Based on the “Scenario-Response” Framework: Analysis of Community Scenario Elements in Sudden Major Respiratory Infectious Disease Outbreaks

Based on the “Scenario-Response” Framework: Analysis of Community Scenario Elements in Sudden Major Respiratory Infectious Disease Outbreaks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Based on the “Scenario-Response” Framework: Analysis of Community Scenario Elements in Sudden Major Respiratory Infectious Disease Outbreaks Yongwen Liu, Haoyang Chen, Yan Li, Jing Feng, Tiemin Zhai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8190005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In recent years, frequent outbreaks of major respiratory infectious diseases (such as Ebola and COVID-19) have posed severe threats to public health and social stability. As the fundamental unit and frontline of epidemic prevention and control, the response capability of communities directly impacts the overall effectiveness of outbreak containment. The extraction of scenario elements can fully analyze the composition and evolution of epidemic scenarios, while the traditional "prediction-response" paradigm often struggles to accurately match the complex and evolving epidemic situations. Therefore, introducing the “scenario-response” paradigm to deeply analyze key scenario elements in community-level epidemic evolution and construct effective response mechanisms has become an urgent imperative for enhancing precision prevention and control capabilities at the grassroots level and strengthening the first line of defense. Methods Based on the “scenario-response” paradigm, this study employed the grounded theory method to systematically review reports, documents, and cases related to sudden major respiratory infectious disease outbreaks, extracting community scenario elements. Results Through qualitative analysis of the initial coded data (yielding 211 concepts), a theoretical model of community scenario elements for sudden major respiratory infectious disease outbreaks in China was established, comprising 40 categories, 14 main categories, and 3 core categories (disaster-causing scenarios, disaster-bearing scenarios, disaster-resisting scenarios). Conclusion By analyzing community epidemic scenario elements from a situational perspective, this study further proposes response mechanisms for communities confronting sudden major respiratory infectious disease outbreaks, providing theoretical support and practical guidance for emergency decision-making. Sudden major respiratory infectious disease Scenario-response Grounded theory Community Figures Figure 1 Figure 2 Background With the acceleration of globalization in the 21st century, factors such as population growth, global climate change, and ecological degradation have made sudden major respiratory infectious disease outbreaks a critical threat to human health and socioeconomic development. This issue is particularly pronounced in China, given its large population and high mobility. From the 2003 SARS outbreak and the 2009 H1N1 influenza pandemic to the global COVID-19 crisis in 2020, such outbreaks have inflicted substantial economic losses and human casualties on China, exposing multifaceted challenges in prevention and response 1 – 4 . The inherently sudden, complex, and uncertain nature of these diseases significantly diminishes the effectiveness of the traditional “prediction-response” model. Throughout the lifecycle of an outbreak-from emergence and escalation to gradual subsidence-diverse complex scenarios arise, each exhibiting distinct “scenario-dependent” characteristics. In response, scholars have progressively developed the “scenario-response” theoretical framework 5 – 6 . This approach dynamically generates and implements emergency decisions based on continuously evolving scenarios during an event’s progression, thereby reducing uncertainties in outbreak management 7 . Communities, as foundational units of epidemic control, play a pivotal role. They serve not only as a central force for mobilizing public participation but also as the frontline for resource allocation, early warning, and response. Communities possess unique advantages in implementing “scenario-response” strategies. Consequently, extracting community scenario elements from past major respiratory infectious disease outbreaks provides essential material for constructing potential future scenarios. This enables more comprehensive preparedness and response to protect public health and social stability, carrying significant practical implications. The main research contents of this study are as follows: Firstly, based on the grounded theory, the community situational elements of the sudden outbreak of major respiratory infectious diseases in China are coded in three levels. Secondly, the theoretical saturation of the coded data is tested. Thirdly, according to the obtained community situational factors, the theoretical model of community situational factors of sudden major respiratory infectious diseases is constructed. Finally, the implementation path of community response to epidemic situation optimization is put forward. The rest of this study is arranged as follows: The second part is literature review. The third part introduces materials and methods, including data sources and grounded theory research methods. The fourth part describes the research results, including the coding of situational elements and the model of situational elements. The fifth part summarizes the full text, puts forward the optimization path of community response to the epidemic situation, expounds the significance of the research, and summarizes the limitations of the paper and the future research direction. Literature review Respiratory infectious diseases can be divided into two categories according to their epidemic characteristics and influence degree. One is common respiratory infectious diseases, and the course of this kind of disease generally presents obvious seasonal laws, and most of them enter the high-incidence stage in autumn, winter and spring every year. Such as influenza, mumps, hand-foot-mouth disease and tuberculosis. The other is a major emerging acute respiratory infectious disease. Because of its emerging nature, people generally lack immunity, so it is easy to spread widely through social communication networks, thus threatening people’s health and causing social panic 8 . In recent years, SARS, H7N9 avian influenza, novel coronavirus (COVID-19) and other major respiratory infectious diseases have occurred frequently. In this context, accurately predicting the evolution trend of major respiratory infectious diseases can provide more real and effective information support for decision makers, make them carry out risk assessment more scientifically, and then formulate and implement more targeted prevention and control strategies to achieve optimal allocation of emergency resources. The traditional “prediction-response” emergency decision-making model mainly predicts the evolution track of emergencies and describes its development trend on the basis of continuous integration and updating of information sources 9 . Although this model can help emergency managers reduce the losses caused by unconventional events with the help of early warning and emergency planning, it is difficult for the “prediction-response” model to build an accurate model and make effective decisions in the face of highly complex unconventional events and lack of historical experience. In order to effectively solve this problem, the “scenario-response” emergency decision-making model came into being. Scenario is a collection of many key elements, which is used to describe the possible situation in the future. The core of “scenario-response” is to make dynamic decisions based on the current development trend of the incident and the possible evolution scenarios in the future. This model effectively makes up for the deficiency of the “prediction-response” model in accurate prediction, which not only reduces the uncertainty in the emergency response process, but also improves the decision-making efficiency. The scenario construction of sudden major respiratory infectious diseases aims to analyze and describe the potential development path and consequences of the epidemic in advance, and provide key information support for emergency planning and preparation. Therefore, developing the scenario construction of such risks is a key measure to improve the government’s ability to cope with risk challenges under uncertain environment and promote the transformation of risk management from passive response to active preparation in advance. Research on scenario construction has been explored by scholars worldwide across multiple domains. Domestic studies primarily focus on the localized development and application of theories. Liu Tiemin’s proposed “scenario-response” risk management model emphasizes extracting inherent patterns through summarizing historical precedents 10 . Scholars such as Wang Yanxin and Rong Juntao have extended its application to emergency frameworks and crisis decision-making, systematically addressing critical issues and demands in China’s emergency preparedness practices 11 – 12 . In practical applications, Wang Wenjun conducted scenario analyses focusing on the design, development, and refinement of civil aviation emergency management systems 13 . Following the “scenario-task-capability” research paradigm, You Shangyuan constructed an emergency management capability evaluation framework for power system disruptions 14 . Zhang Wei et al. developed risk scenarios for natural disasters like floods and typhoons to enhance the effectiveness and quality of transportation emergency drills 15 . International research more broadly integrates modeling and simulation technologies for cross-domain risk analysis. Kappos et al. constructed earthquake disaster scenarios to analyze seismic risk characteristics 16 . Dettinger et al. designed storm scenarios for emergency preparedness exercises 17 , while Manley et al. simulated building environment impacts on crowd evacuation 18 . Post-COVID-19, studies in public health emergencies have surged significantly. Scholars including Ivanov, Karatayev, Iwata, and Van Genugten have developed diverse epidemic or contagion scenarios to assess intervention effectiveness, supply chain disruptions, and