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)

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(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」

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)

日本と韓国のPCR検査の検査陽性率(%)「累計」

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。

日本と韓国のPCR検査の暫定致死率(%)「累計」

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)

韓国のPCR検査の結果（日別）

日本と韓国の検査陽性者数（日別）

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）

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）

日本の(報告された)感染者数 対数表示（日別）

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。

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

韓国

オーストラリア（南半球）

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）

日本、韓国、オーストラリアの検査陽性者数の推移

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

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

日本、韓国、オーストラリアの致死率(%)の７日移動平均

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。

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)

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

日本、韓国、台湾、シンガポール、香港のReported Confirmed

日本、韓国、台湾、シンガポール、香港のPCR検査の暫定致死率(%)

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。

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

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)))

表

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。