,764,764,764,NA,NA,764,764,764)
# 3/19 : PCR検査実施人数が減少したのは、千葉県が人数でなく件数でカウントしていたことが判明したため、千葉県の件数を引いたことによる
Jpcr2<- c(603,693,778,874,913,1017,1061,1229,1380,1510,1688,1784,1855,5690,5948,6647,7200,7347,7457,8771,9195,9376,11231,
    12090,12197,12239,14322,14525,14072,18015,18134,18226+1173,18322+1189,22184+1417,21266+1426,22858+1484,24663+1513,
    26105+1530,26401+1530,26607+1530,30088+1580,32002+1677,32002+1679,36687+1930,39992+2061,40263+3547,40481+4862,48357+6125,
    52901+7768,54284+9274,57125+10817,61991+12071,63132+13420,63132+14741,72801+15921,76425+16982,81825+18049,86800+18743,
    91050+19446,91695+20292,94826+21070,101818+21903,107430+22328,112108+23046,117367+23404,122700+23925,123633+24612,
    124456+25407,133578+26139,136695+26731,137338+27442,145243+28078,152029+28669,153047+29375,153581+30176,154646+30868,156866+31232,
    157563+31638,169546+31638,179043+32125,180478+32949,183845+33530,188646+34174,188031+34807,196816+35499,194323+35730,
    203284+36255,206790+36833,211832+37490,216624+38222,218744+38704,221397+39346,224972+39701,227445+40203,229669+40703,
    230562+41297,233399+41942,235422+42391,237367+43097,240334+43600,242734+44191,244824+44783,246100+45640,248662+46892,
    251808+47640,254229+48575,257330+49303,260551+50067,262642+51012,263962+52057,267069+53155,269976+53870,273204+54697,
    276032+55346,279184+56148,280720+57209,281697+58392,284092+59605,286739+60483,334250+61354,340426+62125,345249+62944,
    347723+64117,351850+65272,357226+66712,360948+67704,365927+68739,370382+69528,375140+70620,378673+72270,380186+73594,
    385696+74921,390347+76268,396911+77568,402371+78677,408968+80490,412102+82233,414720+84349,422948+86216,430047+87334,
    438166+88551,449671+89911,459538+91291,464900+92893,466738+94538,477290+96313,488444+97433,499787+98623,516470+99750,
    524327+100993,531493+102675,534755+104237,550714+105992,562828+106962,579185+108022,587495+109188,593190+110381,
    602720+111734,606505+113167,630687+114713,648773+115744,667442+116953,687129+118109,704483+119436,714089+121486,
    724688+123598,750482+125823,769222+127289,808945+128965,830960+130126,853658+131614,867334+133118,880670+134698,
    887110+136296,920049+137286,945831+138662,966893+139815,1022133+141067,1033883+142601,1041321+144318,1067156+146160,
    1088286+147065,1110223+148370,1132112+149752,1153978+151321,1167326+152924,1178279+154776,1200330+156469,1218568+157386,
    1237921+158650,1259467+160293,1278064+161933,1290979+163802,1300556+165747,1322085+168061,1340216+169417,1364339+171053,
    1387330+172346,1402787+173828,1414717+175349,1418892+176840,1442904+178416,1457541+179353,1480613+180649,1502919+181963,
    1519398+183507,1529943+185188,1530415+186811,1555861+188621,1576024+189625,1594338+191175,1617651+193036,1635256+195161,
    1644786+196891,1650939+198893,1653717+200694,1657103+201761,1684028+203299,1703049+204829,1731575+206423,1742097+208323,
    1746647+210497,1766397+212703,1870073+213992,1889535+215599,1907206+217727,1929066+220189,1942750+222552,1947678+224528,
    1969543+226459,1989978+227846,2011379+229421,2032776+231078,2052473+232768,2063276+234940,2068353+236851,2091588+238594,
    2111416+239986,2133253+241845,2153990+243589,2174691+245523,2184587+248249,2189710+250611,2211630+253054,2227116+254540,
    2249260+256307,2272678+258464,2295558+260827,2308769+263897,2314196+266431,2336798+268572,2362512+270262,2385576+272181,
    2406080+274758,2427862+277477,2439260+281216,2445474+284221,2467009+287608,2474486+288777,2496326+290388,2525859+291995,
    2554096+291443,2567556+293163,2575560+294737,2601036+296556,2626215+297511,2659896+298849,2689808+300427,2713846+301670,
    2730497+303998,2739624+305471,2769733+307137,2797367+308363,2831997+309838,2869807+311568,2911577+313261,2936501+315193,
    2956890+316935,2966450+319044,3010223+320340,3051275+321959,3093909+323782,3137260+325583,3159567+328270,3171542+330165,
    3212877+332210,3257166+333602,3300402+335281,3337225+337190,3375370+338931,3397683+341634,3415295+344181,3453032+346828,
    3493092+348603,3547649+350491,3592261+352740,3646095+354630,3674298+357689,3687442+360439,3741200+363619,3787017+365743,
    3841320+367647,3903759+370045,3960180+372008,3987229+374828,3999511+378727,4055161+383283,4086271+386156,4158529+388421,
    4222041+391069,4282302+393373,4314979+396043,4326942+398195,4401325+400085,4426175+402065,4447244+403864,4486819+405670,
    4511891+407177,4520637+408569,4540928+411247,4625266+413737,4713712+415071,4787679+416785,4869119+418607,4932492+420307,
    4968222+421935,4992724+425533,5012172+429314,5098723+431753,5179978+434449,5281233+436832,5351843+439975,5396505+443680,
    5412543+447118,5493316+451547,5596629+456022,5683433+460947,5769202+466023,5870475+467292,5907591+469079,5926375+470336,
    6008316+472374,6074651+473253,6165475+474662,6229807+475757,6312103+476999,6352528+478790,6370300+480301,6437569+482725,
    6496679+483761,6565486+485170,6627388+486304,6698532+487519,6731575+489260,6749766+490619,6808265+492515,6886418+493341,
    6951770+494671,6984635+495913,7041285+497289,7071505+498771,7086221+499986,7144642+501660,7204491+502466,7265629+503748,
    7320133+504849,7395088+506005,7426667+507867,7441350+509398,7495530+511480,7513854+512316,7568556+513845,7627790+515305,
    7685654+516802,7714850+519303,7734625+521148,7794715+524148,7877039+525508,7907207+527272,7976063+528983,8021216+530768,
    8052274+533048,8066683+535057,8134600+537672,8174837+538949,8234989+540423,8291840+542130,8347402+544014,8378697+546770,
    8395963+549455,8445833+553431,8493609+555715,8578528+557940,8633427+560131,8704728+562309,8735386+565106,8752383+567416,
    8803852+570164,8856567+571717,8916541+573331,8979186+575034,9046482+576766,9086073+579131,9104960+581260,9177139+584163,
    9218386+585610,9285821+587263,9351198+589611,9412344+591834,9453447+594837,9470636+596581,9530910+598885,9583929+600256,
    9642353+601670,9713312+603621,9780921+605382)+829
Jpcr<- c(Jpcr1,Jpcr2)
Confirmed<- c(rep(NA,6),25,NA,NA,26,NA,28,29,33,NA,NA,59,66,73,84,93,105,132,144,156,164,186,210,230,239,254,268,284,317,348,
    407,454,487,513,567,619,674,714,777,809,824,868,907,943,996,1046,1089,1128,1193,1291,1387,1499,1693,1866,1953,2178,