outbreak trajectories 19 – 22 . From the above research, it can be found that the existing research mainly focuses on natural disasters, safe production, public health and other scenarios, in contrast, the research on the subdivision of sudden major respiratory infectious diseases, especially focusing on the community level, is relatively scarce. Most of the existing achievements focus on the use of quantitative modeling technology to simulate and predict the development track and potential impact of the epidemic under different preset scenarios. However, these scenarios are usually directly set, and there is little in-depth analysis of the construction logic and methods of the scenarios themselves. It is particularly noteworthy that the research on community-oriented systematic scenario construction is still blank. Therefore, based on the “scenario-response” theoretical paradigm, this study strictly follows the standard process of scenario variable description in scenario construction, aiming at systematically presenting the scenario construction process and its key elements of major respiratory infectious diseases in the community, with a view to supplementing the current situation that academic circles have not paid enough attention to this subdivision. Materials and Methods Data Source Data were obtained from official reports, guidance documents, and national/regional case studies on three major respiratory infectious disease outbreaks-Severe Acute Respiratory Syndrome (SARS) in 2003, H7N9 avian influenza in 2013, and COVID-19 in 2019-via the official websites of China’s National Health Commission and governmental authorities. A corpus of 1.3643 million Chinese characters was extracted, encompassing China’s emergency response strategies for sudden major respiratory infectious disease outbreaks. Following thematic screening of this material, 327,600 characters specifically related to community-level outbreaks were selected. Grounded theory coding was subsequently applied to the finalized dataset. Research Method Grounded theory is a qualitative analysis method rooted in original data, whose core lies in conceptualizing and abstracting data to extract core concepts and categories, thereby constructing theories from the bottom up 23 . Its core process includes three levels of coding: open coding, axial coding, and selective coding, which progress from data fragmentation to theoretical integration. In this study, using the qualitative analysis software Nvivo12, the collected and preliminarily sorted data were gradually conceptualized and categorized. To ensure inter-rater reliability and validity, two-person independent coding was adopted; coding discrepancies were resolved through group discussion and literature review. Meanwhile, 30% of the text materials were randomly reserved to test theoretical saturation, i.e., coding continued until no new categories emerged from the reserved data. The specific analysis flow is shown in Fig. 1 . Results Coding of Community Scenario Elements for Major Sudden Respiratory Infectious Disease Outbreaks (1) Open Coding As the initial stage of coding, open coding requires researchers to study the original text deeply and identify and extract the initial concepts 24 . In this study, the original data collected were reviewed first, and then the repeated and invalid contents were eliminated, and then the open coding was implemented. After this process, 211 initial concepts were finally extracted and summarized into 40 categories (Table 1 ). Table 1 Results of Open coding Initial concept Categorization a1 Population; a2 Population Density; a3 Age Difference; a4 Economic Income Level; a5 Education Level; a6 Geographical Location; a7 Gender Differences; a8 Population Structure; a9 Aging A1 Population characteristics a10 Returning home during the Spring Festival; a11 Visit relatives and friends during the Spring Festival; A2 Population mobility a12 Wash hands before meals;a13 Open windows for ventilation༛a14 Maintain regular daily routines༛a15 Disinfect surfaces༛a16 Practice hygienic dining habits༛a17 Practice safe poultry farming༛a18 Forms of living environment༛a19 Wear face masks properly༛ A3 Living habits a20 Closure of public venues;a21 Ban on mass gatherings༛a22 Movement restrictions༛a23 Maintain physical distancing༛a24 Community containment measures༛a25 Cancellation of congregate events༛a26 Road closures༛a27Grid-based management༛a28 Culling of infected poultry༛a29 Live market closures༛a30 Full-chain wildlife trade restrictions༛ A4 Community emergency management a31 Isolation;a32 Close contact management༛a33 Suspected case management༛a34 Epidemiological investigation༛a35 Medical observation༛a36 Emergency response level༛a37 “Four Earlies” ( Early Detection, Early Reporting, Early Isolation, Early Treatment); “Four Concentrations” (Concentrate Patients, Concentrate Experts, Concentrate Resources, Concentrate Treatment)༛a38 Infection Prevention and Protection༛a39 Fever Clinic༛a40 Pre-examination Triage༛a41 Disinfection and Isolation༛a42 Patient Referral༛ A5 Community case management a43 Community Party Branch;a44 Community Residents’ Committee A6 Epidemic prevention and control leading group a45 Community Police Officer;a46 Community Property Service Staff༛a47 Community Grid Worker༛a48 Community Resident Representative༛a49 Community Volunteers༛a50 Community Assistant༛a51 Community Health Worker༛a52Epidemiological Investigator༛ A7 Community Workers a53 Prevention for Pregnant and Postpartum Women;a54 Infants and Young Children༛a55 Elderly Population༛ A8 Vulnerable Populations a56 Resumption of Work and Production;a57 Material preparation༛a58 Green Channel(Fast-track mechanism for priority handling, e.g., medical supplies)༛a59 E-commerce Supply-Demand Coordination Mechanism༛a60 Daily Necessities Supply Team༛a61 Assessment of Daily Necessities Supply Status༛a62 Material Allocation༛a63 Governor’s “Rice Bag” and Mayor’s “Vegetable Basket” Responsibility System༛a64 Logistics Transfer Station༛ A9 Resource Security a65 Medical Insurance Reimbursement;a66 Urban-Rural Medical Assistance Program༛a67 Financial Support༛a68 Epidemic Prevention-Control and Treatment Funding༛ A10 Financial Security a69 Paying attention to the psychological status of staff;a70 Staff Compensation and Benefits༛a71 Community Workforce Management༛a72 Personnel Allocation༛a73 Temporary Work Subsidies༛a74 Health Protection for Community Staff༛a75 Humanistic Care Support༛ A11 Staff Support and Incentives a76 Essential Living Supplies;a77 Supply-Demand Imbalance༛ A12 Daily Necessities Supply a78 Medical Masks;a79 Personal Protective Equipment༛a80 Alcohol-Based Hand Sanitizer༛ A13 Personal Protective Equipment a81 Protective Coveralls;a82 Protective Goggles༛a83 Protective Goggles༛a84 Medical Supplies༛a85 Ambulances༛a86 Basic Medical Equipment༛a87 Community Hospital Bed Capacity༛a88 Medications༛a89 Medical Consumables༛a90 Vehicle-Mounted Equipment for Professional Teams༛a91 Thermometers༛a92 Vaccines A14 Medical Resources a93 Inter-Departmental Communication on Epidemic Control;a94 Community-Level Coordination༛ A15 Multi-Sectoral Coordination Mechanism a95 Dissemination of Epidemic Prevention Knowledge;a96 Q&A Releases on Epidemic Control༛a97 Public Awareness Campaigns & Advisory Services༛a98 Educational Publications & Booklets༛a99 Interpersonal Health Education Initiatives༛a100 Personal Protection Guidance༛a101 Health Advisories & Medical Care Guidelines༛ A16 Health Education a102 WeChat Coordination Groups;a103 Public opinion analysis and judgment༛ A17 Public Opinion Monitoring a104 Cumulative Confirmed Cases;a105 Cumulative Deaths༛a106 Case Fatality Rate༛a107 Newly Diagnosed༛a108 New Deaths༛a109 Transmission Intensity༛a110 Transmission Routes༛a111 Source of Infection༛a112 Population Susceptibility༛a113 High-Risk Groups༛ A18 Epidemiological Characteristics a114 Coronavirus Variants;a115 Novel Recombination Virus༛a116 Infectiousness༛a117 Basic Reproduction Number (R0)༛a118 Latency Period༛a119 Serial Interval༛a120 Viral Mutation༛ A19 Virological Characteristics a121 Incubation Period;a122 Common Symptoms༛a123 Prognosis༛a124 Infection Spectrum༛a125 Disease Progression༛a126 Clinical Outcomes༛ A20 Clinical Features a127 RT-PCR Test Kits;a128 Testing Sites༛a129 Specimen Collection and Referral༛a130 Testing Indications༛ A21 Testing a131 Clinical Management;a132 Centralized Treatment༛a133 Critical Case Management༛a134 Medical Treatment Costs༛ A22 Personnel Diagnosis and Treatment a135 Viral Antibodies A23 Population Immunity a136 Psychological Panic;a137 Self-Medication Abuse༛a138 Fraudulent Practices༛a139 Acute Stress Reaction༛a140 Post-Traumatic Stress Disorder (PTSD)༛a141 Disordered Healthcare-Seeking༛a142 Over-the-Counter Drug Stockpiling༛a143 Panic Buying of Supplies༛a144 Public Confidence in Pandemic Response༛ A24 Psychological Crisis a145 Mental Health Hotlines;a146 Establishment of Psychological Crisis Intervention Teams༛ A25 Psychological Counseling a147 Rumor Proliferation;a148 Public Dissatisfaction༛a149 Information Fabrication༛a150 Online Harassment༛a151 Price Gouging༛a152 Sale of Counterfeit/Substandard Goods༛a153 Discrimination Against Patients༛a154 Income Security Concerns༛ A26 Community Disruptions a155 Division of Responsibilities;a156 Contingency Plans༛a157 Early Warning Concepts and Models༛a158 Joint Prevention and Control Mechanism༛a159 Evacuation Protocols A27 Emergency Management Systems a160Patriotic Health Campaign Implementation;a161 Environmental Sanitation Governance༛a162 Promotion of Healthy Lifestyles༛a163 Public Space Disinfection༛a164 Domestic Waste Management A28 Community Environmental Factors a165 Public Compliance;a166 Cultural Differences༛a167 Perceptual Variations༛ A29 Cultural Factors a168 Big Data Analytics;a169 Telemedicine Services༛a170 “Internet Plus” Initiative A30 Technology Applications a171 Penalties for Quarantine Violations;a172 Supervision and Inspection༛ A31 Accountability Mechanisms a173 Epidemic Bulletins;a174 Clinical Treatment Protocols༛a175 Disease Control Strategies༛a176 Technical Guidelines༛a177 Proactive Case Reporting༛a178 Daily Situation Reports༛a179 Information Disclosure Transparency༛ A32 Epidemic Information Disclosure a180 Case Reporting;a181 Reporting Timelines༛a182 Reporting Categories༛a183 “Zero-Reporting” Mechanism༛ A33 Information Reporting a184 Case Surveillance;a185 Epidemic Surveillance༛a186 Outbreak