Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression

Table 5

Obviously overestimated or underestimated cities with relatively high WP (>0.65).

City	WP	Observed CCCs	Estimated CCCs
Xianning^1,†	9.0345	836	1,158
Jingmen^1,†	6.1535	925	1,213
Xiantao^1,†	5.4869	575	1,031
Enshi^1,†	4.0948	252	491
Qianjiang^1,†	1.9928	198	425
Nanyang^1	1.2508	156	264
Jiujiang^1	0.8113	118	212
Zhengzhou^1,∗	0.7422	157	262
Xiaogan^1,†	26.2352	3,518	1,736
Huanggang^1,†	26.0067	2,905	1,621
Jingzhou^1,†	12.2395	1,579	905
Ezhou^1,†	6.8330	1,391	1,065
Suizhou^1,†	5.7935	1,307	1,174
Yichang^1,†	5.5920	931	734
Shiyan^1,†	3.8053	672	416
Chongqing^1,∗	1.8785	576	270
Beijing^2,∗	0.8908	414	275
Shenzhen^2,∗	0.7387	417	319
Shanghai^1,∗	0.7329	337	224
Nanchang^1	0.7036	230	167
Guangzhou^2,∗	0.6845	346	281

¹Cities close to Wuhan, . ²Cities far from Wuhan, . Municipalities and capital cities. ^†Cities in Hubei province.