    2381,2617,2935,3271,3654,3906,4257,4768,5347,6005,6748,7255,7645,8100,8582,9167,9795,10361,10751,11119,11496,11919,
    12388,12829,13182,13385,13576,13852,14088,14281,14544,14839,15057,15231,15354,15463,15547,15649,15747,15798,15874,16024,
    16079,16193,16237,16285,16305,16365,16385,16424,16513,16536,16550,16581,16623,16651,16683,16719,16804,16851,16884,16930,
    16986,17018,17064,17103,17141,17174,17210,17251,17292,17332,17382,17429,17502,17587,17628,17668,17740,17799,17864,17916,
    17968,18024,18110,18197,18297,18390,18476,18593,18723,18874,19068,19282,19522,19775,19981,20174,20371,20719,21129,21502,
    21868,22220,22508,22890,23473,24132,24642,25096,25736,26303,27029,27956,28786,29382,29989,30961,31901,33049,34372,35836,
    36689,38687,39858,41129,42263,43815,45439,46783,47990,48928,50210,51147,52217,53577,54714,55667,56685,57550,58501,59721,
    60733,61747,62507,63121,63822,64668,65573,66423,67264,67865,68392,69001,69599,70268,70876,71419,71856,72234,72726,73221,
    73901,74544,75218,75657,75958,76448,77009,77494,78073,78657,79140,79438,79768,80041,80497,81055,81690,82131,82494,83010,
    83563,84215,84768,85339,85739,86047,86543,87020,87639,88233,88912,89347,89673,90140,90710,91431,92063,92656,93127,93480,
    93933,94524,95138,95835,96534,97074,97498,98116,98852,99622,100392,101146,101813,102281,102900,103838,104782,105914,107086,
    108084,108983,110156,111711,113298,114983,116677,118136,119326,120815,122966,125267,127665,130179,132358,133929,135400,
    137261,139491,142068,144653,146760,148694,150386,152827,155232,157674,160098,162067,163929,165840,168573,171542,174299,
    177287,179653,181870,184042,187103,190138,193031,195880,198523,200658,203113,206139,209980,213547,217312,220236,223120,
    226596,230304,234395,238012,240954,243847,247960,252317,258393,265299,273154,280775,286752,292212,297315,302623,309214,
    315910,322296,328294,334328,339774,345221,351020,356074,360661,364813,368143,371680,375607,379516,383083,386742,389518,
    391626,393836,396429,399048,401355,403435,404990,406766,408186,410012,411751,413154,414472,415782,417765,419015,420408,
    421967,423311,424507,425597,426456,427467,428553,429472,430539,431740,432773,433504,434356,435548,436728,437892,438956,
    439992,440671,441729,443001,444289,445585,446873,447906,448688,449713,451186,452702,454158,455638,456781,457754,459043,
    460897,462840,464866,466849,468614,470175,472112,474773,477458,480165,482867,485325,487545,489576,492875,496206,499793)
Deaths<- c(rep(NA,6),0,NA,NA,0,NA,0,0,1,NA,NA,1,1,1,1,1,1,1,1,1,1,3,4,5,5,6,6,6,6,6,6,6,7,9,12,15,19,21,22,24,28,29,31,33,35,36,
    41,42,43,45,46,49,52,54,56,57,60,63,69,70,73,80,81,85,88,94,98,102,109,119,136,148,154,161,171,186,277,287,317,334,348,
    351,376,389,415,432,458,492,510,521,543,551,557,600,613,621,643,668,687,710,725,744,749,763,771,777,796,808,820,830,846,
    858,867,874,886,891,892,894,900,903,907,914,916,916,916,919,920,922,924,925,925,927,931,935,935,952,953,953,955,963,968,
    969,971,971,972,972,974,975,976,977,977,977,978,980,981,982,982,982,982,982,984,985,985,985,985,985,988,989,990,992,993,
    996,996,998,1001,1004,1006,1011,1011,1012,1016,1022,1026,1033,1039,1040,1047,1052,1059,1063,1073,1085,1088,1099,1115,1128,
    1144,1155,1169,1176,1181,1196,1209,1226,1238,1255,1264,1279,1296,1307,1319,1330,1349,1357,1363,1377,1393,1406,1412,1423,1439,
    1442,1451,1461,1473,1482,1495,1500,1500,1508,1512,1520,1532,1540,1545,1548,1557,1564,1571,1578,1590,1597,1599,1602,1605,1613,
    1616,1624,1627,1629,1634,1638,1646,1650,1661,1670,1674,1676,1679,1685,1694,1706,1711,1718,1725,1730,1733,1744,1755,1766,1774,
    1780,1786,1794,1806,1809,1812,1818,1829,1841,1851,1867,1880,1883,1885,1903,1913,1922,1943,1963,1974,1981,1989,2001,2022,2051,
    2074,2106,2119,2139,2172,2213,2240,2283,2315,2335,2382,2420,2465,2502,2534,2562,2585,2643,2688,2739,2783,2828,2873,2900,2944,
    2994,3050,3105,3155,3213,3252,3306,3349,3414,3460,3514,3548,3599,3655,3719,3791,3857,3932,3996,4044,4094,4145,4233,4315,4380,
    4446,4501,4548,4647,4743,4830,4935,5019,5084,5158,5252,5361,5452,5546,5654,5722,5794,5912,6020,6135,6243,6338,6395,6476,6557,
    6678,6774,6849,6912,6952,7015,7102,7196,7274,7333,7417,7474,7529,7584,7647,7722,7807,7860,7887,7933,7984,8052,8119,8178,8227,
    8253,8299,8353,8402,8451,8509,8560,8590,8622,8678,8717,8758,8790,8812,8835,8861,8908,8938,8967,8998,9031,9061,9086,9113,9162,
    9185,9213,9221,9231,9249,9279,9301,9334,9353)
Jdf<- data.frame(Tested=Jpcr,Confirmed,Deaths)
kj<-paste0(round(結果判明[length(結果判明)]/max(Jpcr,na.rm=T),2),"倍")
#
Jpop<- 127103388
Kpop<- 49039986
#
jp<- round(max(Jpcr2,na.rm=T)*1000000/Jpop,0)
kr<- round(max(結果判明,na.rm=T)*1000000/Kpop,0)
#
# 指数表示を抑制
options(scipen=2) 
#png("pcr04.png",width=800,height=600)
par(mar=c(4,6,4,2),family="serif")
b<- barplot(t(df[,c(2,4,5)]),col=c("red","lightblue","gray80"),
    ylim= c(0,max(Jpcr2,na.rm=T)*1.1),yaxt ="n",
    legend=T,args.legend = list(x="topleft",inset=c(0.03,0.03)),axisnames=F)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",rownames(df)))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- which(labels== i)
    axis(1,at=b[at],labels = rep(paste0(sub("/1","",i),"月"),length(at)),tck= -0.02)    
    }
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[336],side=1,line=2.5,cex=1.2) 
# Add comma separator to axis labels
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)  
#text(x=b,y=検査を受けた人,labels= 検査を受けた人,pos=3,col="blue")
#points(x=b,y=Jpcr,pch=16)
lines(x=b,y=Jpcr,pch=16,lwd=2)
legend(x="topleft",inset=c(0.03,0.2),bty="n",legend="日本のPCR検査実施人数(データ:厚生労働省HP)\n(注意)一部自治体について件数を計上しているため、実際の人数より過大である。",lwd=2)
legend(x="topleft",inset=c(0.01,0.28),bty="n",legend="* 韓国の人口は日本の約41%",cex=1.2)
legend(x="topleft",inset=c(0.01,0.33),bty="n",legend=paste("* PCR検査で結果判明した数は日本の",kj),cex=1.2)
legend(x="topleft",inset=c(0.01,0.38),bty="n",legend="(日本:チャーター便帰国者及び空港検疫も含む)",cex=1.2)
legend("topleft",inset=c(0.27,-0.05),cex=1.5,bty="n",
    legend=paste("PCR検査数(人口100万あたり)\n ・日本:",formatC(jp, format="d", big.mark=','),"人\n ・韓国:",formatC(kr, format="d", big.mark=','),"人(結果判明した数)"),text.col="blue")
title("韓国と日本のPCR検査実施人数の推移",cex.main=2)
#dev.off()