Source Tracing༛a187 High-Risk Site Monitoring༛a188 Enhanced Animal Disease Surveillance༛a189 Sentinel Surveillance Sites༛ a190 Temperature Screening༛a191 Returnee Registration༛a192 Vehicle Registration Tracking༛a193 Real-Time Health Monitoring of High-Risk Groups༛ A34 Epidemic Surveillance a194 Income Reductio;a195 Supply Chain Disruption༛a196 Labor Supply Shortfall༛ A35 Economic Losses A197 Virus Transmission Risk;a198 Epidemic Situation Analysis༛a199 Dynamic Evaluation of Control Measures A36 Risk Assessment a200 Community Health Worker Training;a201 Technical Support Deployment༛a202 Operational Guidance༛a203 Community Leadership Training༛ A37 Personnel Training a204 Full-Scale Disease Response Exercise;a205 Containment Emergency Drill༛ A38 Emergency Drills a206 Resource Allocation Urgency;a207 Community-Level Epidemic Severity A39 Resource Allocation Equity a208 Incubation Stage;a209 Prodromal Stage༛a210 Clinical Manifestation Stage༛a211 Convalescence Stage A40 Disease Progression (2) Axial Coding Based on open coding, and in accordance with the causal conditions, phenomena, action/interaction strategies, and outcomes of the grounded theory paradigm model, initial concepts derived from open coding were integrated into an overarching framework centered on core categories. This process yielded the axial coding for grounded theory research on major emergent respiratory infectious disease outbreaks. Through analyzing the logical relationships among subcategories, 14 main categories were identified. (Table 2 ) Table 2 Results of Axial Coding Main Categories Subcategories Cultural Factors Living habits;Cultural Factors Environmental Factors Community Environmental Factors Disaster-causing substance Virological Characteristics;Epidemiological Characteristics༛Clinical Features Disaster-causing process Disease Progression Human Factors Population Characteristics;Population Mobility Personal Safety Population Immunity;Personnel Diagnosis and Treatment Social Impacts Community Disruptions;Psychological Crisis༛Vulnerable Populations Economic Impacts Economic Losses Technological Factors Technology Applications;Epidemic Surveillance༛Testing Community Governance Community emergency management;Community case management༛Emergency Management Systems༛Psychological Counseling༛Public Opinion Monitoring༛ Resource Allocation Resource Security;Financial Security༛Staff Support and Incentives༛Daily Necessities Supply༛Personal Protective Equipment; Medical Resources༛Community Workers༛Epidemic prevention and control leading group༛Resource Allocation Equity༛ Risk Communication Risk Assessment;Multi-Sectoral Coordination Mechanism༛Epidemic Information Disclosure༛Health Education༛Information Reporting Training and Drills Personnel Training;Emergency Drills Supervise Accountability Mechanisms; (3) Selective Coding As the final stage of grounded theoretical data analysis, the core goal of selective coding is to integrate the developed, elaborated and related categories in spindle coding and build an integrated theoretical framework, which requires further elaboration, integration and verification of spindle coding results. Through this process, this study identified three core categories: disaster-causing scenario, disaster-bearing scenario and disaster-resistant scenario (Table 3 ). Based on the analysis of the data of sudden major respiratory infectious diseases, it is revealed that the community epidemic situation is essentially a dynamic process of the diffusion and development of disaster-causing substances (pathogens) under the action of disaster-causing behaviors (such as population mobility); This process is influenced by many disaster-causing ways (such as cultural/socio-environmental factors), which leads to different development orientations in different disaster-causing processes (that is, epidemic development stages); The deterioration of the epidemic situation will aggravate the destruction of individual safety and social order, and a series of disaster-resistant measures can guide the epidemic situation towards a benign development. Table 3 Results of Selective Coding Core Categories Main Categories disaster-causing scenarios Disaster-causing substance;Human Factors༛Cultural Factors༛Environmental Factors༛Disaster-causing process disaster-bearing scenarios Personal Safety;Social Impacts༛Economic Impacts༛ disaster-resisting scenarios Community Governance;Resource Allocation༛Risk Communication༛Training and Drills༛Technological Factors༛Supervise (4) Theoretical Saturation Testing The criterion for determining theoretical saturation is whether new categories emerge when additional data are incorporated. This study conducted coding analysis on the reserved 30% of raw materials, revealing that no new concepts or categories were generated, and the logical relationships among existing concepts and categories remained consistent. This validation process confirms that the core categories and their corresponding main categories have been fully conceptualized, indicating that the theoretical model has achieved coding saturation. Theoretical Model of Community Scenario Elements for Major Sudden Respiratory Infectious Disease Outbreaks This study utilizes the “Scenario-Response” theoretical framework to identify the patterns and characteristics of major respiratory infectious disease outbreaks in communities and describes a series of scenario elements. After passing the theoretical saturation test, the theoretical model of community scenario elements for major sudden respiratory infectious disease outbreaks was obtained, as shown in Fig. 2 . Following the occurrence of a major respiratory infectious disease outbreak, community scenario information primarily consists of three dimensions: disaster-causing scenarios, disaster-bearing scenarios and disaster-resisting scenarios, encompassing 14 secondary constituent elements. (1) Disaster-Causing Scenario Elements The community disaster-causing scenario for a major sudden respiratory infectious disease outbreak describes the community background environment in which the outbreak occurs, spreads, and causes severe consequences, as well as the evolving process of the outbreak under the coupling influence of various factors. It mainly includes scenario elements such as disaster-causing substance, human factors, cultural factors, environmental factors, and the disaster-causing process. These scenario elements can be further subdivided into secondary constituent elements. These include the disaster-causing substance (such as virological, epidemiological, and clinical characteristics of the disease), human factors (such as population mobility and demographic characteristics), cultural factors (such as lifestyle and customs), environmental factors (such as community environment), and the disaster-causing process encompassing the disease’s occurrence, development, and decline. (2) Disaster-Bearing Scenario Elements The community disaster-bearing scenario for a major sudden respiratory infectious disease outbreak describes the state formed after the outbreak and the consequences it brings to human society. This includes threats to personal safety, economic impact, changes in social/public opinion and social psychological crisis. (3) Disaster-Resisting Scenario Elements The community disaster-resisting scenario for a major sudden respiratory infectious disease outbreak describes the emergency prevention and control measures taken by the community after the outbreak occurs. It is primarily composed of elements such as community management, resource allocation, risk communication, training and drills, technology application, and monitoring/supervision. Discussion Scenario elements are indispensable for describing the state of emergency events, as they provide a clear depiction of their development process, thereby offering robust support for emergency decision-making. Laws and regulations such as the “Law of the People’s Republic of China on the Prevention and Control of Infectious Diseases” and the “Emergency Response Law of the People’s Republic of China” explicitly define the responsibilities of communities in responding to public health emergencies. In the practice of community emergency response to major respiratory infectious disease outbreaks, the appropriate selection of scenario elements across different situations plays a crucial role. This enables graded and categorized precision in prevention and control, empowers community agency in outbreak response, and is essential for effectively containing the spread of the epidemic. Disaster-Causing Scenarios: Foundational Prevention and Risk Identification In disaster-causing scenarios, enhancing community response capabilities should prioritize foundational prevention and risk identification. During the COVID-19 pandemic, multiple countries and regions implemented community symptom surveillance systems, such as: China’s Health Code and Travel Itinerary Card 25 , Community symptom surveillance in Japan and Australia 26 – 28 . These measures provided critical support for pandemic control. Further unleashing community potential to establish an “early detection-rapid containment” defense foundation is essential. Specific strategies include:(1) Deepen disease awareness and strengthen surveillance systems. Communities should actively participate in or support government-established respiratory infectious disease surveillance networks. Through training and collaboration, community workers’ capacity to identify early symptoms should be enhanced. (2) Develop multi-dimensional risk assessment models. Based on disease awareness, communities should collect localized data through questionnaires, resident interviews, etc. A comprehensive analysis of variables including population structure, behavioral patterns, cultural backgrounds, and environmental factors should be conducted to identify community vulnerabilities and perform granular risk assessments. This enables precise targeting of high-risk zones/populations and provides data for differentiated containment strategies. (3) Implement public health education and behavioral interventions. As the frontline of prevention, health education guides residents to adopt healthy habits and reduce transmission risks, which is key to building a collective defense ethos, consistent with Luo Qiangqiang’s research 29 . Practical approaches: Design accessible