Emergency Management Operations Research Articles

Assessing volcanic hazard and exposure in a data poor context: Case study for Ethiopia, Kenya, and Cabo Verde

Volcanoes produce a wide variety of hazards across varying spatial and temporal scales. When data are scarce on past eruptions and hazards, it can falsely imply low hazard recurrence and create challenges for robust hazard and risk assessment. Data quality and quantity vary considerably across different regions, volcanoes, and eruptions. Yet, there is a need for regional to global scale information on volcanic hazard and risk, where consistent and reproducible methods are applied. Such information is used by international stakeholders to inform funding priorities, risk reduction policies, and to highlight data and knowledge gaps, contributing towards the Sendai Framework's Sustainable Development Goals. Challenges in gathering this information can be most problematic where large populations are exposed to potential volcanic hazards but there are few comprehensive eruptive histories, as in sub-Saharan Africa. Here, we present a unique study to evaluate hazard and exposure for nine volcanoes in Ethiopia, Kenya and Cabo Verde, as part of an international project to develop multi-hazard Disaster Risk Country Profiles. We applied a two-stage expert elicitation process to volcanoes for the first time, and coupled the results with vent mapping, numerical hazard modelling, and GIS analysis of eight exposure categories to identify where high volcanic hazard and exposure coincide. Testing the sensitivity of our findings to input assumptions, to better understand where uncertainties lay, showed that improving our knowledge of past eruption volumes, frequencies, and dates was key to reducing uncertainty. Expert elicitations proposed that Fogo, Cabo Verde, is the most likely to erupt (eruption on average every 25 years), while Fentale (Ethiopia), Longonot and Suswa (Kenya) were elicited to have the greatest probability for a large explosive (VEI ≥ 4) eruption (on average every 400 years). Menengai and Longonot produce the larger exposure values across most VEI scenarios and categories of exposure, but population and GDP exposure was also large for more distal tephra fall and flows at Corbetti and Suswa, with order of magnitude increases expected between 2010 estimates and 2050 projections. Potentially high impact scenarios include tephra being dispersed across large cities (e.g. Nairobi, 55 km from Suswa) and key infrastructure (e.g. geothermal power station ∼2.5 km from Aluto), as well as important tourist destinations, seats of government and emergency management operations (e.g. islands east of Fogo). This study provided the first hazard and exposure assessment of its kind for these volcanoes and drew attention to volcanic risk at the levels required to inform policy and future in-country funding opportunities.

An operator’s guide to SPAR(CD): a model to support decision-making

In order to explore the commonalities and differences in decision-making across emergency services organisations, a systematic literature review of over 10,000 peer reviewed English-language studies was undertaken looking at sense-making and critical decision-making in police, military, ambulance and firefighting contexts. The insights gained and lessons learnt from this research enabled the development of the Situation, Context, Decision, Plan, Act, Review or S(CD)PAR framework. The new framework is hazard and service agnostic, not only incorporating complexity analysis and contextual factors but also being applicable to the full spectrum of emergency management operations. This paper presents the operational translation of the theoretical S(CD)PAR framework into a multi-sector end-user decision SPAR(CD) model that can guide operational decision-making as well as the development of policy, procedures and learning and assessment tools. While the S(CD)PAR framework and SPAR(CD) model have been developed based on research in high-consequence and low-time emergency services and military environments, they are equally applicable in other environments including business contexts and boardrooms wherever decisions are made. This research and subsequent model are important as it supports a common approach to decision-making and also provides a foundation for teaching and assessing evidence-based decision-making across multiple contexts.

Towards a common framework to support decision‐making in high‐risk, low‐time environments

AbstractIn a recently published study, we completed a systematic literature review of 10,084 peer reviewed English language studies since 2000 within the context of threat assessment, sense making, and critical decision‐making in police, military, ambulance, and firefighting contexts. The results of our descriptive analysis found that although research into threat assessment and decision‐making has been historically ‘siloed’ from a service context perspective there are nonetheless some common elements that have either independently identified or in some instances coevolved. The insights gained and lessons learned from this vast data pool enabled the enhancement of the urban fire service operational response‐based SPAR decision framework into the Situation, Context, Decision, Plan, Act, Review or SPAR (CD) framework, a true interoperable all hazards and all services decision‐making framework that not only incorporates complexity analysis and contextual factors but is applicable to the full spectrum of emergency management operations. It is hoped that the application of a common framework may facilitate cross industry learnings by applying a more consistent lens for the review of high‐risk, low‐time decisions and the identification of common decision errors and effectiveness (and measurement) of current strategies to improve decision‐making including learning and development and policy and procedure design and implementation. While the SPAR (CD) framework has been developed based on research in high consequence and low time emergency service and military environments, we posit it is equally as applicable in other environments including business contexts and board rooms wherever decisions are required.

