重大传染病多渠道疫情信息发布模式的演化路径分析

doi:10.16381/j.cnki.issn1003-207x.2021.0793

Abstract

Abstract:

Public information release and response to public concerns are key links in the prevention and control of major infectious diseases.The release of epidemic information requires the coordination and cooperation of government departments， medical experts， and the public. Promoting the linkage between government departments and medical experts in the early stages of the epidemic to quickly obtain and share real-time information plays an important role in curbing the spread of the epidemic. However， due to the differences in national conditions and social environment there are great differences in epidemic information release systems in various countries. Government departments and medical experts should timely adjust various epidemic prevention and control strategies， including information release， according to the development trend of the epidemic situation.According to the different subjects and channels of epidemic information release， four epidemic information release modes are summarized in the environment of highly uncertain information in the early stage of epidemic， namely linkage mode， government monopoly mode， expert disclosure mode and delay mode. Then the evolutionary game model of multi-channel information release of government departments and medical experts is constructed.Firstly， the Nash equilibrium was solved by a one-time simultaneous game to analyze the optimal effort level and the optimal income of both parties under each information release mode in the short term. Then， both sides continuously adjusted their information release strategies in a long period of time and analyzed the evolution path and equilibrium conditions of each epidemic information release mode.Finally， the influence of correlation coefficient on the evolution of information release mode is analyzed through a case.The results show that the trade-off between the short-term benefits of social stability maintenance and the long-term benefits of epidemic prevention and control is the key factor affecting evolution.There are multiple evolutionary paths in the information release mode. Increasing the long-term benefits of epidemic prevention and control is conducive to the linkage mode becoming an evolutionary equilibrium.In addition， the information release model also has multiple evolution paths such as “government monopoly” and “expert disclosure”， and it is necessary to avoid the multiple equilibrium of “government monopoly” and “expert disclosure” or the reciprocating fluctuation caused by information release disorder.As the leading force in the fight against epidemic diseases， government departments have taken a series of measures to promote the formation of a joint force between government departments and medical experts in the fight against epidemic diseases by improving the construction of epidemic information release system， reducing the cost of fighting epidemic diseases， changing the working mode of “tightening inside and loosening outside”， giving professionals a higher tolerance， and strengthening the monitoring and prediction of epidemic information， so as to improve the authority， accuracy and efficiency of epidemic information release.In case selection， the epidemic response measures and relevant data of China and the United States are selected to study the impact of cost coefficient and synergistic benefit coefficient on the evolution of information release mode.Four information release modes are summarized in the context of high information uncertainty at the early stage of the epidemic， then the optimal decision-making and the long-term evolution path of different information release modes combining the short-term benefits of social stability maintenance and long-term benefits of epidemic prevention and control are analyzed. Relevant research has certain theoretical reference significance for further improving the information release of major emergencies.

Key words: epidemic situation of infectious diseases, evolutionary game, emergency management, information release mode, public health emergency

CLC Number:

C931

Fengjiao Chen,Dehai Liu. Evolutionary Path Analysis of Multi-channel Epidemic Information Release of Major Infectious Diseases[J]. Chinese Journal of Management Science, 2024, 32(4): 237-249.

Figures/Tables 7

变量和参数	具体含义与定义	函数	具体含义与定义
$e$	政府部门和医疗专家在信息发布模式i中的努力水平分别为 $e i g > ? 0, e i d > ? 0 (i = 1,2, 3,4)$	$R l o n g$	政府部门及时发布信息为双方带来的疫情防控长期收益 $R l o n g - g$ ， $R l o n g - d$
$τ j$	政府部门及时与延缓发布信息时的努力水平对其长期或短期收益的影响系数， $τ j > 0, j = 1,2$	$R s h o r t - g$	政府部门延缓发布信息的社会维稳短期收益
$α 1$	政府部门及时发布信息时的努力水平对医疗专家的长期收益的影响系数， $α 1 > 0$	$R c$	政府部门及时发布信息，医疗专家主动发声，协同效应下双方获得协同收益 $R c - g$ 和 $R c - d$
$λ k$	医疗专家主动发声，政府部门选择不同行动策略时的协同收益系数， $λ k > 0, k = 1,2$	$R L$	政府部门延缓发布信息，医疗专家主动发声时，政府部门的社会维稳短期收益损失
$β 1$	医疗专家主动发声，政府部门及时发布信息时，医疗专家的协同收益系数， $β 1 > 0$	$R m$	政府部门及时发布信息，医疗专家保持沉默，政府部门获得潜在收益（如公信力和权威性提升，获得更多关注和认可）
$θ j$	政府部门及时发布信息和延缓发布信息的努力成本系数， $θ j (j = 1,2) > 0$	$R n$	政府部门延缓发布信息，医疗专家主动发声时，医疗专家获得潜在收益（如受到关注和认可，职业道德感提升）
$γ 1$	医疗专家主动发声的努力成本系数， $γ 1 > 0$	$C r e l e a s e - g$	政府部门的信息发布成本(包括信息收集处理成本、信息风险、问责成本等)
$m$	医疗专家保持沉默，政府部门及时发布信息的努力水平对其潜在收益的影响系数， $m > 0$	$C r e l e a s e - d$	医疗专家的疫情信息披露成本(包括信息收集处理成本、信息风险、问责成本等)
$n$	政府部门延缓发布信息，医疗专家主动发声的努力水平对其潜在收益的影响系数， $n > 0$	$C d e l a y - g$	政府部门延缓发布信息产生的成本(包括信息收集处理和管控成本、信息风险损失等)