educational materials (comics, short videos, pamphlets) covering personal hygiene, household prevention, and the importance of vaccination; Mobilize volunteer teams for door-to-door outreach to ensure universal coverage. Disaster-Bearing Scenarios: Emergency Preparedness and Coordinated Response In disaster-bearing scenarios, community response capabilities must advance to the level of emergency preparedness and coordinated response. (1) Build an efficient emergency response framework. Develop detailed and actionable contingency plans based on prior risk assessments, covering medical treatment, material supply, social stability maintenance, etc., ensuring rapid and orderly activation of response mechanisms upon outbreak to effectively curb escalation. (2) Establish and optimize cross-sector collaboration mechanisms. Strengthen communication and coordination among government bodies, medical institutions, research organizations, and social entities, actively exploring pathways for non-health sectors-including businesses, individuals, and social third parties-to engage in emergency management 30 – 31 . This integration ensures all stakeholders swiftly unite to combat the outbreak. (3) Implement a psychosocial intervention system. As highlighted by Choi et al., the COVID-19 pandemic triggered not only a global health crisis but also secondary mental health crises among residents, underscoring urgent needs for psychological support 32 . This view is further substantiated by Salari et al.’s systematic review and meta-analysis 33 . Communities must therefore establish professional psychological intervention teams and service networks to provide counseling and emotional support, enhancing psychological resilience through crisis intervention and mental health guidance-critical steps for alleviating panic, strengthening social cohesion, and maintaining stability. Disaster-Resisting Scenarios: Resilience Building and Continuous Improvement In disaster-resisting scenarios, community response capabilities should focus on resilience building and continuous improvement. (1) Modernization-oriented transformation of community governance capacity. Leverage information technology-such as smart health monitoring systems and telemedicine platforms-to enhance governance efficacy, aligning with implementation strategies from multiple studies 34 – 35 . This approach strengthens self-protection capacity and accelerates emergency response, facilitating swift post-pandemic recovery. (2) Scientific Management of Resource Allocation and Scheduling. Develop evidence-based resource distribution plans according to outbreak dynamics and containment needs. Refine supply chain management and storage efficiency while ensuring governmental coordination to maintain sufficient, affordable essentials (e.g., food, medicine)-critical for preventing market volatility or panic buying caused by information asymmetry and securing long-term pandemic control. (3) Improvement of Continuous Learning and Capacity Building Mechanisms. Conduct regular drills and training to advance staff expertise and emergency competencies, complemented by feedback systems that institutionalize lessons learned from successes and failures, ensuring effective future crisis management. Limitations and Future Work In confronting the complex challenges of major sudden respiratory infectious disease outbreaks, communities-as the frontline defense-directly impact overall containment efficacy. While existing studies have explored critical community response factors from diverse perspectives, none have systematically analyzed community scenario elements through a situational lens, nor comprehensively elucidated their interactions or specific impacts on response capabilities. This study employs grounded theory methodology to investigate community scenario elements during such outbreaks, identifying three dimensions: disaster-causing, disaster-bearing, and disaster-resisting scenarios, and constructs a conceptual model of community scenario elements to inform emergency decision-making theoretically and practically. Key limitations remain: future research must examine how the identified scenario elements can serve as triggers for initiating decision-making processes, thereby activating essential scenario resources in practical applications. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Not applicable. Author Contribution YL and HC wrote the draft of the manuscript and interpreted the results. YL and JF collected and analyzed the data. TZ provided administrative supports, comments, and suggestions in revisions of the paper. All authors have approved the final submitted version. Acknowledgements Not applicable. 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Supplementary Files Table1ResultsofOpencoding.docx Table2ResultsofAxialCoding.docx Table3ResultsofSelectiveCoding.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":265093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrounded theory analysis process.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.Groundedtheoryanalysisprocess.png","url":"https://assets-eu.researchsquare.com/files/rs-8190005/v1/d442085e6f8b8b96de05fabc.png"},{"id":97688911,"identity":"ae4c456f-157b-41dd-8f3f-56a53589a780","added_by":"auto","created_at":"2025-12-08 10:41:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical Model of Community Scenario Elements for Major Sudden Respiratory Infectious Disease Outbreaks.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.TheoreticalModelofCommunityScenarioElementsforMajorSuddenRespiratoryInfectiousDiseaseOutbreaks.png","url":"https://assets-eu.researchsquare.com/files/rs-8190005/v1/7ea890873ab392c072d33a53.png"},{"id":100406254,"identity":"ba63961c-632c-4a54-b445-406fa37061a6","added_by":"auto","created_at":"2026-01-16 12:59:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8190005/v1/63e351dc-ca83-4f06-960e-b240786987e6.pdf"},{"id":97688905,"identity":"a52a8988-5106-45bd-a98c-3e38b8bf9472","added_by":"auto","created_at":"2025-12-08 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15:30:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14720,"visible":true,"origin":"","legend":"","description":"","filename":"Table3ResultsofSelectiveCoding.docx","url":"https://assets-eu.researchsquare.com/files/rs-8190005/v1/5d96438c013ea82306e6a3f4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Based on the “Scenario-Response” Framework: Analysis of Community Scenario Elements in Sudden Major Respiratory Infectious Disease Outbreaks","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the acceleration of globalization in the 21st century, factors such as population growth, global climate change, and ecological degradation have made sudden major respiratory infectious disease outbreaks a critical threat to human health and socioeconomic development. This issue is particularly pronounced in China, given its large population and high mobility. From the 2003 SARS outbreak and the 2009 H1N1 influenza pandemic to the global COVID-19 crisis in 2020, such outbreaks have inflicted substantial economic losses and human casualties on China, exposing multifaceted challenges in prevention and response\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The inherently sudden, complex, and uncertain nature of these diseases significantly diminishes the effectiveness of the traditional \u0026ldquo;prediction-response\u0026rdquo; model. Throughout the lifecycle of an outbreak-from emergence and escalation to gradual subsidence-diverse complex scenarios arise, each exhibiting distinct \u0026ldquo;scenario-dependent\u0026rdquo; characteristics. In response, scholars have progressively developed the \u0026ldquo;scenario-response\u0026rdquo; theoretical framework\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This approach dynamically generates and implements emergency decisions based on continuously evolving scenarios during an event\u0026rsquo;s progression, thereby reducing uncertainties in outbreak management\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Communities, as foundational units of epidemic control, play a pivotal role. They serve not only as a central force for mobilizing public participation but also as the frontline for resource allocation, early warning, and response. Communities possess unique advantages in implementing \u0026ldquo;scenario-response\u0026rdquo; strategies. Consequently, extracting community scenario elements from past major respiratory infectious disease outbreaks provides essential material for constructing potential future scenarios. This enables more comprehensive preparedness and response to protect public health and social stability, carrying significant practical implications.\u003c/p\u003e\u003cp\u003eThe main research contents of this study are as follows: Firstly, based on the grounded theory, the community situational elements of the sudden outbreak of major respiratory infectious diseases in China are coded in three levels. Secondly, the theoretical saturation of the coded data is tested. Thirdly, according to the obtained community situational factors, the theoretical model of community situational factors of sudden major respiratory infectious diseases is constructed. Finally, the implementation path of community response to epidemic situation optimization is put forward.\u003c/p\u003e\u003cp\u003eThe rest of this study is arranged as follows: The second part is literature review. The third part introduces materials and methods, including data sources and grounded theory research methods. The fourth part describes the research results, including the coding of situational elements and the model of situational elements. The fifth part summarizes the full text, puts forward the optimization path of community response to the epidemic situation, expounds the significance of the research, and summarizes the limitations of the paper and the future research direction.