日本と韓国の新型コロナウイルスによる死亡者数推移(累計で計算)

# 日本、韓国の人口
# DataComputingパッケージの"CountryData"より
Jpop<- 127103388
Kpop<- 49039986
#
Jpos <- Deaths
#
jp<- round(max(Jpos,na.rm=T)*1000000/Jpop,2)
kr<- round(max(死者,na.rm=T)*1000000/Kpop,2)
#
ylim<- c(0,max(c(Jpos,死者),na.rm=T)*1.1)
#png("pcr04_2.png",width=800,height=600)
par(mar=c(5,4,4,2),family="serif")
plot(死者,type="l",col="blue",lwd=2,xaxt="n",xlab="",yaxt="n",ylab="",las=1,ylim=ylim,bty="n")
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1)  
box(bty="l",lwd=2)
text(x=par("usr")[1],y=par("usr")[4]*1.02,labels="(人)",pos=2,xpd=T)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(rep(1:12,each=1),"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = i,tck= -0.02)}
    }
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
legend("topleft",inset=c(0,0.15),cex=1.5,bty="n",
    legend=paste("新型コロナウイルスによる死亡者数(人口100万あたり)\n ・日本:",jp,"(5/14 韓国を追い抜く)\n ・韓国:",kr))
points(x=c(180,299,328,346,359,370,382,398,422),y=c(1001,2001,3050,4044,5019,6020,7015,8052,9031),pch=16,col="red")
text(x=c(180,299,328,346,359,370,382,398,422),y=c(1001,2001,3050,4044,5019,6020,7015,8052,9031),labels=c("7/29\n1001人","11/25\n2001人","12/24\n3050人",
    "1/11\n4044人","1/24\n5019人","2/4\n6020人","2/16\n7015人","3/4\n8052人","3/28\n9031人"),pos=3)
points(x=c(180,299,328,346,359,370,382,398,422),y=c(300,513,756,1140,1349,1448,1534,1619,1722),pch=16,col="blue")
text(x=c(180,299,328,346,359,370,382,398,422),y=c(300,513,756,1140,1349,1448,1534,1619,1722),labels=c("7/29\n300人","11/25\n513人","12/24\n756人",
    "1/11\n1140人","1/24\n1349人","2/4\n1448人","2/16\n1534人","3/4\n1619人","3/28\n1722人"),pos=c(rep(3,4),1,3,1))
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2) 
title("日本と韓国の新型コロナウイルスによる死亡者数推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()

日本と韓国のPCR検査の検査陽性率(%)推移(累計で計算)

Jpos <- round(Confirmed/Jpcr*100,2)
ylim<- c(0,max(c(Jpos,df2$"陽性率(%)"),na.rm=T)*1.1)
#png("pcr05.png",width=800,height=600)
par(mar=c(5,6,4,4),family="serif")
plot(df2$"陽性率(%)",type="l",col="blue",lwd=2,xaxt="n",xlab="",ylab="陽性率(%)",las=1,ylim=ylim,bty="n")
box(bty="l",lwd=2)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)} #,hadj =0
    }
legend("topright",inset=0.03,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
text(x=par("usr")[2],y=c(tail(df2$"陽性率(%)",1),tail(Jpos,1)),labels=paste0(c(tail(df2$"陽性率(%)",1),tail(Jpos,1)),"%"),xpd=T)
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2) 
title("日本と韓国のPCR検査の検査陽性率(%)の推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()

日本と韓国のPCR検査の暫定致死率(%)推移(累計で計算)

Jpos <- round(Deaths/Confirmed*100,2)
ylim<- c(0,max(c(Jpos,df2$"暫定致死率(%)"),na.rm=T)*1.1)
#png("pcr06.png",width=800,height=600)
par(mar=c(5,6,4,2),family="serif")
plot(df2$"暫定致死率(%)",type="l",col="blue",lwd=2,xaxt="n",xlab="",ylab="暫定致死率(%)",las=1,ylim=ylim,bty="n")
box(bty="l",lwd=2)
lines(Jpos,col="red",lwd=2)
#points(Jpos,col="red",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date))))
labelpos<- paste0(rep(1:12,each=1),"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = i,tck= -0.02)}
    }
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=336,side=1,line=2.5,cex=1.2) 
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
title("日本と韓国の暫定致死率(%)の推移","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()

週単位の陽性者増加比(日本、韓国)

library(TTR)
jpkr<- data.frame(Japan=Confirmed,"South_Korea"=感染者数)
rownames(jpkr)<- sub("-","/",sub("-0","-",sub("^0","",sub("2020-","",date))))
#(日本のデータ)欠損値が多い箇所は外す
jpkr<- jpkr[17:nrow(jpkr),]
#
#grep("TRUE",is.na(jpkr$Japan))
#jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))]<- (jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))-1]+jpkr$Japan[grep("TRUE",is.na(jpkr$Japan))+1])/2
#
x<- apply(jpkr,2,diff)
fun<- function(x){round(runSum(x,n=7)/(runSum(x,n=14) -runSum(x,n=7)),2)}
jpkr2<- apply(x,2,fun)
# InfにNAを入れる
#jpkr2[jpkr2==Inf]<- NA
jpkr2<- data.frame(jpkr2)
rownames(jpkr2)<- rownames(jpkr)[-1]
#
pdat<- jpkr2[14:nrow(jpkr2),]
#png("pcr06_2.png",width=800,height=600)
par(mar=c(5,6,4,7),family="serif")
matplot(pdat,type="l",lwd=2,las=1,lty=1,ylim=c(0,max(pdat)),col=c("red","blue"),xlab="",ylab="",xaxt="n",bty="n")
box(bty="l",lwd=2.5)
abline(h=1,lty=2,col="darkgreen",lwd=1.5)
labels<-gsub("^.*/","",rownames(pdat))
pos<-gsub("/.*$","",rownames(pdat))
pos<- factor(pos,levels=min(as.numeric(pos)):max(as.numeric(pos)))
#axis(1,at=1:nrow(pdat),labels =NA)
#月の区切り
#axis(1,at=cumsum(as.vector(table(pos)))+0.5, labels =NA,tck=-0.1,lty=2 ,lwd=1)
for (i in c("1","10","20")){
    at<- grep("TRUE",is.element(labels,i))
    axis(1,at=at,labels = rep(i,length(at)))
    }
#Month<-c("January","February","March","April","May","June","July","August","September","October","November","December")
Month<-c("Jan.","Feb.","Mar.","Apr.","May","Jun.","Jul.","Aug.","Sep.","Oct.","Nov.","Dec.")
#cut(1:12,breaks = seq(0,12),right=T, labels =Month)
mon<-cut(as.numeric(names(table(pos))),breaks = seq(0,12),right=T, labels =Month)
# 月の中央
#mtext(text=mon,at=cumsum(as.vector(table(pos)))-as.vector(table(pos)/2),side=1,line=2) 
# 月のはじめ
mtext(text=mon,at=1+cumsum(as.vector(table(pos)))-as.vector(table(pos)),side=1,line=2) 
text(x=par("usr")[2],y=pdat[nrow(pdat),],labels=colnames(pdat),xpd=T,pos=1)
arrows(par("usr")[2]*1.08, 1.1,par("usr")[2]*1.08,1.68,length = 0.2,lwd=2.5,xpd=T)
text(x=par("usr")[2]*1.08,y=1.9,labels="増加\n傾向",xpd=T)
arrows(par("usr")[2]*1.08, 0.9,par("usr")[2]*1.08,0.32,length = 0.2,lwd=2.5,xpd=T)
text(x=par("usr")[2]*1.08,y=0.1,labels="減少\n傾向",xpd=T)
title("週単位の陽性者増加比(日本、韓国)",cex.main=1.5)
#dev.off()

