Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

REDISSE in Benin and Lessons Learned for the Management of Public Health Emergencies after Five Years of Implementation

Introduction: Benin was embarked on phase 3 of the REDISSE Benin project (Regional Disease Surveillance Systems Enhancement) which began in 2018. The objectives were in five key components namely, Surveillance and health information;Laboratory capacity building;Emergency preparedness and response;Human resources management for effective disease surveillance and epidemic preparedness;and Institutional Capacity Building, Project Management, Coordination and Advocacy. After five years of implementation, this study aimed at the documentation of lessons learned and best practices. Methods: A descriptive cross-sectional study. Apart from individual semi-structured interviews, a thematic workshops bringing together the project’s main stakeholders recruited on an exhaustive way by component to identify and validate lessons learned, good practices and propose improvement mechanisms to be taken into account by the sector. Criteria were set up and used to validate best practices and lessons learned. Results: A total 54 (Surveillance workshop), 47 (Preparedness & response workshop), 53 (Human Resources workshop), 26 (Laboratories workshop) participated to the thematic workshops, and five interviews. The good practices (33: 9 for animal health, 7 for human health and 17 crosscutting) and lessons learned (10: 3 for animal health and 7 for human health) have been identified and have been the subject, depending on the case, of proposals for improvement or conditions necessary for their maintenance. Discussion: The richness of a project lies not only in the immediate achievement of its results, but also and above all, in its usefulness for similar interventions, whether in the local, regional, national or international context. It is in this context that the REDISSE project has set out to make public the various lessons learned and best practices from the implementation of its activities over a period of some five consecutive years.

Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.

Hierarchical Research on Pragmatic Failures of Apology in Public Events Under the Vision of New Media

Under the new media,the dissemination and the influence of public events are often far more profound than traditional media.Therefore,it has gained a high degree of social attention.We take the apology mistakes in public events as an example,sort out the types and causes of the mistakes into a context,and propose some relevant strategies.

The Influence of Significant Public Health Events on Offline Consumption and Its Pathways

In the face of a significant public health event,consumers may either increase their panic buying or decrease their willingness to make purchases.This study focuses on the impact of a significant public health event on offline store sales and consumer consumption,utilizing data from chain convenience stores in Hefei and Wuhu during early 2019 and early 2020 in China.Employing a difference-in-differences model,the study investigates the effect of the significant public health event outbreak on weekly store sales,order numbers,and consumer consumption in terms of product quantities,transaction amount,average amount per order,and transaction frequency.Different from prior literature that finds hoarding behavior of consumers online,the findings of this paper indicate a significant reduction in stores’offline weekly sales and order numbers,as well as consumers’offline weekly consumption across the four dimensions,as a result of the significant public health event outbreak.Additionally,employing a mediation model,the study explores the pathway of population mobility through which the significant public health event adversely affects offline consumption.Furthermore,subset analysis is conducted for stores located in different areas and consumers with varying characteristics,revealing that the aforementioned conclusions predominantly apply to stores situated in office areas and residential areas,as well as consumers with either no apparent preference for different product categories or a noticeable preference for food.

Enlightenment of COVID-19 Treated by Botanical Drugs on the Development of Drugs for Rare Diseases in China

Objective To study the feasibility of developing botanical drugs to treat intractable diseases and play an important role in dealing with major public health crises.Methods From January 1990 to May 2021,a bibliographic search was carried out on the use of botanical drugs,rare disease drugs,related registration management policies and regulations in PubMed and CNKI.The following keywords were searched in the database:Rare disease policies and regulations,orphan drugs,botanical drugs for intractable diseases,botanical drugs for the treatment of new coronary pneumonia,traditional Chinese medicine,and emergency guidelines for major public health crisis.Other data were obtained from“Chinese Pharmacopoeia”and relevant Chinese government websites for sorting and analysis.Results and Conclusion Based on 39 Chinese corresponding policies and regulations,challenges and opportunities of developing and researching drugs for treating rare diseases were found out after the analysis and comparison.Based on the study of national policies on drugs for rare diseases,the priority review and approval procedures in the drug registration,as well as China’s emergency guidelines and policies for major public health events,some problems in the use of drugs for rare diseases are found out.Therefore,it is recommended to actively adopt the property rights protection system,explore the folk prescriptions of traditional Chinese medicine and the potential of hospital preparations,and the registration review strategy of giving priority to the use of botanical drugs for rare diseases.Thus,the international status of botanical drugs for rare disease and the influence of responding to major public health events can be enhanced.

