Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study

BMC Infectious Diseases

Table 1 Emerging space–time cluster of COVID-19 in mainland China from January 10 to October 5, 2020, at city-level

Cluster	Duration(days)	Center	Radius	# of cities	Observed	Expected	RR	LLR	P
1	Jan 27–Feb 29	Wuhan	102.46	6	57,505	207.52	845.01	292,260.15	< 0.01
2	Jan 25–Feb 13	Shangluo	408.41	33	5674	595.26	10.14	7869.28	< 0.01
3	Jan 25–Feb 14	Hengyang	366.96	27	3663	576.47	6.59	3743.56	< 0.01
4	Jul 17–Aug 13	Urumqi	0.00	1	811	22.08	37.08	2137.36	< 0.01
5	Jan 27–Feb 9	Quzhou	322.25	26	1632	339.54	4.88	1279.59	< 0.01
6	Apr 4–Apr 17	Mudanjiang	0.00	1	368	7.92	46.68	1053.46	< 0.01
7	Jan 26–Feb 8	Shenzhen	110.91	6	844	149.54	5.69	769.00	< 0.01
8	Feb 20	Jining	0.00	1	201	1.88	107.21	740.27	< 0.01
9	Jun 13–Jun 27	Beijing	0.00	1	304	72.65	4.20	204.09	< 0.01
10	Apr 10–Apr 12	Hulunbeir	0.00	1	62	1.71	36.29	162.36	< 0.01
11	Jan 24–Feb 8	Ziyang	266.53	16	705	351.38	2.01	138.03	< 0.01
12	Feb 3–Feb 27	Tibetan Autonomous Prefecture of Garze	0.00	1	72	6.74	10.69	105.29	< 0.01
13	Jul 24–Aug 2	Dalian	0.00	1	84	15.73	5.34	72.48	< 0.01
14	Apr 7	Taiyuan	0.00	1	25	1.00	24.92	56.39	< 0.01
15	Mar 5–Mar 6	Lanzhou	0.00	1	28	1.71	16.43	52.07	< 0.01
16	Jan 26–Feb 14	Yinchuan	87.90	2	59	16.52	3.57	32.63	< 0.01
17	Jan 28–Jan 30	Sipsongpanna	0.00	1	11	0.80	13.72	18.61	< 0.01
18	Jan 28–Feb 1	Chuxiong Yi Autonomous Prefecture	150.77	4	43	14.97	2.87	17.36	0.02
19	Jan 26–Jan 31	Weihai	0.00	1	20	3.83	5.23	16.90	0.03
20	Feb 11	Qiannan Buyi and Miao Autonomous Prefecture	0.00	1	9	0.74	12.16	14.22	0.29

ISSN: 1471-2334