韓国のPCR検査の結果(日別)

dat<-rbind(diff(df2$感染者数),diff(df2$陰性))
rownames(dat)<- c("陽性","陰性")
colnames(dat)<- rownames(df2[-1,])
#png("pcr08.png",width=800,height=600)
par(mar=c(6,6,4,2),family="serif")
b<- barplot(dat,col=c("red","lightblue"),las=1,legend=T,ylim=c(0,max(dat,na.rm=T)*1.1),
    args.legend=list(x="topleft",inset=c(0.03,0.03)),axisnames=F)
box(bty="l",lwd=2)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",colnames(dat)))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2) 
title("韓国のPCR検査の結果(日別)",cex.main=2)
#dev.off()

日本と韓国の検査陽性者数(日別)

ylim<- max(max(diff(Jdf$Confirmed),na.rm=T),max(diff(df$感染者数),na.rm=T))*1.2
#png("pcr07.png",width=800,height=600)
par(mar=c(6,6,4,2),family="serif")
b<- barplot(diff(Jdf$Confirmed),col=rgb(1,0,0,alpha=0.5),axes=F,ylim=c(0,ylim))
b<- barplot(diff(df$感染者数),col=rgb(0,1,0,alpha=0.5),las=1,add=T,ylim=c(0,ylim))
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2) 
legend("topleft",inset=c(0.03,0.08),pch=15,col=c(rgb(1,0,0,alpha=0.5),rgb(0,1,0,alpha=0.5)),legend=c("日本","韓国"),bty="n",cex=1.5)
title("日本と韓国の検査陽性者数(日別)",cex.main=1.5)
#dev.off()

日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)

kdf<- diff(結果判明)
jdf<- diff(Jpcr)
ymin<- min(min(kdf,na.rm=T),min(jdf,na.rm=T))
ymax<- max(max(kdf,na.rm=T),max(jdf,na.rm=T))
#png("pcr07_2.png",width=800,height=600)
par(mar=c(4,6,4,2),family="serif")
plot(jdf,type="l",lwd=2,col="red",xlab="",ylab="",xaxt="n",yaxt="n",bty="n",ylim=c(ymin,ymax),
    panel.first=grid(NA,NULL,lty=2,col="darkgray"))
box(bty="l",lwd=2)
lines(kdf,lwd=2,col="blue")
#points(kdf,pch=16,cex=1,col="blue")
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1) 
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
for(i in 1:(length(jdf)-1)){
    polygon(c(i,i,i+1,i+1), c(0,jdf[i],jdf[i+1], 0), col = rgb(1,0,0,alpha=0.5), lty=0)
}
for(i in 1:(length(kdf)-1)){
    polygon(c(i,i,i+1,i+1), c(0,kdf[i],kdf[i+1], 0), col = rgb(0,0,1,alpha=0.5), lty=0)
}
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=335,side=1,line=2.5,cex=1.2) 
legend("topleft",inset=0.03,pch=16,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
legend("topleft",inset=c(0.03,0.15),cex=1.2,
    legend="日本の6月18日の検査人数が極端に多いのは\n東京都が医療機関等の行った検査の検査人数を過去に遡って計上しているため",bty="n")
title("日本と韓国の検査者数(韓国の場合は「結果が判明した数」)(日別)",cex.main=1.5)
#dev.off()

日本と韓国のPCR検査の検査陽性率(%)の7日移動平均

library(TTR)
x0<- data.frame(日本のPCR検査数=Jpcr,日本の検査陽性者数=Confirmed,韓国のPCR検査数=結果判明,韓国の検査陽性者数=感染者数)
#欠損値のなくなる17番目のデータ以降
x0<- x0[-c(1:16),]
#差分
x1<- apply(x0,2,diff)
#7日間の合計
x2<- data.frame(apply(x1,2,runSum,n=7))
rownames(x2)<- date[-c(1:17)]
Jpos<- round((x2$日本の検査陽性者数/x2$日本のPCR検査数)*100,2)
Kpos<- round((x2$韓国の検査陽性者数/x2$韓国のPCR検査数)*100,2)
#
#png("pcr07_3.png",width=800,height=600)
par(mar=c(5,6,4,6),family="serif")
plot(Jpos,type="l",col="red",lwd=2,xaxt="n",xlab="",ylab="陽性率(%)",las=1,bty="n",ylim=c(0,12))
box(bty="l",lwd=2)
lines(Kpos,col="blue",lwd=2)
#points(Kpos,col="blue",pch=16)
#表示するx軸ラベルを指定
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
#axis(1,at=1,labels =labels[1],tick=F)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
legend("topright",inset=0.03,lwd=2,cex=1.5,col=c("red","blue"),legend=c("日本","韓国"),bty="n")
text(x=par("usr")[2],y=c(tail(Jpos,1),tail(Kpos,1)),labels=paste0(c(tail(Jpos,1),tail(Kpos,1)),"%"),xpd=T)
title("日本と韓国のPCR検査の検査陽性率(%)の7日移動平均","Data : 日本(厚生労働省の報道発表資料) 韓国(KCDC)",cex.main=2)
#dev.off()

韓国の(報告された)感染者数 対数表示(日別)

dat<- diff(df2$感染者数)
dat[dat==0]<- NA
dat2<- diff(df2$結果判明)
ylim<- c(0.9,60000)
#png("pcr08_2.png",width=800,height=600)
par(mar=c(4,5,4,3),family="serif")
b<- barplot(rep(NA,length(dat)),las=1,log="y",ylim=ylim)
abline(h=10^(0:4),col="darkgray",lwd=1.2,lty=3)
for (i in 1:9){
    abline(h=i*10^(0:4),col="darkgray",lwd=0.8,lty=3)
}
barplot(dat,col="red",las=1,log="y",ylim=ylim,axes=F,add=T)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
lines(x=b,y=dat2,lwd=2,col="darkgreen")
points(x=b,y=dat2,pch=16,col="darkgreen")
#text(x=par("usr")[2],y=dat2[length(dat2)],labels="結果判明",col="darkgreen",xpd=T)
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2) 
legend("topleft",inset=0.03,bty="n",legend="PCR検査結果判明\nConfirmed+Tested negative",lwd=2,lty=1,pch=16,col="darkgreen")
title("韓国の検査陽性者数 対数表示(日別)",cex.main=1.5)
#dev.off()