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eRespiratory infectious diseases can be divided into two categories according to their epidemic characteristics and influence degree. One is common respiratory infectious diseases, and the course of this kind of disease generally presents obvious seasonal laws, and most of them enter the high-incidence stage in autumn, winter and spring every year. Such as influenza, mumps, hand-foot-mouth disease and tuberculosis. The other is a major emerging acute respiratory infectious disease. Because of its emerging nature, people generally lack immunity, so it is easy to spread widely through social communication networks, thus threatening people\u0026rsquo;s health and causing social panic\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In recent years, SARS, H7N9 avian influenza, novel coronavirus (COVID-19) and other major respiratory infectious diseases have occurred frequently. In this context, accurately predicting the evolution trend of major respiratory infectious diseases can provide more real and effective information support for decision makers, make them carry out risk assessment more scientifically, and then formulate and implement more targeted prevention and control strategies to achieve optimal allocation of emergency resources. The traditional \u0026ldquo;prediction-response\u0026rdquo; emergency decision-making model mainly predicts the evolution track of emergencies and describes its development trend on the basis of continuous integration and updating of information sources\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Although this model can help emergency managers reduce the losses caused by unconventional events with the help of early warning and emergency planning, it is difficult for the \u0026ldquo;prediction-response\u0026rdquo; model to build an accurate model and make effective decisions in the face of highly complex unconventional events and lack of historical experience. In order to effectively solve this problem, the \u0026ldquo;scenario-response\u0026rdquo; emergency decision-making model came into being.\u003c/p\u003e\u003cp\u003eScenario is a collection of many key elements, which is used to describe the possible situation in the future. The core of \u0026ldquo;scenario-response\u0026rdquo; is to make dynamic decisions based on the current development trend of the incident and the possible evolution scenarios in the future. This model effectively makes up for the deficiency of the \u0026ldquo;prediction-response\u0026rdquo; model in accurate prediction, which not only reduces the uncertainty in the emergency response process, but also improves the decision-making efficiency. The scenario construction of sudden major respiratory infectious diseases aims to analyze and describe the potential development path and consequences of the epidemic in advance, and provide key information support for emergency planning and preparation. Therefore, developing the scenario construction of such risks is a key measure to improve the government\u0026rsquo;s ability to cope with risk challenges under uncertain environment and promote the transformation of risk management from passive response to active preparation in advance.\u003c/p\u003e\u003cp\u003eResearch on scenario construction has been explored by scholars worldwide across multiple domains. Domestic studies primarily focus on the localized development and application of theories. Liu Tiemin\u0026rsquo;s proposed \u0026ldquo;scenario-response\u0026rdquo; risk management model emphasizes extracting inherent patterns through summarizing historical precedents\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Scholars such as Wang Yanxin and Rong Juntao have extended its application to emergency frameworks and crisis decision-making, systematically addressing critical issues and demands in China\u0026rsquo;s emergency preparedness practices\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In practical applications, Wang Wenjun conducted scenario analyses focusing on the design, development, and refinement of civil aviation emergency management systems\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Following the \u0026ldquo;scenario-task-capability\u0026rdquo; research paradigm, You Shangyuan constructed an emergency management capability evaluation framework for power system disruptions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Zhang Wei et al. developed risk scenarios for natural disasters like floods and typhoons to enhance the effectiveness and quality of transportation emergency drills\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. International research more broadly integrates modeling and simulation technologies for cross-domain risk analysis. Kappos et al. constructed earthquake disaster scenarios to analyze seismic risk characteristics\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Dettinger et al. designed storm scenarios for emergency preparedness exercises\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, while Manley et al. simulated building environment impacts on crowd evacuation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Post-COVID-19, studies in public health emergencies have surged significantly. Scholars including Ivanov, Karatayev, Iwata, and Van Genugten have developed diverse epidemic or contagion scenarios to assess intervention effectiveness, supply chain disruptions, and outbreak trajectories\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the above research, it can be found that the existing research mainly focuses on natural disasters, safe production, public health and other scenarios, in contrast, the research on the subdivision of sudden major respiratory infectious diseases, especially focusing on the community level, is relatively scarce. Most of the existing achievements focus on the use of quantitative modeling technology to simulate and predict the development track and potential impact of the epidemic under different preset scenarios. However, these scenarios are usually directly set, and there is little in-depth analysis of the construction logic and methods of the scenarios themselves. It is particularly noteworthy that the research on community-oriented systematic scenario construction is still blank. Therefore, based on the \u0026ldquo;scenario-response\u0026rdquo; theoretical paradigm, this study strictly follows the standard process of scenario variable description in scenario construction, aiming at systematically presenting the scenario construction process and its key elements of major respiratory infectious diseases in the community, with a view to supplementing the current situation that academic circles have not paid enough attention to this subdivision.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eData were obtained from official reports, guidance documents, and national/regional case studies on three major respiratory infectious disease outbreaks-Severe Acute Respiratory Syndrome (SARS) in 2003, H7N9 avian influenza in 2013, and COVID-19 in 2019-via the official websites of China\u0026rsquo;s National Health Commission and governmental authorities. A corpus of 1.3643\u0026nbsp;million Chinese characters was extracted, encompassing China\u0026rsquo;s emergency response strategies for sudden major respiratory infectious disease outbreaks. Following thematic screening of this material, 327,600 characters specifically related to community-level outbreaks were selected. Grounded theory coding was subsequently applied to the finalized dataset.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Method\u003c/h3\u003e\n\u003cp\u003eGrounded theory is a qualitative analysis method rooted in original data, whose core lies in conceptualizing and abstracting data to extract core concepts and categories, thereby constructing theories from the bottom up\u003csup\u003e23\u003c/sup\u003e. Its core process includes three levels of coding: open coding, axial coding, and selective coding, which progress from data fragmentation to theoretical integration. In this study, using the qualitative analysis software Nvivo12, the collected and preliminarily sorted data were gradually conceptualized and categorized. To ensure inter-rater reliability and validity, two-person independent coding was adopted; coding discrepancies were resolved through group discussion and literature review. Meanwhile, 30% of the text materials were randomly reserved to test theoretical saturation, i.e., coding continued until no new categories emerged from the reserved data. The specific analysis flow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch3\u003eCoding of Community Scenario Elements for Major Sudden Respiratory Infectious Disease Outbreaks\u003c/h3\u003e\n \u003ch3\u003e(1) Open Coding\u003c/h3\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003cp\u003eAs the initial stage of coding, open coding requires researchers to study the original text deeply and identify and extract the initial concepts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In this study, the original data collected were reviewed first, and then the repeated and invalid contents were eliminated, and then the open coding was implemented. After this process, 211 initial concepts were finally extracted and summarized into 40 categories (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Open coding\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitial concept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategorization\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea1 Population; a2 Population Density; a3 Age Difference; a4 Economic Income Level; a5 Education Level; a6 Geographical Location; a7 Gender Differences; a8 Population Structure; a9 Aging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1 Population characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea10 Returning home during the Spring Festival; a11 Visit relatives and friends during the Spring Festival;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2 Population mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea12 Wash hands before meals;a13 Open windows for ventilation༛a14 Maintain regular daily routines༛a15 Disinfect surfaces༛a16 Practice hygienic dining habits༛a17 Practice safe poultry farming༛a18 Forms of living environment༛a19 Wear face masks properly༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3 Living habits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea20 Closure of public venues;a21 Ban on mass gatherings༛a22 Movement restrictions༛a23 Maintain physical distancing༛a24 Community containment measures༛a25 Cancellation of congregate events༛a26 Road closures༛a27Grid-based management༛a28 Culling of infected poultry༛a29 Live market closures༛a30 Full-chain wildlife trade