重大突发公共卫生事件中社会群体行为演化的计算实验：以新冠疫情为例

doi:10.16381/j.cnki.issn1003-207x.2021.1002

Abstract

Abstract:

Scientific understanding of social group behavior and its evolution in major public health emergencies is the key way to improve the government's social governance ability. Firstly， a cellular automata （CA） model for the evolution of group behavior is constructed by describing the state， behaviors and evolution rules of different groups under the COVID-19 epidemic. Secondly， the evolution of group behavior under different scenarios is simulated by using the computational experimental method. Finally， according to the simulation results， the corresponding social governance countermeasures are put forward. The results indicated that： In view of the scientific social governance of the evolution of group behavior in the transmission of COVID-19， the spread of the epidemic can be effectively controlled. The main factors affecting the evolution of group behavior include initial infection rate， infection rate， incubation period and public opinion. Among them， the initial infection rate， infection rate and public opinion have a significant impact on the evolution of group behavior， while the effect of incubation period on the evolution of group behavior is not significant. Reducing the initial infection rate and infection rate can directly reduce the number of infected people， encourage individuals to take positive behavior， and guide the evolution of group behavior to positive behavior. If the management of public opinion is improper and most individuals take zero or negative behaviors， negative group behaviors will emerge in the social system， and almost all the society will be infected， which will increase the difficulty of social governance， and vice versa.Therefore， scientifically guiding the behavior of social groups and strengthening the social governance capacity of the government are important ways to effectively control the spread of COVID-19.

Key words: COVID-19, evolution of group behavior, social governance, computational experiment

CLC Number:

C931

Xingang Zhao,Ying Zhou. Computational Experiment on the Evolution of Social Group Behavior in Major Public Health Emergencies: A Case Study of COVID-19 Epidemic[J]. Chinese Journal of Management Science, 2024, 32(5): 207-217.

Figures/Tables 16

	决策序号	1	2	3	4	5	6	其他
条件	$P 1$	Y	Y	N	N	N	N		状态
	$P 2$	N	N	Y	Y	N	N
	$P 3$	N	N	N	N	Y	Y
	$λ$	Y	N	N	N	N	N
	$u$	N	N	N	N	N	N
	$V 1$	N	Y	Y	N	Y	N
	$V 2$	-	-	-	Y	-	Y
影响力	$H i, j = 0$							X	影响方案
	$H i, j = 0.3$		X	X		X
	$H i, j = 0.6$				X		X
	$H i, j = - 0.3$	X

	决策序号	1	2	3	4	ELSE
条件	$P 1$	Y	Y	N	N		状态
	$P 2$	N	N	Y	N
	$P 3$	N	N	N	Y
	$λ$	Y	N	N	N
	$u$	N	N	N	N
	$V 1$	-	-	-	-
	$V 2$	N	Y	Y	Y
影响力	$H i, j = 0$					X	影响方案
	$H i, j = 0.3$
	$H i, j = 0.6$		X	X	X
	$H i, j = - 0.3$	X

社会复杂自适应系统	元胞模拟系统
社会个体所处的复杂系统	元胞空间 $N$
人口规模	元胞总数 $n$
异质个体	元胞（ $i, j$ ）
个体状态（易感、潜伏、染病、移除）	元胞状态（ $S i, j t$ ）
初始感染率（initial-density）	元胞初始感染率（ $α$ ）
个体潜伏天数（latent-time）	元胞（E）潜伏期 $q$
个体传染率	元胞（E）传染率 $V 1$ ，元胞（I）传染率 $V 2$
个体治愈率（recovery-chance）	元胞治愈率 $λ$
个体死亡率（death-chance）	元胞死亡率 $u$
个体行为对其他个体行为及其演化的影响概率	元胞（ $i, j$ ）对邻居元胞的影响力 $H i, j$
个体采取不同行为的概率	元胞采取不同行为的概率 $P 1, P 2, P 3$

参数	取值		参考来源
Initial-density	20%		—
Recovery-change	7%		参考文献[22]
Death-chance	3.8%		参考文献[22]
$V 1$	40%		参考文献[23]
$V 2$	70%		参考文献[23]
Latent-time	5		《新型冠状病毒肺炎诊疗方案（试行第七版）》（国卫办医函〔2020〕184号）
Positive-change	70%	90%	2020年1月23日—2月10日，大部分公众积极采取正行为防范疫情，但仍有部分公众做出如拒戴口罩、公然违抗疫情工作人员、造谣传谣、瞒报行程等负行为。据此，模型假设在此期间内采取正、负和零行为的公众比例分别为70%、20%和10%。自《武汉市新冠肺炎疫情防控指挥部发布第12号通告》发布后，绝大部分公众在政策引导下自觉居家隔离，但仍有极少数公众无视政策规定采取负行为。据此，模型假设2020年2月11日—2月22日期间采取正、负和零行为的公众比例分别为90%、10%和0
Negative-change	20%	10%
Zero-change	10%	0

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行为	正行为						负行为						零行为
具体表现	自愿隔离	自觉佩戴口罩	配合接受检查	积极接受治疗	不信谣传谣	接种疫苗	自由流动	拒绝佩戴口罩	拒绝接受检查	不配合接受治疗	谎报瞒报行程或病情	造谣传谣	无反应
S	√	√	√		√	√	√	√	√			√	√
E	√	√	√	√	√		√	√	√	√	√	√	√
I	√	√	√	√	√		√	√	√	√	√	√	√
R	√	√	√		√	√	√	√	√			√	√

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Computational Experiment on the Evolution of Social Group Behavior in Major Public Health Emergencies: A Case Study of COVID-19 Epidemic

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