Analysis of SAR-derived products to support emergency management during volcanic crisis: La Palma case study

This paper analyses the performance of SAR-based results to support emergency management operations during the volcanic crisis of La Palma island. Tajogaite eruption began on September 19th 2021 affecting a high populated area and the surrounding farmland, used for different agricultural purposes. SAR datasets obtained from Sentinel-1, COSMO-SkyMED, PAZ-TerraSAR-X and Capella satellites, were processed to calculate amplitude, coherence and differential interferograms. These results have permitted to map the morphology of the volcanic cone, the evolution of lava flows and the regional displacements. The comparison of the results obtained with the different satellites has been useful to understand their performance during an emergency scenario triggered by a volcanic eruption. COSMO-SkyMED has provided the most useful information for the analysis of the cone morphology and the lava flows. Sentinel-1 showed the best performance for regional displacements monitoring, whereas PAZ results was the second best option for all the retrieved products.

MOBILE MAPPING SOLUTIONS FOR THE UPDATE AND MANAGEMENT OF TRAFFIC SIGNS IN A ROAD CADASTRE FREE OPEN-SOURCE GIS ARCHITECTURE

Abstract. The adoption of open-source mobile mapping applications in public administration has risen over the last decade due to their interdisciplinary vocation and flexibility in adapting to existing Geographic Information System (GIS) software architectures. This facilitates complex procedures of data collection and management required for transportation and environmental models, emergency management, and maintenance operations. The Ministry of Infrastructure and Transport in Italy requires road-owning agencies to build and maintain a mapping inventory of their road networks, including georeferenced information about streets and ancillary elements, such as traffic signs. Innovative and integrated street-level approaches for the rapid mapping of road entities using open-source mobile mapping tools represent a valuable low-cost solution for the periodical update of road entities inventory. The adoption of these tools allows public administrators to easily consult the road inventory even outside the office, conducting in situ validation and quality evaluation. This work presents a case study focused on the update and management of traffic sign entities of the Road Cadastre of the Province of Piacenza (Italy) using Qfield and Open Data Kit (ODK) Collect as alternatives to the previous traditional survey method that consisted in the use of field papers. A comparison between the adoption of the two mobile apps is conducted, identifying benefits and limitations in terms of both data accuracy and usability. Validation scripts, project and form structure were developed with the perspective of making the entire workflow as reproducible and transparent as possible, sharing details in a dedicated GitHub repository.

Unmanned Aircraft Systems (UAS): Applications and Integration into Hazard Mitigation Planning

Abstract Unmanned Aircraft Systems (UAS) (also referred to as Unmanned Aerial Systems (UAS), Unmanned Autonomous Vehicles (UAVs), or drones) operations, focused on natural hazards, have experienced rapid expansion in the last decade. UAS uses before, during, and after natural hazard events, provide value for emergency management operations (e.g. Search-and-Rescue (SAR)), and post-event analytics. The Department of Homeland Security and Emergency Services (DHSES) manages UAS programs for public safety and emergency response activities in New York State. They also have the first FEMA-approved, locally adopted, web-based, interactive Hazard Mitigation Plans (HMPs). With recent advances in communication technologies (e.g. 5G), opportunities are emerging to establish a stewardship role to maximize regionwide UAS operations, including preparing for catastrophic natural hazards (e.g. earthquakes, hurricanes), leveraging existing HMPs, and incorporating new machine-learning techniques to use swarming networks before, during, and after a natural hazard event. A variety of stewardship approaches are discussed.

Knowledge to action: Resilience planning among state and local transportation agencies in the United States.

The results of a nationwide survey of state and local agencies conducted in 2020 are summarized to understand priorities for improving transportation emergency management (EM) and resilience. Questions on EM activities, policies, and operations were answered by 190 respondents from across the country. In addition to comparing responses across transportation, EM, and other organizations (law enforcement, public works, health, environmental services, etc.), differences are analyzed based on agency size, location, experience, and hazard exposure. Respondents were also asked to prioritize EM activities for additional funding. Capabilities such as Emergency Planning, Traffic Control, Response Teams, and Situational Awareness emerge as priority areas. Approximately 60 percent of transportation agencies report updates of EM plans within the last 5 years compared to 70 percent of EM and 38 percent among other agencies. Four general linear models (GLMs) are developed and tested to determine the attributes of respondents who support improvements in (1) planning, (2) communications, (3) collaboration, and (4) transformation of agencies. An evaluative framework for measuring and managing resilience is developed and tested based on the responses. The analysis reveals diverse EM experiences, capabilities, and priorities, providing insight into future directions for sustaining and improving transportation resilience.