日本の(報告された)感染者数 対数表示(日別)

dat<- diff(Jdf$Confirmed)
dat[dat==0]<- NA
dat2<- diff(Jdf$Tested)
# 韓国のグラフにyの範囲を揃える
ylim<- c(0.9,60000)
#png("pcr08_3.png",width=800,height=600)
par(mar=c(4,5,4,3),family="serif")
b<- barplot(rep(NA,length(dat)),las=1,log="y",ylim=ylim)
abline(h=10^(0:4),col="darkgray",lwd=1.2,lty=3)
for (i in 1:9){
    abline(h=i*10^(0:4),col="darkgray",lwd=0.8,lty=3)
}
barplot(dat,col="red",las=1,log="y",ylim=ylim,axes=F,add=T)
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",date[-1]))))
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=b[at],labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[335],side=1,line=2.5,cex=1.2) 
lines(x=b,y=dat2,lwd=2,col="darkgreen")
points(x=b,y=dat2,pch=16,col="darkgreen")
legend("topleft",inset=0.03,bty="n",legend="PCR検査実施人数",lwd=2,lty=1,pch=16,col="darkgreen")
title("日本の検査陽性者数 対数表示(日別)",cex.main=1.5)
#dev.off()

日本、韓国、台湾、シンガポール、香港のReported Confirmed(報告された感染者)を計算、プロット

# read.csvの際には、check.names=Fをつける
url<- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
Confirmed<- read.csv(url,check.names=F)
# Country/Regionごとに集計
#Confirmed
Ctl<- aggregate(Confirmed[,5:ncol(Confirmed)], sum, by=list(Confirmed$"Country/Region"))
rownames(Ctl)<-Ctl[,1]
Ctl<- Ctl[,-1]
#Japan,South Korea,Taiwan,Singapore
datC<-Ctl[grep("(Japan|Korea, South|Taiwan*|Singapore)",rownames(Ctl)),] 
#Hong Kong
HK<- Confirmed[Confirmed$"Province/State"=="Hong Kong",5:ncol(Confirmed)]
rownames(HK)<- "Hong Kong"
datC<- rbind(datC,HK)
datC[datC<0]<- NA
#png("pcr11.png",width=800,height=600)
par(mar=c(4,5,4,10),family="serif")
matplot(t(datC),type="l",lty=1,lwd=3,xaxt="n",yaxt="n",bty="n",ylab="",xaxs="i")
box(bty="l",lwd=2)
#y軸ラベル
axis(side=2, at=axTicks(2), labels=formatC(axTicks(2), format="d", big.mark=','),las=1) 
#表示するx軸ラベルを指定
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,sub("/..$","",colnames(datC)))
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=346,side=1,line=2.5,cex=1.2) 
text(x=par("usr")[2],y=apply(datC,1,max,na.rm=T),labels=paste(rownames(datC),":",formatC(apply(datC,1,max,na.rm=T), format="d", big.mark=',')),pos=4,xpd=T)
title("Reported Confirmed : Japan , South Korea , Taiwan , Singapore , Hong Kong")
#dev.off()

日本、韓国、台湾、シンガポール、香港の致死率を計算、プロット

url<- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
Deaths<- read.csv(url,check.names=F)
#Deaths
Dtl<- aggregate(Deaths[,5:ncol(Deaths)], sum, by=list(Deaths$"Country/Region"))
rownames(Dtl)<-Dtl[,1]
Dtl<- Dtl[,-1]
#
datD<-Dtl[grep("(Japan|Korea, South|Taiwan*|Singapore)",rownames(Dtl)),] 
#Hong Kong
HK<- Deaths[Deaths$"Province/State"=="Hong Kong",5:ncol(Deaths)]
rownames(HK)<- "Hong Kong"
datD<- rbind(datD,HK)
datD[datD<0]<- NA
datD<- datD[order(apply(datD,1,max,na.rm=T),decreasing=T),]
# 亡くなった人の数
knitr::kable(apply(datD,1,max,na.rm=T))
#
# 致死率(%)計算
#DpC<- matrix(NA,nrow=nrow(datD),ncol=ncol(datD))
DpC<- NULL
for (i in rownames(datD)){
    temp<- round(datD[rownames(datD)== i,] / datC[rownames(datC)== i,]*100,2)
    DpC<- rbind(DpC,temp)
}
#
DpC<- DpC[order(DpC[,ncol(DpC)],decreasing=T),]
n<-nrow(DpC)
col<- rainbow(n)
#pch<-rep(c(0,1,2,4,5,6,15,16,17,18),3)
#png("Coronavirus01_1_2.png",width=800,height=600)
par(mar=c(4,5,4,10),family="serif")
#40日めから
matplot(t(DpC)[40:ncol(DpC),],type="l",lty=1,lwd=3,las=1,col=col,ylab="Reported Deaths/Reported Confirmed(%)",xaxt="n",bty="n")
box(bty="l",lwd=2)
#axis(1,at=1:nrow(t(DpC)[40:ncol(DpC),]),labels=sub("/20","",rownames(t(DpC)[40:ncol(DpC),])))
#表示するx軸ラベルを指定
labelpos<- paste0(1:12,"/",1)
for (i in labelpos){
    at<- match(i,sub("/..$","",rownames(t(DpC)[40:ncol(DpC),])))
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=307,side=1,line=2.5,cex=1.2) 
legend(x=par("usr")[2],y=par("usr")[4],legend=rownames(DpC),lty=1,lwd=3,col=col,bty="n",title="Country/Region",xpd=T)
title("Reported Deaths / Reported Confirmed (%) ")
#dev.off()

日本、韓国、台湾、シンガポール、香港の面積、人口、人口密度

library(DataComputing)
library(knitr)
data("CountryData")
adata<- CountryData[grep("(Japan|Korea, South|Taiwan|Singapore|Hong Kong)",CountryData$country),1:3]
# 人口密度計算
adata$"Population density"<- round(adata$pop/adata$area,0)
#人口で並べ替える(降順)
adata<- adata[order(adata$pop,decreasing=T),]
kable(data.frame(lapply(adata,function(x)formatC(x, format="f", big.mark=",",digits=0))),
    row.names=F,align=c("c",rep("r",3)))

library(knitr)
x<- as.data.frame(apply(datC,1,max,na.rm=T))
colnames(x)<- "Confirmed"
y<- as.data.frame(apply(datD,1,max,na.rm=T))
colnames(y)<- "Deaths"
x<- merge(x,y,by =0)
rownames(x)<- x[,1]
x<- x[,-1]
x$"Deaths/Confirmed (%)"<- round(x[,2]/x[,1]*100,2)
rownames(x)<- sub("\\*","",rownames(x))
x<- merge(x,adata[,c(1,3)],by.x=0,by.y="country")
x$"Deaths/millionpeople"<- round(x$Deaths/x$pop*1000000,2)
x[,1]<-factor(x[,1],levels=c("Japan","Korea, South","Taiwan" ,"Hong Kong", "Singapore"))
x<- x[order(x[,1],decreasing=F),]
kable(data.frame(Confirmed=formatC(x[,2], format="f", big.mark=",",digits=0),x[,c(3:4,6)],check.names=F,row.names=x[,1]),
    row.names=T,align=rep("r",3))

日本、韓国、台湾、シンガポール、香港の100万人あたりの新型コロナウイルスによる死者数

# Deaths/millionpeopleで並べ替え
dat<- x[order(x[,"Deaths/millionpeople"]),]
#png("DperMil01.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Deaths/millionpeople"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Deaths/millionpeople"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Deaths/millionpeople"],y=b,labels= dat[,"Deaths/millionpeople"],pos=4)
title("日本、韓国、台湾、シンガポール、香港の人口100万人あたりの新型コロナウイルスによる死者数")
#dev.off()

