基于百度指数的京津冀地区COVID-19高峰预测与空间分布特征

夏学仓; 刘瑜; 王小琬; 邱文松; 房少洁; 刘超

doi:10.16462/j.cnki.zhjbkz.2023.09.013

基于百度指数的京津冀地区COVID-19高峰预测与空间分布特征

doi: 10.16462/j.cnki.zhjbkz.2023.09.013

河北大学经济学院统计学系，保定 071002

基金项目:

河北省社会科学基金 HB22TJ001

详细信息

通讯作者:
刘超，E-mail: liuchao003003@126.com

中图分类号: R181.2
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出版历程
- 收稿日期: 2022-12-29
- 修回日期: 2023-05-18
- 网络出版日期: 2023-10-12
- 刊出日期: 2023-09-10

Peak prediction and spatial distribution characteristics of COVID-19 in Beijing-Tianjin-Hebei region based on baidu index

School of Economics, Department of Statistics, Hebei University, Baoding 071002, China

Funds:

Hebei Social Science Foundation Project HB22TJ001

More Information

Corresponding author: LIU Chao, E-mail: liuchao003003@126.com

摘要

摘要: 目的分析2022年11月11日―12月22日京津冀地区各市COVID-19的进展周期和空间聚集情况。方法基于各市每日“发烧”关键词百度指数搜索数据，使用logistic回归分析模型模拟并预测此轮COVID-19发展进程，对感染进展周期划分为渐增期、快增期及缓增期，预测感染高峰日，同时对“发烧”百度指数搜索率进行全局和局部空间自相关分析。结果从logistic回归分析模型模拟及预测结果来看，模型拟合效果较好，各市COVID-19流行进展速度及流行阶段各不相同，石家庄市、保定市及邢台市最早进入感染快增期阶段；空间自相关分析显示仅有5 d京津冀地区存在全局空间正相关性(Moran′s I: 0.314~0.491, 均P < 0.05)，其他时间均呈随机分布。结论京津冀地区此轮COVID-19流行均呈暴发趋势，各市疫情进展阶段及感染高峰有较大差异，且大部分时间不存在显著的空间自相关，为卫生医疗配置提供参考依据。
- 新型冠状病毒感染 /
- 百度指数 /
- 时空分析
Abstract: Objective To analyze the progression cycle and spatial clustering of the coronavirus disease 2019(COVID-19)in Beijing-Tianjin-Hebei region from November 11 to December 22, 2022. Methods Based on the daily fever keyword Baidu index search data in each city, the logistic model was used to simulate and predict the development process of this round of COVID-19 infection. The infection progression cycle was divided into an increasing period, a rapid increase period and a slow increase period, and the peak day of infection growth was predicted. At the same time, the global and local spatial autocorrelation analysis of the fever Baidu index search rate was conducted. Results The logistic model simulation and prediction yielded a robust model fitting effect. The epidemic progression speed and stage of COVID-19 infection varied across cities. Shijiazhuang City, Baoding City and Xingtai City were the first to enter the rapid increase stage of infection. Spatial autocorrelation analysis showed that there was a significant global spatial positive correlation in Beijing-Tianjin-Hebei region only for 5 days (Moran′s I: 0.314~0.491, P value was < 0.05), whereas other times exhibited random distribution. Conclusions This round of COVID-19 infection epidemic in Beijing-Tianjin-Hebei region shows an outbreak trend. The epidemic progress stage and infection peak in each city are significantly different, and most of the time there is no significant spatial autocorrelation, which provides a reference for health care configuration.
- COVID-19 /
- Baidu index /
- Temporal-spatial analyze

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图 1 京津冀各市COVID-19进展的logistic回归分析模型拟合及预测

1. A: 北京市; 2. B: 天津市; 3. C: 石家庄市; 4. D: 衡水市; 5. E: 张家口市; 6. F: 承德市; 7. G: 秦皇岛市; 8. H: 廊坊市; 9. I: 沧州市; 10. J: 保定市; 11. K: 唐山市; 12. L: 邯郸市; 13. M: 邢台市。

Figure 1. Logistic regression analysis model fitting and prediction of COVID-19 progress in various cities of Beijing-Tianjin-Hebei

1. A: Beijing City; 2. B: Tianjin City; 3. C: Shijiazhuang City; 4. D: Hengshui City; 5. E: Zhangjiakou City; 6. F: Chengde City; 7. G: Qinhuangdao City 8. H: Langfang City; 9. I: Cangzhou City; 10. J: Baoding City; 11. K: Tangshan City; 12. L: Handan City; 13. M: Xingtai City.