Analysis of CDC’s Role in Public Health Emergencies

Objective:To explore the role of the Centers for Disease Control and Prevention(CDC)in public health emergencies.Methods:The details of 12 public health events that occurred between January 2021 to December 2022 were analyzed to explore the roles of the CDC.Results:There were 160 patients involved in 10 public health events in 2021 and 48 patients involved in 2 public health events in 2022.Besides,the proportion of school public health events in 2022 was 0%,which was lower than in 2021,which was 80%(P<0.05).99.38%of patients during public health events were sent to the hospital promptly in 2022,which was higher than that in 2021,which was 81.25%(P<0.05).Furthermore,the average time taken for the CDC to control public health events in 2022 was 20.11±1.62 hours,and the average time taken to send inspection reports was shorter than that in 2021.The public satisfaction score was also higher in 2022 compared to 2021(P<0.05).Conclusion:The role of the CDC is to control infectious diseases.Therefore,it is important to pinpoint the existing problems in the strategies implemented by the CDC so that more improvements can be made to better prevent infectious diseases.

Analysis of Adverse Reactions Related to Drugs and Vaccines Received at the National Centre for Pharmacovigilance from 2009 to 2016 in Togo

Objectives: To assess the received suspected adverse events occurring upon treatment with drugs and vaccines, at National Centre for Pharmacovigilance, in Togo, from 2009 to 2016. Methods: A crossover study was conducted in order to collect data about patients, drugs, suspected adverse events and notifiers. Suspected adverse events were classified using Med DRA 19.1. Notification’s circumstances were classified into Public Health Programs’ campaigns and routine practice. Data were collated into Excel spreadsheet and processed with SPSS software. Key Findings: Regional distribution is irregular. Of the 322 collected report forms, paramedics have notified 60.8% of the cases. Adult patients were the most represented (70.2%). Public Health Programs campaigns provided 72.6% versus 27.4% for routine practice including Neglected Tropical Diseases (41.4%), immunization (27.7%), tuberculosis (25.9%) and 4.5% for HIV. Skin disorders were the most prevalent suspected adverse events (147 sheets;45.7%) followed by general disorders and administration site disorders (29.8%) and gastro-intestinal disorders (12.7%). General anti-infective drugs for systemic use, antiparasites, and insecticides were the most reported class of medications (161 sheets;44.7%). Conclusions: A thorough follow-up of pharmacovigilance launched activities is needed to build a sustainable adverse effect’s surveillance system and routine practice has to be strengthened.

Business Process Analysis and Simulation: The Contact Center of a Public Health and Social Information Office

This article proposes a framework, called BP-M* which includes: 1) a methodology to analyze, engineer, restructure and implement business processes, and 2) a process model that extends the process diagram with the specification of resources that execute the process activities, allocation policies, schedules, times of activities, management of queues in input to the activities and workloads so that the same model can be simulated by a discrete event simulator. The BP-M* framework has been applied to a real case study, a public Contact Center which provides different typologies of answers to users’ requests. The simulation allows to study different system operating scenarios (“What-If” analysis) providing useful information for analysts to evaluate restructuring actions.

Research on the Dissemination Mechanism and Guiding Tactics of Public Opinion in Catastrophic Event Network

This paper conducts the analysis on the dissemination mechanism and guiding tactics of public opinion in catastrophic event network. Opinion evolution mechanism can be roughly divided into two classes. One is the belief of people based on their neighbors, on the basis of the public opinion is in the social network of acquaintances. Such networks are mostly using cellular automata model for data simulation, the results of numerical simulation are speci？ c to stabilize near the critical value show that the system will reach a critical stable state. The network information collection is the source of network public opinion monitoring its breadth and depth determine the monitoring results for the clear theme of public opinion information collection. Under this basis, this paper proposes the novel idea of making the dissemination mechanism easier. The proposed idea is novel and necessary, the effectiveness is proved via the theoretical analysis.