政府部门

医疗专家

主动发声

(y)

保持沉默

(1 - y)

及时发布

(x)

联动模式

$R l o n g - g + R c - g - C r e l e a s e - g = τ 1 e 1 g + λ 1 e 1 g e 1 d - θ 1 e 1 g 2 / 2 ?, R l o n g - d + R c - d - C r e l e a s e - d = α 1 e 1 g + β 1 e 1 g e 1 d - γ 1 e 1 d 2 / 2$

政府垄断模式

$R l o n g - g + R m - C r e l e a s e - g = τ 1 e 3 g + m e 3 g - θ 1 e 3 g 2 / 2 ?, ?? R l o n g - d = α 1 e 3 g$

延缓发布

(1 - x)

专家披露模式

$R s h o r t - g - R L - C k 1 = τ 2 e 4 g - λ 2 e 4 g e 4 d - θ 2 e 4 g 2 / 2 ?, ?? R n - C r e l e a s e - d = n e 4 d - γ 1 e 4 d 2 / 2$

延迟模式

$R s h o r t - g - C d e l a y - g = τ 2 e 2 g - θ 2 e 2 g 2 / 2 ?, ?? 0$

政府部门

医疗专家

主动发声

(y)

保持沉默

(1 - y)

及时发布

(x)

联动模式

$θ 1 γ 1 2 τ 1 2 2 (γ 1 θ 1 - β 1 λ 1) 2, ?? 2 α 1 γ 1 τ 1 (γ 1 θ 1 - β 1 λ 1) + γ 1 β 1 2 τ 1 2 2 (γ 1 θ 1 - β 1 λ 1) 2$

政府垄断模式

$(τ 1 + m) 2 2 θ 1, ?? α 1 (τ 1 + m) θ 1$

延缓发布

(1 - x)