新型コロナウイルスによる死者数 in アジア

アジアの国のデータ:How many Countries in Asia?:https://www.worldometers.info/geography/how-many-countries-in-asia/ に台湾のデータを加えた。
(注意)日本語名は手打ちしているのでもしかしたら間違いがあるかもしれません。

text<- "country,Population(2020),Subregion,Jname
Turkey,84339067,Western Asia,トルコ
Iraq,40222493,Western Asia,イラク
Saudi Arabia,34813871,Western Asia,サウジアラビア
Yemen,29825964,Western Asia,イエメン
Syria,17500658,Western Asia,シリア
Jordan,10203134,Western Asia,ヨルダン
Azerbaijan,10139177,Western Asia,アゼルバイジャン
United Arab Emirates,9890402,Western Asia,アラブ首長国連邦
Israel,8655535,Western Asia,イスラエル
Lebanon,6825445,Western Asia,レバノン
Oman,5106626,Western Asia,オマーン
State of Palestine,5101414,Western Asia,パレスチナ
Kuwait,4270571,Western Asia,クエート
Georgia,3989167,Western Asia,グルジア
Armenia,2963243,Western Asia,アルメニア
Qatar,2881053,Western Asia,カタール
Bahrain,1701575,Western Asia,バーレーン
Cyprus,1207359,Western Asia,キプロス
India,1380004385,Southern Asia,インド
Pakistan,220892340,Southern Asia,パキスタン
Bangladesh,164689383,Southern Asia,バングラデシュ
Iran,83992949,Southern Asia,イラン
Afghanistan,38928346,Southern Asia,アフガニスタン
Nepal,29136808,Southern Asia,ネパール
Sri Lanka,21413249,Southern Asia,スリランカ
Bhutan,771608,Southern Asia,ブータン
Maldives,540544,Southern Asia,モルジブ
Indonesia,273523615,South-Eastern Asia,インドネシア
Philippines,109581078,South-Eastern Asia,フィリピン
Vietnam,97338579,South-Eastern Asia,ベトナム
Thailand,69799978,South-Eastern Asia,タイ
Myanmar,54409800,South-Eastern Asia,ミャンマー
Malaysia,32365999,South-Eastern Asia,マレーシア
Cambodia,16718965,South-Eastern Asia,カンボジア
Laos,7275560,South-Eastern Asia,ラオス
Singapore,5850342,South-Eastern Asia,シンガポール
Timor-Leste,1318445,South-Eastern Asia,東ティモール
Brunei,437479,South-Eastern Asia,ブルネイ
China,1439323776,Eastern Asia,中国
Japan,126476461,Eastern Asia,日本
South Korea,51269185,Eastern Asia,韓国
North Korea,25778816,Eastern Asia,北朝鮮
Mongolia,3278290,Eastern Asia,モンゴル
Uzbekistan,33469203,Central Asia,ウズベキスタン
Kazakhstan,18776707,Central Asia,カザクスタン
Tajikistan,9537645,Central Asia,タジキスタン
Kyrgyzstan,6524195,Central Asia,キルギスタン
Turkmenistan,6031200,Central Asia,トルクメニスタン
Taiwan,23816775,Eastern Asia,台湾"
#
asia<- read.csv(text=text,check.names=F)
# 米ジョンズ・ホプキンス大学のデータは、「Taiwan*」 , 「Korea, South」となっているので上のデータと一致させる。
rownames(Dtl)<- gsub("\\*","",rownames(Dtl))
rownames(Dtl)<- gsub("Korea, South","South Korea",rownames(Dtl))
asia$country[!is.element(asia$country,rownames(Dtl))]
#[1] State of Palestine Myanmar            North Korea        Turkmenistan
#
dat<-Dtl[is.element(rownames(Dtl),asia$country),] 
#
# 一番新しい日のデータのみ取り出す。
df<- dat[,ncol(dat),drop=F]
names(df)<- "Deaths"
nrow(df)
df<- merge(df,asia,by.x=0,by.y="country")
nrow(df)
df<- df[order(df$Deaths,decreasing=F),]
knitr::kable(df,row.names=F)
df$Subregion<- factor(df$Subregion,
    levels=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"))

新型コロナウイルスによる死者数 in アジア

# 東アジアの国の名前を赤
col<- ifelse(is.element(df$Subregion,c("Eastern Asia")),"red","black")
#png("CdeathsA01.png",width=800,height=800)
par(mar=c(3,8,4,2),family="serif")
b<- barplot(df$Deaths,las=1,col=as.numeric(unclass(df$Subregion)),horiz=T,xlim=c(0,max(df$Deaths,na.rm=T)*1.2),xaxt="n")
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
axis(2, at = b,labels=NA,tck= -0.008)
text(x=par("usr")[1],y=b, labels = df$Jname, col=col,pos=2,xpd=T,font=3)
title("新型コロナウイルスによる死者数 in アジア\n(データのない国 : Myanmar,North Korea,Turkmenistan,State of Palestine)",
    cex.main=1.5)
legend("bottomright",inset=c(0.15,0.05),legend=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"),
    pch=15,col=1:5,cex=1.5,bty="n",title="Subregion")
#dev.off()

新型コロナウイルスによる人口100万人あたりの死者数 in アジア

df$DpP <- round(1000000*df$Deaths/df$"Population(2020)",2)
df<- df[order(df$DpP,decreasing=F),]
#
# 東アジアの国の名前を赤
col<- ifelse(is.element(df$Subregion,c("Eastern Asia")),"red","black")
#png("CdeathsA02.png",width=800,height=800)
par(mar=c(3,8,4,2),family="serif")
b<- barplot(df$DpP,las=1,col=as.numeric(unclass(df$Subregion)),horiz=T,xlim=c(0,max(df$DpP,na.rm=T)*1.2))
axis(2, at = b,labels=NA,tck= -0.008)
text(x=par("usr")[1],y=b,labels = df$Jname,col=col,pos=2,xpd=T,font=3)
title("新型コロナウイルスによる人口100万人あたりの死者数 in アジア\n(データのない国 : Myanmar,North Korea,Turkmenistan,State of Palestine)",
    cex.main=1.5)
legend("bottomright",inset=c(0.15,0.05),legend=c("Central Asia","Eastern Asia","South-Eastern Asia","Southern Asia","Western Asia"),
    pch=15,col=1:5,cex=1.5,bty="n",title="Subregion")
#dev.off()

「人口あたりの死者数」で評価した「日本モデル」より優秀なモデル in アジア

jdeath<- df$DpP[is.element(df$Row.names,c("Japan"))]
paste(df$Jname[df$DpP< jdeath],"モデル")

日本、韓国、台湾、シンガポール、香港のTotal Tests for COVID-19

library("rvest")
# "COVID-19 testing"のデータ取得
html <- read_html("https://en.wikipedia.org/wiki/COVID-19_testing")
tbl<- html_table(html,fill = T)
# "covid19-testing"のtableが何番目か見つける
nodes<- html_nodes(html, "table")
class<-html_attr(nodes,"class")
#num<-grep("plainrowheaders",class)
num<- 3
#
Wtest<- tbl[[num]][,c(1:3,5:8)]
str(Wtest)
#
for (i in c(3:7)){
    Wtest[,i]<- as.numeric(gsub(",","",Wtest[,i]))
}
str(Wtest)
save("Wtest",file="Wtest.Rdata")
#load("Wtest.Rdata")
#asia5<- Wtest[grep("(Japan|South Korea|Singapore|Taiwan|Hong Kong)",Wtest[,1]),]