A Study on the Evolution of Mechanism of Infectious Disease Analysis Model of Event Using Micro-Blog Platform

After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.

Centralization and perceived control of COVID-19 during the pandemic:A cross-sectional study

Objective:To uncover the impact of centralization of COVID-19 and perceived control of COVID-19 on society during the pandemic.Methods:We recruited a total of 1041 people in this cross-sectional study.The data were collected using a questionnaire booklet covering demographics,a COVID-19-related information form,the Centrality of Event Scale,and the Perception of Control of COVID-19 Scale.We utilized independent samples t-test,chi-square test,and one-way analysis of variance to analyze the data.Results:1041 questionnaires were collected and no questionnaire were excluded from our study.Slightly more than half of the participants(51.2%)stated that social isolation impaired public mental health,while 30.1%reported adverse impacts of the pandemic on their sleep quality.Participants with changes to their sleep patterns were found to centralize COVID-19 more.Moreover,measures against COVID-19 and constant announcements of the daily number of cases in the media brought both positive and negative effects on people and further contributed to the participants’centralization of COVID-19.Individuals with low centralization scores were concluded to perceive COVID-19 as a minor disease.Healthcare professionals without a relative diagnosed with COVID-19 and those satisfied with treatment opportunities had a higher perceived control of COVID-19,while those who were not interested in statistical data on COVID-19 and who had difficulty complying with the rules had a lower perceived control of COVID-19.Besides,poorer perceived control of COVID-19 was found to adversely affect sleep quality.Furthermore,healthcare professionals scored higher on the inevitability subscale of the Perception of Control of COVID-19 Scale.Finally,among the participants,most COVID-19 survivors thought COVID-19 to be an avoidable disease.Conclusion:In addition to its physical impacts,COVID-19 adversely impacts on mental health,and these effects are closely linked to a society’s centralization of COVID-19 and perceived control of COVID-19.

Application of Multi-relational Data Clustering Algorithm in Internet Public Opinion Pre-warning on Emergent

At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the traditional research method of internet public opinion pre-waming mainly relies on manual analysis, which is too inefficient to adapt to the analysis of massive internet public opinion information. According to the above analysis, this paper puts forward an internet public opinion pre-warning mechanism on emergent event based on multi-relational data clustering algorithm, discusses the specific pre-waming from the aspects of the state and dissemination of internet public opinions and the historical data, and automatically classifies the internet public opinions through multi-relational data clustering algorithm. And the results show that such method can be used to effectively study the internet public opinion pre-waming on emergent event, with the accuracy rate of as high as 95%.

Study of the Impact of the COVID-19 Pandemic on Health Insurance Fund of Hubei Province in 2020

Objective To provide a reference for future budget of health insurance fund for the COVID-19 pandemic in other parts of China or other major public health events.Meanwhile,it also offers a reference for the government to introduce and adjust the policy of health insurance funds after the pandemic.Methods Models of the income,expenditure and cumulative balance of health insurance fund in Hubei Province in 2020 were established and compared.The former was mainly established and tested using SPSS 26.0 and Excel,while the latter was obtained by inferential analysis.Results and Conclusion The COVID-19 pandemic reduced the income and increased expenditure of the health insurance fund in Hubei Province in 2020,resulting in a deficit.The COVID-19 outbreak has caused a deficit in health insurance fund of Hubei Province in the short term,but in the long term,the outbreak will not have a major impact on the health insurance fund.

