Evaluation of COVID-19 pandemic intervention policies based on Bayesian inference

doi:10.3969/j.issn.1000-5641.2025.03.018

Abstract

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

CLC Number:

O415.6

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.

Figures/Tables 6

Table 1

Fig.1

Table 2

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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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