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图 2 京津冀“发烧”百度指数搜索率空间自相关局部Moran散点图

1. A：2022年11月18日；2. B：2022年11月22日；3. C：2022年11月24日；4. D：2022年12月6日；5. E：2022年12月7日。

Figure 2. Spatial autocorrelation local Moran scatter plot of Baidu index search rate for "fever" in Beijing-Tianjin-Hebei region

1. A: November 18, 2022; 2. B: November 22, 2022; 3. C: November 24, 2022; 4. D: December 6, 2022; 5. E: December 7, 2022.

下载: 全尺寸图片幻灯片

表 1 京津冀各市COVID-19进展阶段及感染增长高峰日预测结果

Table 1. Forecast results of COVID-19 infection progression stage and infection growth peak day in Beijing-Tianjin-Hebei region

地区 Area	k值 value	N值 value	c值 value	渐增期结束日期(月/日) Incremental period end date (Month/Day)	快增期(月/日) Rapid growth period (Month/Day)	缓增期开始日期(月/日) Start date of slow growth period (Month/day)	增长高峰日(月/日) Growth peak day (Month/Day)	R²值 value	RMSE值 value
北京市 Beijing City	0.331	36 947	-10.866	12/9	12/10―12/17	12/18	12/13	0.999	157.154
天津市 Tianjin City	0.281	14 577	-10.100	12/11	12/12―12/20	12/21	12/16	0.994	281.470
石家庄市 Shijiazhuang City	0.165	18 755	-5.284	12/4	12/5―12/20	12/21	12/12	0.999	140.348
衡水市 Hengshui City	0.240	4 870	-8.470	12/10	12/11―12/21	12/22	12/16	0.991	112.352
张家口市 Zhangjiakou City	0.232	4 028	-8.175	12/10	12/11―12/21	12/22	12/16	0.997	48.295
承德市 Chengde City	0.360	2 708	-13.477	12/14	12/15―12/21	12/22	12/18	0.996	39.209
秦皇岛市 Qinhuangdao City	0.295	3 576	-10.494	12/11	12/12―12/20	12/21	12/16	0.996	57.624
廊坊市 Langfang City	0.262	7 179	-8.563	12/8	12/9―12/18	12/19	12/13	0.998	90.128
沧州市 Cangzhou City	0.250	6 824	-9.102	12/11	12/12―12/22	12/23	12/17	0.994	122.054
保定市 Baoding City	0.220	12 915	-6.273	12/3	12/4―12/15	12/16	12/9	0.998	141.046
唐山市 Tangshan City	0.322	6 837	-10.733	12/9	12/10―12/17	12/18	12/14	0.999	37.088
邯郸市 Handan City	0.242	9 908	-7.851	12/7	12/8―12/18	12/19	12/13	0.999	74.091
邢台市 Xingtai City	0.151	8 923	-4.396	11/30	12/1―12/18	12/19	12/10	0.998	105.894
注：RMSE, 均方误差。 Note: RMSE, root mean square error.

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表 2 京津冀地区“发烧”关键词百度指数搜索率显著全局空间自相关结果

Table 2. Significant global spatial autocorrelation results of Baidu index search rate for the keyword "fever" in the Beijing-Tianjin-Hebei region

时间(月/日) Time (Month/Day)	Moran′s I值 value	Z值 value	P值 value
11/18	0.385	2.876	0.004
11/22	0.491	3.530	0
11/24	0.356	2.372	0.017
12/6	0.339	2.246	0.024
12/7	0.314	2.085	0.037

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表 3 京津冀地区“发烧”关键词百度指数搜索率局部空间自相关结果

Table 3. Beijing-Tianjin-Hebei region "fever" keyword Baidu index search rate local space autocorrelation results

时间(月/日) Time (Month/Day)	地区 Area	Moran′s I值 value	Z值 value	P值 value
11/18	石家庄市 Shijiazhuang City	1.312	3.040	0.002
	邢台市 Xingtai City	1.629	3.732	0
11/22	石家庄市 Shijiazhuang City	1.155	2.700	0.007
	衡水市 Hengshui City	0.891	2.531	0.011
	邢台市 Xingtai City	2.114	4.791	0
11/24	石家庄市 Shijiazhuang City	2.070	4.335	0
	邢台市 Xingtai City	1.239	2.662	0.008
12/6	石家庄市 Shijiazhuang City	1.283	2.723	0.006
	承德市 Chengde City	0.610	1.989	0.047
12/7	石家庄市 Shijiazhuang City	1.054	2.246	0.025
	承德市 Chengde City	0.621	2.012	0.044
注：此表仅列出局部空间自相关结果中显著(P＜0.05)城市的莫兰指数值。 Note: This table only lists the Moran′s I values of cities with significant (P < 0.05) local spatial autocorrelation results.

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