2021年初河北省和黑龙江省COVID-19传播差异性的对比

焦海燕; 廖影; 王蕾

doi:10.16462/j.cnki.zhjbkz.2023.02.005

2021年初河北省和黑龙江省COVID-19传播差异性的对比

doi: 10.16462/j.cnki.zhjbkz.2023.02.005

焦海燕¹,
廖影¹,
王蕾^2, ,

1.
830011 乌鲁木齐，新疆医科大学公共卫生学院流行病与卫生统计学教研室
2.
830011 乌鲁木齐，新疆医科大学医学工程技术学院数学教研室

基金项目:

国家自然科学基金 12061079

新疆维吾尔自治区自然科学基金 2019D01C206

详细信息

通讯作者:
王蕾, E-mail: wlei81@126.com

中图分类号: R181
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-03
- 修回日期: 2022-04-06
- 网络出版日期: 2023-02-20
- 刊出日期: 2023-02-10

Comparison of the transmission of COVID-19 between Hebei and Heilongjiang in early 2021

1.
Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi 830011, China
2.
Department of Math, College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China

Funds:

National Natural Science Foundation of China 12061079

National Natural Science Foundation of Xinjiang Uygur Autonomous Region of China 2019D01C206

More Information

Corresponding author: WANG Lei, E-mail: wlei81@126.com

摘要

摘要: 目的对2021年1月2日至2021年3月3日河北省和2020年12月10日至2021年2月28日黑龙江省COVID-19传播的差异性进行对比研究，为疫情防控策略的制定提供理论支撑。方法基于2021年初河北省和黑龙江省的现存无症状病例数、现存确诊病例数与累计移出病例数，构建一类阶段性控制策略的动力学模型，利用非线性最小二乘法估计模型参数；利用参数敏感性分析探讨关键参数对两个地区现存确诊病例数峰值及峰值时间的影响。分别分析初期接触数、初期接触传播概率、潜伏者的传染力校正因子与有症状感染者构成比的强弱变化对两个地区的现存无症状病例数、现存确诊病例数与累计移出病例数的影响。结果两个地区的模型拟合效果均良好；与河北省相比，黑龙江省的无症状感染者构成比较大；当初期接触数、初期接触传播概率、潜伏者的传染力校正因子与有症状感染者构成比分别减少10%时，黑龙江省的现存无症状病例数峰值、现存确诊病例数峰值与累计移出病例数的平均减少幅度均较河北省的大。结论 2021年初河北省和黑龙江省的COVID-19疫情传播存在明显差异，尤其是控制措施的强弱变化对不同地区疫情发展的影响不同。
- 新型冠状病毒肺炎 /
- 动力学模型 /
- 阶段性控制策略 /
- 敏感性分析
Abstract: Objective To compare the diversity of transmission of COVID-19 in Hebei and Heilongjiang Province in early 2021, and to provide theoretical support for the formulation of prevention and control strategies for COVID-19. Methods A dynamical model with staged control strategies was constructed based on the number of existing asymptomatic cases, the number of existing confirmed cases and the cumulative number of removed cases in Hebei and Heilongjiang at the beginning of 2021. Parameters of the model were estimated by the nonlinear least square method. Sensitivity analysis was used to explore the impact of key parameters on the peak number and peak time of existing confirmed cases in the two regions. We respectively analyzed the influence of the change for the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection on the number of existing asymptomatic cases, the number of existing confirmed cases and the number of cumulative cases in the two regions. Results The model fitting results of the two regions were good. Compared the results of Hebei with those of Heilongjiang, there was a larger proportion of asymptomatic infected persons. When the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection separately decreased by 10%, the average decrease for the peak number of existing asymptomatic and existing confirmed cases, and the cumulative removed cases in Heilongjiang were more than those in Hebei. Conclusions In early 2021, the transmissions of COVID-19 in Hebei and Heilongjiang were significantly different. In particular, the impact of control measures on the development of the epidemic is different in different areas.
- COVID-19 /
- Dynamics model /
- Phased control strategy /
- Sensitivity analysis

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图 1 COVID-19传播流程

Figure 1. Flowchart of COVID-19 transmission

下载: 全尺寸图片幻灯片

图 2 现存无症状病例数、现存确诊病例数和累计移出病例数的模型拟合效果图

注：A1-A3:河北省；B1-B3：黑龙江省。

Figure 2. Model fitting of the number of existing asymptomatic cases, the number of existing confirmed cases and the cumulative number of removed cases

下载: 全尺寸图片幻灯片

图 3 现存确诊病例数峰值(H_max)及峰值时间(T_{H_max})关于十个参数的PRCCs结果

注：A: 河北省，B：黑龙江省。

Figure 3. Partial rank correlation coefficients (PRCCs) results for the peak number (H_max) and peak time (T_{H_max}) of existing confirmed cases with respect to ten paraments

下载: 全尺寸图片幻灯片

图 4 参数变化对两个地区疫情发展影响的对比效果图

注：A：c₀减少10%，B：β₀减少10%，C：ε减少10%，D：η减少10%E：c₀增加10%，F：β₀增加10%，G：ε增加10%，H：η增加10%。

Figure 4. Comparative effect of parameters change on epidemic development in two regions

下载: 全尺寸图片幻灯片

表 1 模型(1)的参数含义及估计值

Table 1. Interpretations and estimated values of the parameters for model (1)

参数	含义	地区		来源
参数	含义	河北省	黑龙江省	来源
R(0)	初始R人数	0	0	文献^[19-20]
H(0)	初始H人数	1	2	文献^[19-20]
Sq(0)	初始Sq(无被感染风险)	0	0	文献^[19-20]
Eq(0)	初始Eq(0)	0	0	文献^[19-20]
E(0)	初始E人数	1	0	参数估计
A(0)	初始A人数	0	0	参数估计
I(0)	初始I人数	0	0	参数估计
S(0)	初始S人数	4 500	4 510	参数估计
q	接触者隔离率	0.400	0.545	参数估计
c₀	初期接触数	14	13	参数估计
c_b	最小接触数	1	1	参数估计
r_I	接触传播数的指数衰减率	0.420	0.475	参数估计
β₀	初期接触传播概率	0.600	0.524	参数估计
β_b	最小接触传播概率	0.001	0.001	参数估计
r₂	接触传播概率的指数衰减率	0.420	0.470	参数估计
ε	E传染力矫正因子	0.392	0.390	参数估计
θ	A传染力矫正因子	0.500	0.501	参数估计
λ	解除隔离率	1/14	1/14	文献^[16]
η	I的构成比	0.410	0.075	参数估计
γ_A	A移出速率	0.020	0.025	参数估计
γ_H	H移出速率	0.041	0.053	参数估计
δ_A	从A到H的确诊速率	0.170	0.010	参数估计
δ_I	从I到H的确诊速率	0.950	0.990	参数估计
δ_{E_q}	从E_q到H的确诊速率	0.079	0.070	参数估计
σ	E进展为感染者的速率(潜伏期的倒数)	1/7	1/7	文献^[16]
d	因病死亡率	0.042	0.042	参数估计

下载: 导出CSV

表 2 模型(1)拟合效果的评价

Table 2. Evaluation of the fitting effect for the model (1)

数据	指标
	R²		CRM
	河北省	黑龙江省	河北省	黑龙江省
现存无症状病例数	0.947	0.872	0.102	-0.117
现存确诊病例数	0.844	0.719	-0.364	-0.497
累计移出病例数	0.969	0.958	0.017	0.079

下载: 导出CSV

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