2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper
2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper
2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper
2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
2 Theoretical Foundations 2.1 Disaster System Theory Disaster system theory, grounded in systems science, conceptualizes disasters as complex, dynamic entities characterized by integrality and systematicity (Shi et al., 2020). This study adopts the Environment (E) - Hazard (H) - Subject (S) model (Fig. 2a), which identifies disaster events as the synergistic outcome of interactions between natural environmental conditions, physical hazards, and the affected subjects (Wang et al., 2024b). Historically, the evaluation of disaster risk has relied upon the HEV (Hazard-Exposure-Vulnerablility) model (Fig. 2b). In this context, hazard characterizes the intrinsic properties of the climate event, such as frequency and intensity. Exposure reflects the degree to which populations and assets are situated in hazard-prone regions. Vulnerability denotes the susceptibility to damage shaped by socioeconomic fragilities (D'Ambrosio et al., 2023; Rising et al., 2022). A pivotal advancement in recent risk management scholarship is the extension of this model into the HEVR (Four-element) framework, which introduces response capacity as a critical fourth dimension (Fig. 2c) (Ayanlade et al., 2023; Simpson et al., 2021). Fig.2 Disaster system theory: (a) disaster formation model, (b) three elements of risk and (c) four elements of risk. 2.2 Disaster Risk Management Building upon the HEVR theory, this study proposes a comprehensive governance framework that facilitates a paradigm shift from static risk description to mechanism-oriented explanation (Ayanlade et al., 2023). This framework operates across two synergistic dimensions: spatial and temporal. For the spatial dimension, this study introduces a typological zoning approach to move beyond conventional risk mapping (Fig.3 a). By transitioning from basic risk assessment to the classification of regions into distinct risk-dominant types, the framework pinpoints the specific structural weaknesses of different areas, thereby enabling the formulation of targeted, precision-based interventions. Temporally, the governance of climate risk is conceptualized through a continuous disaster full-cycle management framework (Fig. 3b). This dynamic, closed-loop system spans the entire disaster timeline through four interdependent phases: Prevent and Prepare (pre-disaster), Response (mid-disaster), and Recover (post-disaster). Post-disaster recovery efforts continuously inform and enhance proactive prevention and preparedness, systematically reshaping the future risk landscape. By integrating this dual-dimensional framework with interpretable machine learning, this study contributes a scalable theoretical foundation for systematic risk governance. It transforms abstract climate variables into actionable parameters, empowering policymakers to optimize resource allocation and strengthen long-term resilience against the escalating threats of extreme heat. Fig.3 Disaster risk management framework: (a) risk assessment and typological zoning and (b) disaster full-cycle framework.
07
Hero
storm, typhon, rock fill river, litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
1109
Openjourney
draw this model: Three-Layered Model of the U.S. Healthcare System's Response to COVID-19 Structure of the Model Three Layers: Layer 1: Individual Level (Micro) Layer 2: Organizational Level (Meso) Layer 3: Societal Level (Macro) Dominant System Based on Adhocracy: Indicate how the adhocracy model influences which system (rational, natural, or open) becomes dominant at each layer. Model Description Layer 1: Individual Level (Micro) Characteristics: Focus on individual healthcare workers, patients, and community members. Dominant System: Natural System Examples: Healthcare Workers: Nurses and doctors adapting their roles to meet the urgent needs of patients, often working overtime and forming informal support networks. Patient Behavior: Individuals seeking information about COVID-19 through social media and community resources, demonstrating diverse motivations and loyalties to different health messages. Layer 2: Organizational Level (Meso) Characteristics: Focus on healthcare organizations, hospitals, and public health agencies. Dominant System: Open System Examples: Testing and Vaccination Sites: Hospitals and clinics collaborating with local health departments to set up drive-through testing and vaccination clinics, responding to community needs and external pressures. Data Sharing: Organizations sharing data on infection rates and vaccination progress with public health authorities to adapt strategies in real-time, illustrating the interconnectedness with the environment. Layer 3: Societal Level (Macro) Characteristics: Focus on the broader healthcare system, policies, and societal impacts. Dominant System: Rational System Examples: Policy Implementation: The federal government implementing structured policies like the CARES Act to provide funding for healthcare facilities and support for individuals affected by the pandemic. Regulatory Frameworks: Establishing guidelines for healthcare practices, such as mask mandates and social distancing protocols, to ensure public safety and accountability. Visual Representation To create the visual model: Draw three horizontal layers stacked on top of each other, labeled as Individual Level, Organizational Level, and Societal Level. In each layer, include a brief description of the characteristics and the dominant system, along with the specific examples provided. Use arrows or lines to indicate the influence of the adhocracy model on the dominant system at each layer. Consider using different colors or shapes to represent each system (rational, natural, open) for clarity. Summary This model illustrates how the U.S. healthcare system's response to COVID-19 can be understood as a three-layered system, with the dominant perspective shifting based on the adhocracy model. The natural system perspective is prominent at the individual level, the open system perspective at the organizational level, and the rational system perspective at the societal level. This layered approach highlights the complexity and dynamism of the healthcare response during the pandemic.
095
DreamShaper
storm,typhon,rock fill river,litter,rianing,rapid river water,Seawater backflow, natural disaster , flood ,landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, inspired by in the style of disney animation, Wide Angle, 360 Panorama, high detail.
063
DreamShaper
storm,typhon,rock fill river,litter, natural disaster in forest, flood landslide, no human characters, Showing the disharmony of nature, crash trees, intricate detail, and no defects, 3d rendering, front view, emphasize textures that rough-textured colored paper, masterpiece, handmade
056
DreamShaper