Machine Learning for Emergency Management: A Survey and Future Outlook

Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.

COVID-19 Response Roles among CDC International Public Health Emergency Management Fellowship Graduates

Since 2013, the US Centers for Disease Control and Prevention has offered the Public Health Emergency Management Fellowship to health professionals from around the world. The goal of this program is to build an international workforce to establish public health emergency management programs and operations centers in participating countries. In March 2021, all 141 graduates of the fellowship program were invited to complete a web survey designed to examine their job roles and functions, assess their contributions to their country's COVID-19 response, and identify needs for technical assistance to strengthen national preparedness and response systems. Of 141 fellows, 89 successfully completed the survey. Findings showed that fellowship graduates served key roles in COVID-19 response in many countries, used skills they gained from the fellowship, and desired continuing engagement between the Centers for Disease Control and Prevention and fellowship alumni to strengthen the community of practice for international public health emergency management.

Augmentation of a Hospital Incident Command System to Support Continued Waves of the COVID-19 Pandemic.

Stamford Hospital (SH) is a 305 bed Level II Trauma center located in Fairfield County, the epicenter of the coronavirus disease 2019 (COVID-19) pandemic in Connecticut. The COVID-19 pandemic was a fast paced, rapidly evolving crisis, presenting our leadership team with unique challenges related to resource availability, patient care, and staff safety. The existing Hospital Incident Command System (HICS) was activated in March 2020 to coordinate our operational emergency management planning, response, and recovery capability for this unplanned event. Although HICS lays the groundwork for hospital preparations and protocol implementation, it is not designed to withstand prolonged crisis circumstances. Given the conditions that the COVID-19 pandemic presented, the possibility for future waves became an impending reality, prompting the need for a long-term solution. To establish guidelines that promoted balance between necessary preparations in the case of additional surges of the pandemic and concurrent resumption then maintenance of routine hospital operations, the SH COVID-19 Playbook was created. The Playbook, presented here, is arranged in accordance with the evidence-based 4 S's (Space, Staff, Systems, and Stuff) strategic critical care planning framework, to address surge capacity management within our hospital's four main patient care areas and additional supportive services. Through feedback from frontline caregivers and leaders within SH, the Playbook captures our experience, best practices, and insight acquired during the first wave of the pandemic. Established with the intentions of equipping leadership and staff globally, guidelines are presented to aid in the navigation of future pandemic surges and successfully care for COVID-19 patients, ensure staff safety, allow for normal services to operate, and provide optimal communication and support for the community, patients, and staff.

Toward Next‐Generation Lava Flow Forecasting: Development of a Fast, Physics‐Based Lava Propagation Model

AbstractDuring effusive volcanic crises, the eruption and propagation of lava flows pose a significant hazard to nearby populations, homes, and infrastructure. Consequently, timely lava flow forecasts are a critical need for volcano observatory and emergency management operations. Previous lava flow modeling tools are typically either too slow to produce timely forecasts, or are fast, but lack critical aspects of lava physics or important forecasting outputs. In particular, the strong thermal stratification present in laminar, high‐Prandtl number flows has generally been neglected. Bulk rheological changes have previously been computed from cell‐averaged temperatures, assuming that the flow is thermally mixed. Here, we detail the development and initial testing of Lava2d, a new two‐dimensional depth‐averaged finite volume model of lava flow propagation over natural terrain which accounts for bulk rheological changes due to thermorheological stratification. We use a novel approach to energy conservation based on tracking cooling and solidifying at the flow base and at the moving flow surface, allowing for the estimation of more realistic vertical thermorheological profiles, while maintaining computational efficiency, producing very timely model runs. We validate our approach with three examples: comparison with theoretical propagation of crust‐dominated lava flows, comparison with a large‐scale molten basalt experiment from the Syracuse University Lava Project, and efficiency testing and comparison with the initial phase of the 1984 Mauna Loa lava flows. Our model is shown to produce rapid, realistic forecasts, making it a good candidate for operationalization in active volcanic regions such as in Hawai'i.