(注)国と地域の表が別になっていた

num<- 5
#
Wtest2<- tbl[[num]][,1:8]
str(Wtest2)
#
for (i in 4:8){
    Wtest2[,i]<- as.numeric(gsub(",","",Wtest2[,i]))
}
str(Wtest2)
save("Wtest2",file="Wtest2.Rdata")
(asia5<- Wtest[grep("(Japan|South Korea|Singapore|Taiwan)",Wtest[,1]),])
colnames(asia5)[1]<- "Country or Subdivision"
colnames(asia5)[2]<- "Date"
colnames(asia5)[3]<- "Tested"
colnames(asia5)[6]<- "Tested /millionpeople"
(asia5 <- asia5[!is.na(asia5[,4]),])
temp<- Wtest2[grep("(Taiwan|Hong Kong)",Wtest2[,"Subdivision"]),]
(temp <- temp[!is.na(temp[,4]),2:8])
colnames(temp)[1]<- "Country or Subdivision"
colnames(temp)[2]<- "Date"
( asia5<- rbind(asia5,temp) )

日本、韓国、台湾、シンガポール、香港のTotal Tests for COVID-19

# Testedで並べ替え
dat<- asia5[order(asia5[,"Tested"]),]
#png("pcr09.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Tested"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Tested"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Tested"],y=b,labels= paste("As of",dat[,"Date"],"\n",formatC(dat[,"Tested"],format="d",big.mark=',')),pos=4)
title("Total Tests for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
    "Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()

日本、韓国、台湾、シンガポール、香港の検査陽性率(%) Positive/Tests*100

# %で並べ替え
dat<- asia5[order(asia5[,"%"]),]
#png("pcr12.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"%"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"%"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=axTicks(1))
text(x=dat[,"%"],y=b,labels= paste("As of",dat[,"Date"],"\n",dat[,"%"],"%"),pos=4)
title("Positive/Tests*100 for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
    "Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()

日本、韓国、台湾、シンガポール、香港のTests /million people for COVID-19

# 人口100万人あたり
# Tested /millionpeopleで並べ替え
dat<- asia5[order(asia5[,"Tested /millionpeople"]),]
#png("pcr10.png",width=800,height=600)
par(mar=c(7,7,3,2),family="serif")
b<- barplot(dat[,"Tested /millionpeople"],horiz=T,col="pink",xaxt="n",names=dat[,1],xlim=c(0,max(dat[,"Tested /millionpeople"])*1.2),las=1)
axis(side=1, at=axTicks(1), labels=formatC(axTicks(1), format="d", big.mark=','))
text(x=dat[,"Tested /millionpeople"],y=b,labels= paste("As of",dat[,"Date"],"\n",formatC(dat[,"Tested /millionpeople"],format="d",big.mark=',')),pos=4)
title("Tested /million people for COVID-19(Japan,South Korea,Singapore,Taiwan,Hong Kong)",
    "Data : [Wikipedia:COVID-19 testing](https://en.wikipedia.org/wiki/COVID-19_testing)")
#dev.off()

主な地点の平年値(日本、韓国、オーストラリア)

ダーウィン<- c(28.2,28.0,28.1,28.2,27.0,25.1,24.7,25.6,27.7,29.0,29.2,28.8)
ブリズベン<- c(25,24.8,23.5,21.1,18.3,15.6,14.8,15.6,18.2,20.5,22.3,24)
パース<- c(24.4,24.6,22.8,19.5,16.3,13.7,12.7,13.2,14.6,16.6,19.7,22.1)
キャンベラ<- c(20.8,20.2,17.8,13.5,9.8,6.7,5.8,7.2,10.2,13,16.2,18.8)
ソウル<- c(-2.4,0.6,5.7,12.5,17.8,22.2,24.9,25.7,21.2,14.8,7.2,0.4)
プサン<- c(3.2,4.9,8.7,13.6,17.5,20.7,24.1,26,22.3,17.6,11.6,5.8)
札幌<- c(-3.6,-3.1,0.6,7.1,12.4,16.7,20.5,22.3,18.1,11.8,4.9,-0.9)
東京<- c(5.2,5.7,8.7,13.9,18.2,21.4,25,26.4,22.8,17.5,12.1,7.6)
大阪<- c(6.0,6.3,9.4,15.1,19.7,23.5,27.4,28.8,25.0,19.0,13.6,8.6)
那覇<- c(17,17.1,18.9,21.4,24,26.8,28.9,28.7,27.6,25.2,22.1,18.7)
#
temp<- data.frame(ダーウィン,ブリズベン,パース,キャンベラ,ソウル,プサン,札幌,東京,大阪,那覇)
rownames(temp)<- c("1月","2月","3月","4月","5月","6月","7月","8月","9月","10月","11月","12月")
#
col=c("chartreuse","chartreuse2","chartreuse3","darkgreen","blue","blue4","deeppink","red","red1","red2")
#png("Ctemp01.png",width=800,height=600)
par(mar=c(6,4,4,8),family="serif")
matplot(temp,type="o",pch=c(0,1,2,4,5,6,15,16,17,18),lty=c(3,3,3,3,2,2,1,1,1,1),col=col,lwd=2,xaxt="n",bty="n",las=1,ylab="気温(℃)")
box(bty="l",lwd=3)
axis(1,at=1:12,labels=rownames(temp))
text(x=par("usr")[2],y=temp[nrow(temp),],labels=colnames(temp),xpd=T,pos=c(rep(3,4),rep(1,6)),col=col)
abline(h=10,lty=4,lwd=0.5)
title("主な地点の平年値(日本、韓国、オーストラリア)",
    "[Data:http://www.data.jma.go.jp/gmd/cpd/monitor/mainstn/nrmlist.php#reg0 & https://www.stat.go.jp/data/nihon/01.html]")
#dev.off()

致死率 7日移動平均(Hokkaido|Tokyo|Kanagawa|Aichi|Osaka)