restrictions༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4 Community emergency management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea31 Isolation;a32 Close contact management༛a33 Suspected case management༛a34 Epidemiological investigation༛a35 Medical observation༛a36 Emergency response level༛a37 \u0026ldquo;Four Earlies\u0026rdquo; ( Early Detection, Early Reporting, Early Isolation, Early Treatment); \u0026ldquo;Four Concentrations\u0026rdquo; (Concentrate Patients, Concentrate Experts, Concentrate Resources, Concentrate Treatment)༛a38 Infection Prevention and Protection༛a39 Fever Clinic༛a40 Pre-examination Triage༛a41 Disinfection and Isolation༛a42 Patient Referral༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5 Community case management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea43 Community Party Branch;a44 Community Residents\u0026rsquo; Committee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6 Epidemic prevention and control leading group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea45 Community Police Officer;a46 Community Property Service Staff༛a47 Community Grid Worker༛a48 Community Resident Representative༛a49 Community Volunteers༛a50 Community Assistant༛a51 Community Health Worker༛a52Epidemiological Investigator༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA7 Community Workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea53 Prevention for Pregnant and Postpartum Women;a54 Infants and Young Children༛a55 Elderly Population༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA8 Vulnerable Populations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea56 Resumption of Work and Production;a57 Material preparation༛a58 Green Channel(Fast-track mechanism for priority handling, e.g., medical supplies)༛a59 E-commerce Supply-Demand Coordination Mechanism༛a60 Daily Necessities Supply Team༛a61 Assessment of Daily Necessities Supply Status༛a62 Material Allocation༛a63 Governor\u0026rsquo;s \u0026ldquo;Rice Bag\u0026rdquo; and Mayor\u0026rsquo;s \u0026ldquo;Vegetable Basket\u0026rdquo; Responsibility System༛a64 Logistics Transfer Station༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA9 Resource Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea65 Medical Insurance Reimbursement;a66 Urban-Rural Medical Assistance Program༛a67 Financial Support༛a68 Epidemic Prevention-Control and Treatment Funding༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA10 Financial Security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea69 Paying attention to the psychological status of staff;a70 Staff Compensation and Benefits༛a71 Community Workforce Management༛a72 Personnel Allocation༛a73 Temporary Work Subsidies༛a74 Health Protection for Community Staff༛a75 Humanistic Care Support༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA11 Staff Support and Incentives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea76 Essential Living Supplies;a77 Supply-Demand Imbalance༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA12 Daily Necessities Supply\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea78 Medical Masks;a79 Personal Protective Equipment༛a80 Alcohol-Based Hand Sanitizer༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA13 Personal Protective Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea81 Protective Coveralls;a82 Protective Goggles༛a83 Protective Goggles༛a84 Medical Supplies༛a85 Ambulances༛a86 Basic Medical Equipment༛a87 Community Hospital Bed Capacity༛a88 Medications༛a89 Medical Consumables༛a90 Vehicle-Mounted Equipment for Professional Teams༛a91 Thermometers༛a92 Vaccines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA14 Medical Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea93 Inter-Departmental Communication on Epidemic Control;a94 Community-Level Coordination༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA15 Multi-Sectoral Coordination Mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea95 Dissemination of Epidemic Prevention Knowledge;a96 Q\u0026amp;A Releases on Epidemic Control༛a97 Public Awareness Campaigns \u0026amp; Advisory Services༛a98 Educational Publications \u0026amp; Booklets༛a99 Interpersonal Health Education Initiatives༛a100 Personal Protection Guidance༛a101 Health Advisories \u0026amp; Medical Care Guidelines༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA16 Health Education\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea102 WeChat Coordination Groups;a103 Public opinion analysis and judgment༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA17 Public Opinion Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea104 Cumulative Confirmed Cases;a105 Cumulative Deaths༛a106 Case Fatality Rate༛a107 Newly Diagnosed༛a108 New Deaths༛a109 Transmission Intensity༛a110 Transmission Routes༛a111 Source of Infection༛a112 Population Susceptibility༛a113 High-Risk Groups༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA18 Epidemiological Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea114 Coronavirus Variants;a115 Novel Recombination Virus༛a116 Infectiousness༛a117 Basic Reproduction Number (R0)༛a118 Latency Period༛a119 Serial Interval༛a120 Viral Mutation༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA19 Virological Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea121 Incubation Period;a122 Common Symptoms༛a123 Prognosis༛a124 Infection Spectrum༛a125 Disease Progression༛a126 Clinical Outcomes༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA20 Clinical Features\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea127 RT-PCR Test Kits;a128 Testing Sites༛a129 Specimen Collection and Referral༛a130 Testing Indications༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA21 Testing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea131 Clinical Management;a132 Centralized Treatment༛a133 Critical Case Management༛a134 Medical Treatment Costs༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA22 Personnel Diagnosis and Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea135 Viral Antibodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA23 Population Immunity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea136 Psychological Panic;a137 Self-Medication Abuse༛a138 Fraudulent Practices༛a139 Acute Stress Reaction༛a140 Post-Traumatic Stress Disorder (PTSD)༛a141 Disordered Healthcare-Seeking༛a142 Over-the-Counter Drug Stockpiling༛a143 Panic Buying of Supplies༛a144 Public Confidence in Pandemic Response༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA24 Psychological Crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea145 Mental Health Hotlines;a146 Establishment of Psychological Crisis Intervention Teams༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA25 Psychological Counseling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea147 Rumor Proliferation;a148 Public Dissatisfaction༛a149 Information Fabrication༛a150 Online Harassment༛a151 Price Gouging༛a152 Sale of Counterfeit/Substandard Goods༛a153 Discrimination Against Patients༛a154 Income Security Concerns༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA26 Community Disruptions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea155 Division of Responsibilities;a156 Contingency Plans༛a157 Early Warning Concepts and Models༛a158 Joint Prevention and Control Mechanism༛a159 Evacuation Protocols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA27 Emergency Management Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea160Patriotic Health Campaign Implementation;a161 Environmental Sanitation Governance༛a162 Promotion of Healthy Lifestyles༛a163 Public Space Disinfection༛a164 Domestic Waste Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA28 Community Environmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea165 Public Compliance;a166 Cultural Differences༛a167 Perceptual Variations༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA29 Cultural Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea168 Big Data Analytics;a169 Telemedicine Services༛a170 \u0026ldquo;Internet Plus\u0026rdquo; Initiative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA30 Technology Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea171 Penalties for Quarantine Violations;a172 Supervision and Inspection༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA31 Accountability Mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea173 Epidemic Bulletins;a174 Clinical Treatment Protocols༛a175 Disease Control Strategies༛a176 Technical Guidelines༛a177 Proactive Case Reporting༛a178 Daily Situation Reports༛a179 Information Disclosure Transparency༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA32 Epidemic Information Disclosure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea180 Case Reporting;a181 Reporting Timelines༛a182 Reporting Categories༛a183 \u0026ldquo;Zero-Reporting\u0026rdquo; Mechanism༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA33 Information Reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea184 Case Surveillance;a185 Epidemic Surveillance༛a186 Outbreak Source Tracing༛a187 High-Risk Site Monitoring༛a188 Enhanced Animal Disease Surveillance༛a189 Sentinel Surveillance Sites༛ a190 Temperature Screening༛a191 Returnee Registration༛a192 Vehicle Registration Tracking༛a193 Real-Time Health Monitoring of High-Risk Groups༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA34 Epidemic Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea194 Income Reductio;a195 Supply Chain Disruption༛a196 Labor Supply Shortfall༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA35 Economic Losses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA197 Virus Transmission Risk;a198 Epidemic Situation Analysis༛a199 Dynamic Evaluation of Control Measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA36 Risk Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea200 Community Health Worker Training;a201 Technical Support Deployment༛a202 Operational Guidance༛a203 Community Leadership Training༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA37 Personnel Training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea204 Full-Scale Disease Response Exercise;a205 Containment Emergency Drill༛\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA38 Emergency Drills\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea206 Resource Allocation Urgency;a207 Community-Level Epidemic Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA39 