Quantifying the value of SHM information for bridges under flood-induced scour

Bridge scour is a leading cause of failure for bridges over waterways and is notoriously difficult to detect with accuracy. Dynamic Structural Health Monitoring (SHM) of bridges for scour has gained traction in recent years as monitoring systems have improved and the reliability of measurements increased. Due to the large number of bridges on typical networks and the limited financial resources within asset management agencies, decision-makers must prioritize certain structures when it comes to management in the event of flooding. The decision to install a dynamic SHM system on a bridge must be balanced by the financial benefit of doing so, as limited resources often need to be carefully rationed. In this paper, a methodology is proposed to evaluate such benefit based on the Value of Information (VoI) from Bayesian decision analysis. A case study is presented whereby a dynamic SHM system is considered to be installed on a typical bridge with the aim to support emergency management operations during flooding. The proposed methodology allows computation of the financial benefit of installing a dynamic SHM system over a certain reference period, thus accounting for multiple flood events and scenarios. The various elements of the procedure are discussed in detail.

AI (artificial intelligence)-assisted planning within emergency management operations

There is a demand for future technologies to be embedded within emergency management operations. Artificial intelligence (AI) can help save lives and create more efficient systems for emergency management operators to prepare, design, develop, and execute responses to disasters and catastrophes. This study intends to provide insight into how AI can integrate with climate modeling and traffic management systems in response to natural disasters. Research with supporting evidence implies that current technology and frameworks can coexist inside the existing infrastructure and emergency management operations. A growing population with an increase in anthropogenic emissions and the inability to predict future disasters and catastrophes suggests that AI can help address these challenges.

COVID-19 and Local Emergency Management Operations

As the COVID-19 pandemic spread across the globe, local (county and municipal) emergency management departments in the United States were thrust to the forefront in addressing the needs of its citizenry. Doing so likely involved the use of Comprehensive Emergency Management (CEM) principles and planning efforts to address the pandemic. This paper outlines how respondents to a May/June 2020 survey of lead local emergency management department professionals nationwide used a variety of tools to address the evolving demands of the pandemic. The results suggest that a majority of local departments report having preparation and response means designed to address a pandemic in place prior to the outbreak of COVID- 19, but the use of specific efforts to confront the virus varied from place to place, as do attitudes regarding the impact of the pandemic on the daily operations of the local emergency management agency.

Operation LENTUS and You: An Emergency Manager’s Guide to the CAF


 
 As the scale and frequency of natural disasters and other emergencies continues to rise in Canada, the Canadian Armed Forces (CAF) has increasingly taken part in domestic disaster assistance operations. Operation LENTUS, being the CAF’s name for all domestic natural disaster assistance operations, has seen the deployment of thousands of CAF personnel over recent years. The same is true for Operation LASER and VECTOR—the CAF’s operations in support of COVID-19 mitigation and vaccination efforts respectively. As such, emergency management (EM) practitioners are increasingly interacting with CAF personnel, both in headquarters environments for operational planning as well as in field conditions during the execution of specific EM tasks. Despite this, and by no fault of their own, many EM practitioners are unfamiliar with the CAF and are subsequently unsure how best to integrate CAF resources into their operations. Due to the complex nature of interagency EM operations, fostering mutual understanding and awareness is crucial to conducting effective EM operations. As such, this paper seeks to bridge this gap in the general knowledge of emergency managers towards their CAF partners during domestic operations.
 In doing so, this paper will explain and discuss various aspects of the CAF in domestic operations across the complete spectrum of operations, from legislative/strategic considerations to aspects of local execution of EM tasks. These explanations will serve as a starting point for emergency managers to improve their understanding of the CAF and guide their considerations when working in partnership with the CAF. It is hoped that bridging this gap will improve the operational effectiveness of CAF-Civil authority interagency operations and subsequently benefit Canadians in their times of need.
 

The application of cascading consequences for emergency management operations

The application of cascading consequences for emergency management operations

Nurse Leaders' Knowledge and Confidence Managing Disasters in the Acute Care Setting.

Whether natural or human-induced, disasters are a global issue that impact health care systems' operations, especially in the acute care setting. The current COVID-19 pandemic is a recent illustration of how health care systems and providers, especially nurses, respond to a rapidly evolving crisis. Nurse leaders in the acute care setting are pivotal in responding to the multifactorial challenges caused by a disaster. A quality improvement project was developed to increase nurse leaders' knowledge and confidence in disaster management during the COVID-19 pandemic at 2 Magnet-designated acute care hospitals within the John Muir Health system in Northern California. A total of 50 nurse leaders initially participated in this project, with 33 participants completing the postintervention survey. Results indicated significant improvement in perceived knowledge and confidence in disaster management after the intervention. Qualitative responses from project participants highlighted the need to annualize educational opportunities to sustain knowledge and consistently review emergency management operations plans. This quality improvement project provided an approach to educating nurse leaders in disaster management to promote resilience, support of employees, and optimal patient outcomes during disasters.