专家披露模式

$(γ 1 τ 2 - n λ 2) 2 2 γ 1 2 θ 2, ?? n 2 2 γ 1$

延迟模式

$τ 2 2 2 θ 2, ?? 0$

References 29

1	Savoia E， Lin L， Viswanath K. Communications in public health emergency preparedness： a systematic review of the literature［J］.Biosecurity and Bioterrorism-Biodefense Strategy Practice and Science， 2013， 11（3）： 170-184.
2	Please Kaufman E.， Shooting Stop. We Need the Beds［EB/OL］. （2020-04-01）［2020-04-02］. .
3	Heymann D L. Data sharing and outbreaks： best practice exemplified［J］.The Lancet，2020，395（10223）：469-470.
4	Yasnoff W A， O’Carroll P W， Koo D， et al. Public health informatics： improving and transforming public health in the information age［J］. Journal of Public Health Management and Practice， 2000， 6（6）： 67-75.
5	Li L， Xu L， Jeng H A， et al. Creation of environmental health information system for public health service： a pilot study［J］. Information Systems Frontiers， 2008， 10（5）： 531-542.
6	Vance K， Howe W， DellavalleR P. Social internet sites as a source of public health information［J］. Dermatologic Clinics， 2009， 27（2）： 133-136.
7	Boerma T， Abouzahr C. Health information systems： the foundations of public health［J］. Bulletin of the World Health Organization， 2005， 83（8）： 578-83.
8	何林，李幼平，徐娟，等. 从SARS防治看突发公共卫生事件的信息管制与成效［J］. 中国循证医学杂志， 2004（2）： 122-128.
	He L， Li Y P， Xu J， et al. Information management of public health emergencies in SARS crisis［J］.Chinese Journal of Evidence-Based Medicine， 2004（2）： 122-128.
9	叶育登. 信息的透明度政府的公信力社会的凝聚力——从政府对禽流感信息发布工作说起［J］. 中国行政管理， 2004（4）： 56-58.
	Ye Y D. Information transparency， government credibility and social cohesion——starting from the government's release of avian influenza information［J］. Chinese Public Administration， 2004（4）： 56-58.
10	Bialek S R， Allen D， Alvarado-Ramy F， et al. First confirmed cases of Middle East respiratory syndrome coronavirus （MERS-CoV） infection in the United States， updated information on the epidemiology of MERS-CoV infection， and guidance for the public， clinicians， and public health authorities-May 2014［J］. American Journal of Transplantation， 2014， 14（7）： 1693-1699.
11	Jang K， Baek Y M. When information from public health officials is untrustworthy： the use of online news， interpersonal networks， and social media during the MERS outbreak in south korea［J］. Health Communication， 2019， 34（9）： 991-998.
12	Avery E J. Public information officers’ social media monitoring during the Zika virus crisis， a global health threat surrounded by public uncertainty［J］. Public Relations Review， 2017， 43（3）： 468-476.
13	牛金玉，郝艳华，宁良文，等. 辽吉两省传染性疾病与感染信息动态变化趋势比较分析［J］. 中国公共卫生管理， 2020， 36（1）： 7-10.
	Niu J Y， Hao Y H， Ning L W， et al. Comparative analysis of the information dynamics trends of infectious diseases and infections in Liaoning and Jilin［J］. Chinese Journal of Public Health Management， 2020， 36（1）： 7-10.
14	Tabano D C， Cole E， Holve E， et al. Distributed data networks that support public health information needs［J］. Journal of Public Health Management and Practice， 2017， 23（6）： 674-683.
15	韩玮，陈樱花，陈安. 基于KANO模型的突发公共卫生事件信息公开的公众需求研究［J］. 情报理论与实践， 2020， 43（5）： 9-16.
	Han W， Chen Y H， Chen A. Research on public’s demands for information disclosure of public health emergencies based on KANO model［J］. Information Studies： Theory & Application， 2020， 43（5）： 9-16.
16	Mitchell L， Ross J V. A data-driven model for influenza transmission incorporating media effects［J］. Royal Society Open Science， 2016，3（10）.
17	朱晓峰，程琳，陆敬筠，等. 微政务信息公开信号博弈研究——基于转移支付的视角［J］. 情报理论与实践， 2017， 40（12）： 107-111.
	Zhu X F， Cheng L， Lu J Y， et al. Research on signaling game of micro government affairs information disclosure from the perspective of transfer payment［J］. Information Studies： Theory & Application， 2017， 40（12）： 107-111.
18	刘德海. 信息交流在群体性突发事件处理中作用的博弈分析［J］. 中国管理科学， 2005（3）： 95-102.
	Liu D H. The role of communication in the mass unexpected incident management［J］. Chinese Journal of Management Science， 2005（3）： 95-102.
19	徐寅峰，马丽娟，刘德海. 信息交流在公共卫生突发事件处理中作用的博弈分析［J］. 系统工程， 2005 （1）： 21-27.
	Xu Y F， Ma L J， Liu D H. The role of communication in the mass unexpected incident management［J］. Systems Engineering， 2005（1）： 21-27.
20	李燕凌，丁莹. 网络舆情公共危机治理中社会信任修复研究——基于动物疫情危机演化博弈的实证分析［J］. 公共管理学报， 2017， 14（4）： 91-101+157.
	Li Y L， Ding Y. Research on social trust repair of public crisis governance under network public opinion——based on an empirical analysis of evolutionary game of animal epidemic public crisis［J］. Journal of Public Management， 2017， 14（4）： 91-101+157.
21	Vijaykumar S， Meurzec R W， Jayasundar K， et al. What's buzzing on your feed？ Health authorities' use of facebook to combat zika in singapore［J］. Journal of the American Medical Informatics Association， 2017， 24（6）： 1155-1159.
22	Salathé M， Freifeld C C， Mekaru S R， et al. Influenza A （H7N9） and the importance of digital epidemiology［J］. New England Journal of Medicine， 2013， 369（5）： 401-404.
23	Lus B， Muriel A. Measuring the impact of increased product substitution on pricing and capacity decisions under linear demand models［J］. Production & Operations Management， 2009， 18（1）：95-113.
24	Guan P Q， Zhang J， Payyappalli V M， et al. Modeling and validating public-private partnerships in disaster management［J］. Decision Analysis， 2018， 15（2）： 55-71.
25	Yang M， Jacob V， Raghunathan S. Cloud service model's role in provider and user security investment incentives［J］. Production and Operations Management，2021， 30（2）： 419-437.
26	赵黎明，宋瑶，殷建立. 战略性新兴产业、传统产业与政府合作策略研究［J］. 系统工程理论与实践， 2017， 37（3）： 642-663.
	Zhao L M， Song Y， Ying J L. Cooperation among strategic emerging industry， traditional industry and the government based on differential game［J］.Systems Engineering-Theory & Practice， 2017， 37（3）： 642-663.
27	Lai S J， Ruktanonchai N W， Zhou L C， et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China［J/OL］.Nature： 585，410-413［2020-05-04］..
28	Our world in data. Overview of the Coronavirus Pandemic （COVID-19）［EB/OL］. ［2020-05-25］. .
29	于安. 论国家应急基本法的结构调整——以《突发事件应对法》的修订为起点［J］. 行政法学研究， 2020（3）： 3-10.
	Yu A. On the structural adjustment of the national emergencies basic act： starting with the revision of the emergency response law［J］. Administrative Law Review， 2020（3）： 3-10.