Resource Allocation Equity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea208 Incubation Stage;a209 Prodromal Stage༛a210 Clinical Manifestation Stage༛a211 Convalescence Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA40 Disease Progression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e(2) Axial Coding\u003c/h3\u003e\n\u003cp\u003eBased on open coding, and in accordance with the causal conditions, phenomena, action/interaction strategies, and outcomes of the grounded theory paradigm model, initial concepts derived from open coding were integrated into an overarching framework centered on core categories. This process yielded the axial coding for grounded theory research on major emergent respiratory infectious disease outbreaks. Through analyzing the logical relationships among subcategories, 14 main categories were identified. (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Axial Coding\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMain Categories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubcategories\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving habits;Cultural Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnvironmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Environmental Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisaster-causing substance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirological Characteristics;Epidemiological Characteristics༛Clinical Features\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisaster-causing process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Progression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Characteristics;Population Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Immunity;Personnel Diagnosis and Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Impacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Disruptions;Psychological Crisis༛Vulnerable Populations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic Impacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic Losses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnological Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnology Applications;Epidemic Surveillance༛Testing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Governance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity emergency management;Community case management༛Emergency Management Systems༛Psychological Counseling༛Public Opinion Monitoring༛\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource Allocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource Security;Financial Security༛Staff Support and Incentives༛Daily Necessities Supply༛Personal Protective Equipment; Medical Resources༛Community Workers༛Epidemic prevention and control leading group༛Resource Allocation Equity༛\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk Assessment;Multi-Sectoral Coordination Mechanism༛Epidemic Information Disclosure༛Health Education༛Information Reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining and Drills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonnel Training;Emergency Drills\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupervise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccountability Mechanisms;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e(3) Selective Coding\u003c/h3\u003e\n\u003cp\u003eAs the final stage of grounded theoretical data analysis, the core goal of selective coding is to integrate the developed, elaborated and related categories in spindle coding and build an integrated theoretical framework, which requires further elaboration, integration and verification of spindle coding results. Through this process, this study identified three core categories: disaster-causing scenario, disaster-bearing scenario and disaster-resistant scenario (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the analysis of the data of sudden major respiratory infectious diseases, it is revealed that the community epidemic situation is essentially a dynamic process of the diffusion and development of disaster-causing substances (pathogens) under the action of disaster-causing behaviors (such as population mobility); This process is influenced by many disaster-causing ways (such as cultural/socio-environmental factors), which leads to different development orientations in different disaster-causing processes (that is, epidemic development stages); The deterioration of the epidemic situation will aggravate the destruction of individual safety and social order, and a series of disaster-resistant measures can guide the epidemic situation towards a benign development.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Selective Coding\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCore Categories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMain Categories\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edisaster-causing scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisaster-causing substance;Human Factors༛Cultural Factors༛Environmental Factors༛Disaster-causing process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edisaster-bearing scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal Safety;Social Impacts༛Economic Impacts༛\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edisaster-resisting scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Governance;Resource Allocation༛Risk Communication༛Training and Drills༛Technological Factors༛Supervise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e(4) Theoretical Saturation Testing\u003c/h2\u003e\n \u003cp\u003eThe criterion for determining theoretical saturation is whether new categories emerge when additional data are incorporated. This study conducted coding analysis on the reserved 30% of raw materials, revealing that no new concepts or categories were generated, and the logical relationships among existing concepts and categories remained consistent. This validation process confirms that the core categories and their corresponding main categories have been fully conceptualized, indicating that the theoretical model has achieved coding saturation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eTheoretical Model of Community Scenario Elements for Major Sudden Respiratory Infectious Disease Outbreaks\u003c/h2\u003e\n \u003cp\u003eThis study utilizes the \u0026ldquo;Scenario-Response\u0026rdquo; theoretical framework to identify the patterns and characteristics of major respiratory infectious disease outbreaks in communities and describes a series of scenario elements. After passing the theoretical saturation test, the theoretical model of community scenario elements for major sudden respiratory infectious disease outbreaks was obtained, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Following the occurrence of a major respiratory infectious disease outbreak, community scenario information primarily consists of three dimensions: disaster-causing scenarios, disaster-bearing scenarios and disaster-resisting scenarios, encompassing 14 secondary constituent elements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e(1) Disaster-Causing Scenario Elements\u003c/h2\u003e\n \u003cp\u003eThe community disaster-causing scenario for a major sudden respiratory infectious disease outbreak describes the community background environment in which the outbreak occurs, spreads, and causes severe consequences, as well as the evolving process of the outbreak under the coupling influence of various factors. It mainly includes scenario elements such as disaster-causing substance, human factors, cultural factors, environmental factors, and the disaster-causing process. These scenario elements can be further subdivided into secondary constituent elements. These include the disaster-causing substance (such as virological, epidemiological, and clinical characteristics of the disease), human factors (such as population mobility and demographic characteristics), cultural factors (such as lifestyle and customs), environmental factors (such as community environment), and the disaster-causing process encompassing the disease\u0026rsquo;s occurrence, development, and decline.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e(2) Disaster-Bearing Scenario Elements\u003c/h2\u003e\n \u003cp\u003eThe community disaster-bearing scenario for a major sudden respiratory infectious disease outbreak describes the state formed after the outbreak and the consequences it brings to human society. This includes threats to personal safety, economic impact, changes in social/public opinion and social psychological crisis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e(3) Disaster-Resisting Scenario Elements\u003c/h2\u003e\n \u003cp\u003eThe community disaster-resisting scenario for a major sudden respiratory infectious disease outbreak describes the emergency prevention and control measures taken by the community after the outbreak occurs. It is primarily composed of elements such as community management, resource allocation, risk communication, training and drills, technology application, and monitoring/supervision.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eScenario elements are indispensable for describing the state of emergency events, as they provide a clear depiction of their development process, thereby offering robust support for emergency decision-making. Laws and regulations such as the \u0026ldquo;Law of the People\u0026rsquo;s Republic of China on the Prevention and Control of Infectious Diseases\u0026rdquo; and the \u0026ldquo;Emergency Response Law of the People\u0026rsquo;s Republic of China\u0026rdquo; explicitly define the responsibilities of communities in responding to public health emergencies. In the practice of community emergency response to major respiratory infectious disease outbreaks, the appropriate selection of scenario elements across different situations plays a crucial role. This enables graded and categorized precision in prevention and control, empowers community agency in outbreak response, and is essential for effectively containing the spread of the epidemic.