TOWARDS SEAMLESS INDOOR-OUTDOOR POSITIONING: THE IOPES PROJECT APPROACH

Abstract. The management of emergencies require the use of multiple resources that must be coordinated to achieve the best possible results. For a good decision-making process, the availability of timely and reliable information about the variables on which such process rely is crucial. Among these variables, the ability to track the position in the field - either outdoors or indoors - of the members of the emergency teams it is of special importance. The IOPES project targets at improving an existing, already operational emergency management system where the tracking of operative staff is integrated. This paper concentrates only in the positioning aspect of IOPES - which encompasses other subsystems, such as portable communications or fast mapping - and describes the approach adopted by the project to perform such integration. This includes the concept itself, the hardware selected and well as the algorithms used to implement a portable, lightweight positioning device able to provide seamless indoor / outdoor positioning that will make possible the real-time tracking of personnel in the field. Promising preliminary results for mixed indoor-outdoor trajectories are as well presented.

A New Information System for the Management of Non-Epidemic Veterinary Emergencies.

Simple SummaryVeterinary services, along with public health professionals, can be involved in major adverse events resulting from processes of the Earth striking both humans and animals, so-called non-epidemic emergencies. A dedicated information system able to collect specific data and tprovide information to stakeholders is a pre-requisite for the management of these events. The Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale”, home of the National Reference Centre for Urban Hygiene and Non-Epidemic Emergencies (IUVENE), has been entrusted with the development of a system that provides different veterinary operational levels with a spatial and decisional tool to assist them in cases of natural disasters. This kind of decision support system is still missing at a national level. The present paper describes the implementation of the Veterinary Information System for Non-Epidemic Emergencies (SIVENE), an innovative, flexible, and user-friendly system and modular tool including a database, web application, mobile app, and Web Geographic Information Systems (GIS) component. SIVENE provides National Veterinary Services (local health units and national and regional veterinary services) with an emergency management tool responding to the needs identified in the context of catastrophic events, thanks to the data maintained within its database and converted into real time information.The Italian National Veterinary Services, public health professionals, and policy makers are asked to participate at different levels in the decision-making process for the management of non-epidemic emergencies. A decision support system offering the different administrative and operational emergency management levels with a spatial and decisional tool to be used in the case of natural disasters is still missing at the national level. Within this context, the Italian General Directorate for Animal Health of the Ministry of Health funded a research project for the implementation of a new Veterinary Information System for Non-Epidemic Emergencies (SIVENE), an innovative real-time decision support tool for emergency response in a disaster management scenario. SIVENE was developed according to a multi-layer architecture with four integrated components: the database layer, which was implemented by an RDBMS Oracle 11 g; the ReST service layer, which was created using J2EE, Spring, and MyBatis technologies; the web application (business framework and user interface), which was developed in Angular4 framework using TypeScript language; and the web Geographic Information Systems (GIS), which was realized through the implementation of a geodatabase in Oracle RDBMS 11 g. This system allows us to build up and dynamically create a set of dedicated checklists to be used in the field when gathering the information needed for the management of non-epidemic emergencies; employ the application on mobile devices, such as tablets and smartphones; and use the web GIS to manage and visualize data of veterinary interest and territorial maps of risk and damage.

Testing structural and relational embeddedness in collaboration risk

The study investigates the effect of embeddedness, defined as a property of interdependent relations in which organizations are integrated in a network, on collaboration risk emerging from relational uncertainty. Despite efforts to understand the structural effects of network governance, embedded relationships and their influence on collaboration remain relatively unexplored. A case of intergovernmental collaboration for emergency management is used as a test bed to examine the role of embeddedness in disaster networks and to extend the knowledge of collaboration risk within the institutional collective action framework. We hypothesize and test the effect of relational and structural embeddedness on the level of collaboration risk that an organization perceives. Our analysis of 69 organizations engaged in emergency management operations in the Seoul Metropolitan Area, South Korea reveals that both structural and relational embeddedness facilitate organizations to mitigate perceived collaboration risk. The results suggest that reachability secures relief of relational risk, and that commitment relationships bind participants.