中国乡村体育赛事治理的互动机制与模式创新—基于价值治理分析框架

凝聚共同体精神、价值认同和道德规范是实现体育治理现代化、柔性化的重要方式。将多元治理主体的地位和属性这一重要常量纳入乡村体育赛事治理体系进行考察,从乡村体育共同体价值观念整合的视角,回应乡村体育赛事举办中广泛存在的超经济行为逻辑。认为:价值治理应遵循责任认知的价值规范、相互信任的价值理念和“以村民为中心”的价值本位等基本原则;应从关系结构及其信念伦理、权益结构及其理念互构、规则结构及其评价导向展开对组织结构的考察;在历史维度上,乡村体育赛事经历了从“团结目标”到“争做先锋”再到“热爱驱动”,并迈向“荣誉追求”的价值演变进程;面对融媒体时代复杂的风险治理格局,乡村体育赛事需要科学技术赋能,更需要优化价值驱动机制,吸纳多元价值目标,构建更具开放性、兼容性和共享性的制度系统。

基于区块链技术的体育热点事件中的网络情绪治理

5G时代下体育网络情绪传播业态发生巨大变革,为其治理带来了新的挑战。当前,体育网络情绪传播主体和范围不断拓展、传播速度不断加快、传播不确性不断增加,使体育网络情绪治理难度不断提高。区块链技术凭借其去中心化、难以篡改、公开透明等优势,不仅可以有效提升体育网络情绪治理的精准性,而且可以有效提升治理效率。基于区块链技术从不同层面构建体育网络情绪治理机制,在宏观层面构建体育网络情绪信息联合治理平台,在中观层面建立基于大数据的体育网络情绪监测体系,在微观层面自媒体及其服务商实时跟踪处理体育网络情绪,以加强体育舆论引导,促进体育事业健康发展。

网络舆情事件链演化分析

为防范舆情风险,分析网络舆情的细粒度演化,提出一种去冗余的衍生事件内容关联演化分析框架。通过文本挖掘技术从海量文本流中提取主要的衍生事件,将舆情内容压缩到人工可判读的数量级;利用词移距计算相邻两个时间片上的衍生事件相似度,构建反映演化关系的衍生事件链图。以“上海特斯拉自燃”事件为例进行事件链演化分析,得到微博网络舆情事件发展不同阶段涉事主体在各个衍生事件中的话题转移关系,最后鲁棒性分析的结果验证了该分析方法具有降低微博短文本冗余信息的能力,提高了事件演化关联识别分析的准确性。该研究方法为舆情事件的事后复盘、同类舆情事件的预判和介入、衍生事件的科学研判提供了决策支持。

护理人员应对突发公共卫生事件心理健康状况影响因素分析

目的调查临床一线护士应对突发公共卫生事件心理健康状况及其影响因素,为提升护士心理健康提供理论依据。方法于2020年2月采用便利抽样法选取某市四所医院中(包括所有定点医院及三级综合医院)工作在发热门诊及隔离病房的192名一线护士作为研究对象。采用一般资料调查表、一般心理健康状况问卷(GHQ-12)、护士职业获益感量表进行调查。结果本次调查中有51.04%(98/192)的一线护士GHQ-12得分≥3分,疑似有心理健康问题。护士职业获益感得分为(44.25±9.90)分。Logistic多因素回归分析结果显示:工作时长、有类似灾害救护经历、护士职业获益感是一线护士心理健康状况的主要影响因素(P<0.05)。结论突发公共卫生事件期间,临床一线护士心理健康问题较为普遍,提高护士职业获益感有利于减少心理问题,管理者需要针对主要影响因素进行干预,改善此类人群心理健康状况。

旅游者风险感知对目的地信任影响研究——政府信任的中介作用

旅游者风险感知与信任密切相关,各种风险所带来的不确定性会改变社会互动并造成信任危机,而旅游者目的地信任是影响旅游者满意度与忠诚度的重要因素。以安东尼·吉登斯关于风险感知和信任的论述为理论依据建立分析框架,通过网络问卷对326名风险经历者进行调查,并以政府信任为中介变量,以风险沟通、应急处置为调节变量,重点探讨了公共卫生事件情境下旅游者风险感知对其目的地信任的作用机制与效应。结果显示:风险感知对旅游者政府信任具有显著负向影响,而风险感知对旅游者目的地信任无显著影响;政府信任对旅游者目的地信任具有显著正向影响;风险沟通在风险感知与政府信任中不存在调节作用,应急处置在风险感知与政府信任中存在调节作用。