基于Bayes推断的COVID-19流行病干预政策评估

doi:10.3969/j.issn.1000-5641.2025.03.018

摘要/Abstract

摘要：

为应对2019 年新型冠状病毒病 (corona virus disease 2019, COVID-19) 的大流行, 全球197个国家采取了各种防控政策, 取得了不同程度的抑制效果. 许多学者利用数学建模分析了各种非药物干预和疫苗接种政策对COVID-19传播的影响, 但这些研究主要侧重于定量评估干预政策对COVID-19再生数的影响. 建立了一个双层Bayes模型, 并基于Bayes推断分别定量评估了不同政策对COVID-19感染和恢复过程影响的有效性; 将干预措施分为公共卫生干预政策和管控政策两大类. 结果显示, 两类干预政策都可以降低COVID-19的感染率, 提高COVID-19的恢复率; 但干预政策的类型对传播过程和恢复过程的影响有明显的倾向性, 即公共卫生干预政策更有助于COVID-19的恢复过程, 大多数管控政策及部分公共卫生措施对COVID-19的传播过程影响较大.

关键词: COVID-19, 传播, 恢复, Bayes推断, 政策评估

Abstract:

In response to the 2019 novel coronavirus disease (COVID-19) pandemic, 197 countries have implemented various government control policies to achieve varying degrees of suppression. Many scholars have analyzed the impact of various non-pharmaceutical interventions and vaccination policies on COVID-19 using mathematical modeling. These studies have primarily focused on the quantitative assessment of the impact of interventions on the reproductive number of COVID-19 patients. This study establishes a two-layer Bayes model to quantitatively estimate the effectiveness of different policies on COVID-19 infection and recovery based on Bayesian inference. It categorizes intervention measures into two groups: public health intervention policies and control policies. The results show that both types of intervention policies can reduce the infection rate of COVID-19 and improve the recovery rate. However, each type of intervention policy has a distinct impact on the transmission and recovery processes. Specifically, public health intervention measures have a greater impact on COVID-19 recovery, while control policies and public health measures significantly affect the transmission of COVID-19.

Key words: COVID-19, transmission, recovery, Bayesian inference, policy evaluation

中图分类号:

O415.6

罗俊藤, 唐明. 基于Bayes推断的COVID-19流行病干预政策评估[J]. 华东师范大学学报（自然科学版）, 2025, 2025(3): 157-166.

Junteng LUO, Ming TANG. Evaluation of COVID-19 pandemic intervention policies based on Bayesian inference[J]. J* E* C* N* U* N* S*, 2025, 2025(3): 157-166.

图/表 6

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参考文献 18

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参数	物理意义	参数	物理意义
${R}_{0}^{\rm{c}}\left(t\right)$	不同国家在某一天的瞬时基本再生数(时变)	${\gamma }_{0}^{\rm{c}}$	不同国家的基本恢复率
$ {\alpha }_{i} $	管控政策对基本再生数的影响参数	$\alpha '_{i}$	管控政策对恢复天数的影响参数
$ {\delta }_{i} $	公共卫生政策对基本再生数的影响参数	$\delta '_{i}$	公共卫生政策对恢复天数的影响参数
${n}_{7}^{\rm{c}}$	基本再生数的随机噪声(由未知因素带来的随机误差), 每7 d变化1次	${n}^{\rm{c}' }_7$	恢复天数的随机噪声, 每7 d变化1次
${\varDelta }_{\rm{c}}$	不同国家在实施干预政策时展现出显著的差异性, 体现在基本再生数的噪声上	${\varDelta }' _{\rm{c} }$	不同国家在实施干预政策时展现出显著的差异性, 体现在基本恢复率的噪声上
$ \tau $	温度对基本再生数的影响参数	$ \tau' $	温度对恢复天数的影响因子

干预政策		${\Delta }\beta \left(t\right)$/%	${\Delta }(1/\gamma (t\left)\right)$/%
政府管控政策	关闭学校	12.60	–13.50
	关闭工作场所	12.05	–12.29
	取消大型集会	–13.79	11.58
	限制聚集人数	–16.84	14.70
	关闭公共交通	–11.90	10.82
	要求居家	–17.82	17.08
	限制城市间人员流动	13.03	–17.04
公共卫生干预政策	核酸或抗原检测	–38.19	–15.66
	接触追踪	–16.17	20.45
	强制佩戴口罩	–14.09	15.88
	接种疫苗	15.90	–46.60

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