library(xts)
library(TTR)
#[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv)
nhkC<- read.csv("https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv")
save(nhkC,file="nhkC.Rdata")
#load("nhkC.Rdata")
code<- c(1, 11, 13, 14, 23, 27)
#感染者数
# 
Cdata<- nhkC[nhkC$都道府県コード==code[1],c(1,4)]
Cdata.xts<- as.xts(read.zoo(Cdata, format="%Y/%m/%d"))
# 
for (i in code[-1]){
    Cdata<- nhkC[nhkC$都道府県コード== i,c(1,4)]
    tmp.xts<- as.xts(read.zoo(Cdata, format="%Y/%m/%d"))
    Cdata.xts<- merge(Cdata.xts,tmp.xts)
}
# NA<- 0
coredata(Cdata.xts)[is.na(Cdata.xts)]<- 0
colnames(Cdata.xts)<- paste0(unique(nhkC[nhkC$都道府県コード==code,"都道府県名"]),"C")
#
#死者数
# 
Ddata<- nhkC[nhkC$都道府県コード==code[1],c(1,6)]
Ddata.xts<- as.xts(read.zoo(Ddata, format="%Y/%m/%d"))
# 
for (i in code[-1]){
    Ddata<- nhkC[nhkC$都道府県コード== i,c(1,6)]
    tmp.xts<- as.xts(read.zoo(Ddata, format="%Y/%m/%d"))
    Ddata.xts<- merge(Ddata.xts,tmp.xts)
}
# NA<- 0
coredata(Ddata.xts)[is.na(Ddata.xts)]<- 0
colnames(Ddata.xts)<- paste0(unique(nhkC[nhkC$都道府県コード==code,"都道府県名"]),"D")
data.xts<- merge(Cdata.xts,Ddata.xts)
# 致死率(%)7日移動平均
#死者数(7日間合計)/陽性者数(7日間合計)*100
data<-  data.frame(apply(data.xts,2,runSum,7))
rownames(data)<- as.character(index(data.xts))
data<- data[-c(1:6),]
for (i in 1:length(code)){
    data<- cbind(data, round(data[,(i+length(code))]/data[,i]*100,2))
}
data2<- data[,(length(code)*2+1):(length(code)*3)]
colnames(data2)<- unique(nhkC[nhkC$都道府県コード==code,"都道府県名"])
#2020-07-20から
data2<- data2[-c(1:180),]
#
labels<- sub("-","/",sub("-0","-",sub("^0","",sub("^....-","",rownames(data2)))))
# 毎月1日
labelpos<- paste0(1:12,"/",1)
#png("covOsaka11.png",width=800,height=600)
par(mar=c(5,4,5,9),family="serif")
matplot(data2,type="l",lty=1,col=rainbow(length(code),alpha=0.8),lwd=1.5,las=1,bty="n",xlab="",ylab="",xaxt="n",xaxs="i")
box(bty="l",lwd=2.5)
for (i in labelpos){
    at<- match(i,labels)
    if (!is.na(at)){ axis(1,at=at,labels = paste0(sub("/1","",i),"月"),tck= -0.02)}
    }
text(x=par("usr")[1],y=par("usr")[4],labels="(%)",pos=2,xpd=T)
text(x=par("usr")[2],y=tail(data2,1),labels=paste0(colnames(data2),":",tail(data2,1),"%"),xpd=T,pos=4)
mtext(text="2020年",at=1,side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=166,side=1,line=2.5,cex=1.2) 
legend("topleft",inset=c(0.03,0.03),legend=colnames(data2),lty=1,col=rainbow(length(code),alpha=0.8),lwd=1.5,xpd=T)
title("致死率(%)7日移動平均","[データ:NHK https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv]")
#dev.off()

北海道、埼玉、東京、神奈川、愛知、大阪の月別死者数と月別人口100万人あたりの死者数(データ:NHK)

#都道府県別人口はNipponMapパッケージのデータを使う
shp <- system.file("shapes/jpn.shp", package = "NipponMap")[1]
m <- sf::read_sf(shp)
#
data.xts<- Ddata.xts
colnames(data.xts)<- unique(nhkC[nhkC$都道府県コード==code,"都道府県名"])
#
monthsum<- NULL
for (i in 1:ncol(data.xts)){
#各月ごとの死亡者の合計
m.xts<- apply.monthly(data.xts[,i],sum)
monthsum<- cbind(monthsum,m.xts)
}
#
monthsum<- data.frame(monthsum)
#if (rownames(monthsum)[nrow(monthsum)]!="11"){
#    monthsum= rbind(monthsum,0)
#}
# 最初の月の死亡者がすべての県で0なら削除
if ( all(monthsum[1,]==0) ) {monthsum<- monthsum[-1,]}
#plot
png("covOsaka12.png",width=800,height=600)
par(mar=c(4,5,3,2),family="serif",mfrow=c(2,1))
b<- barplot(t(monthsum),beside=T,names=paste0(sub("^0","",substring(rownames(monthsum),6,7)),"月"),las=1,col=rainbow(ncol(monthsum)),ylim=c(0,max(monthsum)*1.2),
    legend=T,args.legend=list(x="topleft",inset=0.02))
box(bty="l",lwd=2.5)
abline(h=c(100,200),lwd=0.8,lty=2,col="gray")
#for (i in 1:ncol(monthsum)){
#    text(x=b[i,],y=monthsum[,i],labels=monthsum[,i],pos=3)
#}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[12],side=1,line=2.5,cex=1.2) 
title("新型コロナウイルス月別死亡者")
title("\n\n\nデータ:[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv)",cex.main=0.8)
#
CperPop<- monthsum
for (i in 1:ncol(CperPop)){
    CperPop[,i]<- round(CperPop[,i]/m$population[code[i]]*10^6,0)
}
#
#plot
b<- barplot(t(CperPop),beside=T,names=paste0(sub("^0","",substring(rownames(CperPop),6,7)),"月"),las=1,col=rainbow(ncol(CperPop)),ylim=c(0,max(CperPop)*1.2),
    legend=T,args.legend=list(x="topleft",inset=0.02))
box(bty="l",lwd=2.5)
abline(h=c(10,20,30,40,50),lwd=0.8,lty=2,col="gray")
#for (i in 1:ncol(CperPop)){
#    text(x=b[i,],y=CperPop[,i],labels=CperPop[,i],pos=3)
#}
mtext(text="2020年",at=b[1],side=1,line=2.5,cex=1.2) 
mtext(text="2021年",at=b[12],side=1,line=2.5,cex=1.2) 
title("人口100万人あたり新型コロナウイルス月別死亡者")
title("\n\n\nデータ:[NHK](https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv) & 都道府県別人口:NipponMapパッケージ",cex.main=0.8)
#
#dev.off()

(注意)
新型コロナウイルス感染症の現在の状況と厚生労働省の対応について(令和2年4月21日版)

  • 都道府県から公表された死亡者数の合計は244(+21)名であるが、うち58名については個々の陽性者との突合作業中のため、計上するに至っていない。

新型コロナウイルス感染症の現在の状況と厚生労働省の対応について(令和2年4月22日版)

  • 退院した者のうち616名、死亡者のうち74名については、個々の陽性者との突合作業中。従って、入退院等の状況の合計とPCR検査陽性者数は一致しない。

※3:日本のPCR検査実施人数は、一部自治体について件数を計上しているため、実際の人数より過大である。
また、人数を公表していない自治体の数は計上しておらず、更新がなかった自治体については、前日の数値を使用している。

また、ありえないデータ(日本)

  • PCR検査実施人数 国内事例 5月12日: 188646 5月13日: 188031 5月12日の方が多い!
  • PCR検査実施人数 国内事例 5月14日: 196816 5月15日: 194323 5月14日の方が多い!

※6:東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。

日本と韓国の新型コロナウイルスに関する情報開示の比較 「データの信頼性」という観点からみてみると、(参考)COVID-19

(注)日本の6月18日の検査人数の上昇は以下の理由によるものです。

  • 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。

<span style="color: red; ">6月9日に韓国のPCR検査の結果判明数が100万人超えました。</span>
(注意)日本のPCR検査実施人数は、一部自治体について件数を計上しているため、実際の人数より過大である。

日本の新型コロナウイルスによる死亡者数はもっと多いのではないか。
根拠:日本法医病理学会HP:法医解剖、検案からの検体に対する新型コロナウイルス検査状況(pdf) 2020/04/26
法医解剖、検案からの検体に対する新型コロナウイルス検査状況
○ 回答機関: 26 機関

  • 実施件数
    • 保健所: 9 件 他の検査機関: 2 件
  • 拒否件数
    • 保健所: 12 件 他の検査機関: 0 件

(注)日本の6月18日のPCR検査の陽性率の下降は以下の理由によるものです。

  • 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。

(注)日本の6月18日の検査人数の上昇は以下の理由によるものです。

  • 東京都は、医療機関による保険適用での検査人数等を除いた検査人数をウェブサイトに掲載していたが、 6月15日以降、医療機関等が行った検査を含む検査人数を過去に遡って計上しているため、検査実施人数が大幅に(4万人以上)増加している。

データの信頼性(データのとり方の一貫性)が劣るのでおかしな箇所(検査人数より陽性者が多い!)が見受けられます。