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDisaster-Causing Scenarios: Foundational Prevention and Risk Identification\u003c/h2\u003e\u003cp\u003eIn disaster-causing scenarios, enhancing community response capabilities should prioritize foundational prevention and risk identification. During the COVID-19 pandemic, multiple countries and regions implemented community symptom surveillance systems, such as: China\u0026rsquo;s Health Code and Travel Itinerary Card\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, Community symptom surveillance in Japan and Australia\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These measures provided critical support for pandemic control. Further unleashing community potential to establish an \u0026ldquo;early detection-rapid containment\u0026rdquo; defense foundation is essential. Specific strategies include:(1) Deepen disease awareness and strengthen surveillance systems. Communities should actively participate in or support government-established respiratory infectious disease surveillance networks. Through training and collaboration, community workers\u0026rsquo; capacity to identify early symptoms should be enhanced. (2) Develop multi-dimensional risk assessment models. Based on disease awareness, communities should collect localized data through questionnaires, resident interviews, etc. A comprehensive analysis of variables including population structure, behavioral patterns, cultural backgrounds, and environmental factors should be conducted to identify community vulnerabilities and perform granular risk assessments. This enables precise targeting of high-risk zones/populations and provides data for differentiated containment strategies. (3) Implement public health education and behavioral interventions. As the frontline of prevention, health education guides residents to adopt healthy habits and reduce transmission risks, which is key to building a collective defense ethos, consistent with Luo Qiangqiang\u0026rsquo;s research\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Practical approaches: Design accessible educational materials (comics, short videos, pamphlets) covering personal hygiene, household prevention, and the importance of vaccination; Mobilize volunteer teams for door-to-door outreach to ensure universal coverage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eDisaster-Bearing Scenarios: Emergency Preparedness and Coordinated Response\u003c/h2\u003e\u003cp\u003eIn disaster-bearing scenarios, community response capabilities must advance to the level of emergency preparedness and coordinated response. (1) Build an efficient emergency response framework. Develop detailed and actionable contingency plans based on prior risk assessments, covering medical treatment, material supply, social stability maintenance, etc., ensuring rapid and orderly activation of response mechanisms upon outbreak to effectively curb escalation. (2) Establish and optimize cross-sector collaboration mechanisms. Strengthen communication and coordination among government bodies, medical institutions, research organizations, and social entities, actively exploring pathways for non-health sectors-including businesses, individuals, and social third parties-to engage in emergency management\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This integration ensures all stakeholders swiftly unite to combat the outbreak. (3) Implement a psychosocial intervention system. As highlighted by Choi et al., the COVID-19 pandemic triggered not only a global health crisis but also secondary mental health crises among residents, underscoring urgent needs for psychological support\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This view is further substantiated by Salari et al.\u0026rsquo;s systematic review and meta-analysis\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Communities must therefore establish professional psychological intervention teams and service networks to provide counseling and emotional support, enhancing psychological resilience through crisis intervention and mental health guidance-critical steps for alleviating panic, strengthening social cohesion, and maintaining stability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDisaster-Resisting Scenarios: Resilience Building and Continuous Improvement\u003c/h2\u003e\u003cp\u003eIn disaster-resisting scenarios, community response capabilities should focus on resilience building and continuous improvement. (1) Modernization-oriented transformation of community governance capacity. Leverage information technology-such as smart health monitoring systems and telemedicine platforms-to enhance governance efficacy, aligning with implementation strategies from multiple studies\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This approach strengthens self-protection capacity and accelerates emergency response, facilitating swift post-pandemic recovery. (2) Scientific Management of Resource Allocation and Scheduling. Develop evidence-based resource distribution plans according to outbreak dynamics and containment needs. Refine supply chain management and storage efficiency while ensuring governmental coordination to maintain sufficient, affordable essentials (e.g., food, medicine)-critical for preventing market volatility or panic buying caused by information asymmetry and securing long-term pandemic control. (3) Improvement of Continuous Learning and Capacity Building Mechanisms. Conduct regular drills and training to advance staff expertise and emergency competencies, complemented by feedback systems that institutionalize lessons learned from successes and failures, ensuring effective future crisis management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Work\u003c/h2\u003e\u003cp\u003eIn confronting the complex challenges of major sudden respiratory infectious disease outbreaks, communities-as the frontline defense-directly impact overall containment efficacy. While existing studies have explored critical community response factors from diverse perspectives, none have systematically analyzed community scenario elements through a situational lens, nor comprehensively elucidated their interactions or specific impacts on response capabilities. This study employs grounded theory methodology to investigate community scenario elements during such outbreaks, identifying three dimensions: disaster-causing, disaster-bearing, and disaster-resisting scenarios, and constructs a conceptual model of community scenario elements to inform emergency decision-making theoretically and practically. Key limitations remain: future research must examine how the identified scenario elements can serve as triggers for initiating decision-making processes, thereby activating essential scenario resources in practical applications.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYL and HC wrote the draft of the manuscript and interpreted the results. YL and JF collected and analyzed the data. TZ provided administrative supports, comments, and suggestions in revisions of the paper. All authors have approved the final submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the first author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhuang QW. Research on Optimization of Complete Vehicle Logistics Service Network under Public Health Emergencies[D]. Beijing Jiaotong University; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinistry of Health. Situation of National Influenza A Prevention and Control Work in December 2009[EB/OL]. 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Chin Health Service Manage. 2022;39(06):471\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sudden major respiratory infectious disease, Scenario-response, Grounded theory, Community","lastPublishedDoi":"10.21203/rs.3.rs-8190005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8190005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn recent years, frequent outbreaks of major respiratory infectious diseases (such as Ebola and COVID-19) have posed severe threats to public health and social stability. As the fundamental unit and frontline of epidemic prevention and control, the response capability of communities directly impacts the overall effectiveness of outbreak containment. The extraction of scenario elements can fully analyze the composition and evolution of epidemic scenarios, while the traditional \"prediction-response\" paradigm often struggles to accurately match the complex and evolving epidemic situations. Therefore, introducing the \u0026ldquo;scenario-response\u0026rdquo; paradigm to deeply analyze key scenario elements in community-level epidemic evolution and construct effective response mechanisms has become an urgent imperative for enhancing precision prevention and control capabilities at the grassroots level and strengthening the first line of defense.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBased on the \u0026ldquo;scenario-response\u0026rdquo; paradigm, this study employed the grounded theory method to systematically review reports, documents, and cases related to sudden major respiratory infectious disease outbreaks, extracting community scenario elements.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThrough qualitative analysis of the initial coded data (yielding 211 concepts), a theoretical model of community scenario elements for sudden major respiratory infectious disease outbreaks in China was established, comprising 40 categories, 14 main categories, and 3 core categories (disaster-causing scenarios, disaster-bearing scenarios, disaster-resisting scenarios).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eBy analyzing community epidemic scenario elements from a situational perspective, this study further proposes response mechanisms for communities confronting sudden major respiratory infectious disease outbreaks, providing theoretical support and practical guidance for emergency decision-making.\u003c/p\u003e","manuscriptTitle":"Based on the “Scenario-Response” Framework: Analysis of Community Scenario Elements in Sudden Major Respiratory Infectious Disease Outbreaks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:41:27","doi":"10.21203/rs.3.rs-8190005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66952150-838b-4b91-85b9-e0e3077339d7","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T17:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:41:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8190005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8190005","identity":"rs-8190005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}