[1]	Xichen Lyu, Yinfeng Tian, Shihai Tian, Jiayuan Liu, Qingwei Kong. Research on Multi-Agent Decision-Making in Carbon Data Quality Supervision under MRV Mechanism: A Blockchain Technology Empowerment Perspective [J]. Chinese Journal of Management Science, 2025, 33(8): 355-368.
[2]	Jihai Zhang, Qichang Guo, Xu Jiang, Zhengfan Yang. Research on Emergency Supplies Reserve Strategy of Government-enterprise Cooperation from the Perspective of Hierarchical Responsibility [J]. Chinese Journal of Management Science, 2025, 33(7): 232-242.
[3]	Zhiwen Li, Xianhao Xu, Xu Guan, Qingguo Bai, Cheng Chen. Evolutionary Game Analysis for Blockchain Adoption Decisions of Two Oligopolies in An Agricultural Market [J]. Chinese Journal of Management Science, 2025, 33(7): 324-336.
[4]	Wenhui Qi, Hong Chi, Mingliang Qi, Shouhao Zhang. Simulation Study on Nuclear Emergency Evacuation Considering Residents' Dynamic Decision-making [J]. Chinese Journal of Management Science, 2025, 33(5): 259-267.
[5]	Lili Ding, Zhongchao Zhao, Kaixuan Zhang. Research on the Mechanism of Perceived Value on the Development of Personal Carbon Account Green Credit [J]. Chinese Journal of Management Science, 2025, 33(5): 344-355.
[6]	Hongyang He, Bin Zhang, Sujun Tian. Evolutionary Game Analysis of Government-Enterprise Collaborative Governance in Platform Economy [J]. Chinese Journal of Management Science, 2025, 33(5): 76-87.
[7]	Yu Bai, Xin Zhao, Lili Ding. Research on Chinese Emission Allowances Storage and Lending Mechanism Considering Enterprise Time Preference [J]. Chinese Journal of Management Science, 2025, 33(4): 357-368.
[8]	Weian Li, Ruirui Feng, Guangqian Ren, Minna Zheng. Differences in Government Incentive Models, Mixed Ownership Reform and Strategic Choice of State /Non-state Capital [J]. Chinese Journal of Management Science, 2025, 33(2): 356-368.
[9]	Huifang Nie, Jianjun Zhu. Two-Stage Decision-making Method in Response to Public Opinion Crisis Derived from Emergency Considering Emotion Evolution [J]. Chinese Journal of Management Science, 2025, 33(10): 112-125.
[10]	Zhi Yang. Quadripartite Evolutionary Game of Public Participation in Interactive Transboundary Pollution Control Compensation [J]. Chinese Journal of Management Science, 2024, 32(8): 261-273.
[11]	Xiangyan Sun,Xinchi Qu. Multi-stage Dynamic Evolution of Over-treatment Behavior under Drug-proportion Regulation [J]. Chinese Journal of Management Science, 2024, 32(8): 308-321.
[12]	Zuankuo Liu,Yu Li,Shouwei Li,Kaixuan Di,Changyu Liu,Rongkun Liu. Research on the Third-party Market Cooperation and Stability Strategy Based on the Perspective of Financing Mode [J]. Chinese Journal of Management Science, 2024, 32(8): 36-49.
[13]	Xusheng Yao,Shoufeng Ma,Shunqiang Ye,Shuai Ling. Yield or Go?Evolutionary Game Analysis of Pedestrian and Autonomous Vehicle Conflicts [J]. Chinese Journal of Management Science, 2024, 32(7): 138-149.
[14]	Zhiguo Shao,Mengdi Li,Chuanfeng Han,Lingpeng Meng,Qidi Wu. Simulation and Cooperative Mechanism of Construction Waste Disposal Based on Evolutionary Game [J]. Chinese Journal of Management Science, 2024, 32(7): 324-334.
[15]	Yanru Liang,Jianzhong Xu. Research on Incentive Policy Optimization of Enterprise Ecological Innovation Driving System [J]. Chinese Journal of Management Science, 2024, 32(6): 281-289.

Evolutionary Path Analysis of Multi-channel Epidemic Information Release of Major Infectious Diseases

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 29

Related Articles 15

Metrics

Comments

Recommended 0