Emergency Resources Research Articles

Application of SARIMA Model and Simple Seasonal Exponential Smoothing on Diabetes Mellitus: A Case of Enugu State Teaching Hospital, Nigeria

This paper aims at obtaining better model between seasonal ARIMA and simple seasonal exponential smoothing that will be used for forecasting number of diabetes patients in a given hospital. Monthly dataset from January 2009 to December 2019 from Enugu State Teaching Hospital was used for this research. Seasonal ARIMA was modelled using the techniques of Box-Jenkins, and simple seasonal exponential smoothing modelled using the least squares method. Bayesian Information Criterion (BIC) was employed to obtain the best seasonal ARIMA model, while the Theil’s U statistics and MAPE were used to obtain the best forecast model. ARIMA (1,1,2)(0,0,0)12 was selected as the best SARIMA model with the BIC of 7.873, and simple seasonal exponential smoothing was considered the best forecast model with a Theil’s U Statistic of 0.11241 and MAPE of 23.450. The fitted model was used to make out-sample forecast for the period January 2020-December 2025. The fitted model in this findings will help Enugu state government to plan efficiently, expand public sensitization, and allocate adequate resources for emergencies.

Three-level multimodal transportation network for cross-regional emergency resources dispatch under demand and route reliability

Global emergencies, ranging from natural disasters and public health emergencies to terrorist attacks, have been more frequent of late. In the face of large-scale emergencies, it is difficult for an intra-regional emergency resource dispatch mode to meet the subsequent requirements. Essentially, the interruption of local routes leads to uncertainty in resource demand and transportation time. In light of this, this study defines route reliability to describe the possibility of interruption and establishes a functional relationship between route reliability and delay. We further propose a model, using an ant colony optimization design, as a solution to the problem. Using the 2008 Wenchuan earthquake in China as a numerical experiment, the target value of generalized cost was calculated to be 0.3819, which was 9.53% lower than that obtained by a two-level transportation network and 5.71% lower than that obtained by a three-level single transportation network. Additionally, dispatch plans with different route reliability were compared. In the case of pessimistic route reliability, the resources transported by highways were found to shift to railways for long distances and to aviation for short distances. In contrast, for optimistic route reliability, highway transportation with moderate cost and speed was preferred.

A multi-objective improved novel discrete particle swarm optimization for emergency resource center location problem

The location of emergency resource centers has always been a great challenge in emergency management, which directly influences the recovery speed of disaster areas and the credibility of the government. Although most studies propose mathematical models based on economic indicators and solve them with various algorithms, few of them consider the panic perceived by the victims, and several algorithms are designed from the characteristics of the problem. To address these issues, we develop an emergency resource center location model (ERCLM) with objectives of the panic perception and the total weighted distance, and then design a multi-objective improved novel discrete particle swarm optimization (MOINDPSO) for it. The main idea of the proposed algorithm is three-fold: 1) The representation of the solution is modified and the search operator is improved by a new mechanism named solution memory. 2) The solutions are evaluated by the idea of fuzzy correlation entropy analysis (FCEA), which is efficient in the selection of better solutions. 3) An external archive is used to store the nondominated solutions, and a mechanism for choosing leaders from the external archive along with a mechanism to enhance the stability for the obtained solutions is put forward. Eventually, numeric experiments of comparison algorithms and ablation experiments of two designed mechanisms are carried out on 26 data sets to show the effectiveness and the universality of the proposed algorithm.

Disaster Preparedness Model Development Community-Based Landslide

Intruduction:Preparedness is an effort carried out to anticipate the possibility of a disaster in order to avoid loss of life, loss of property, and changes in the life order of the community. Preparedness to face a disaster is a condition of individuals and groups who have physical and psychological abilities in dealing with disasters. The purpose of the study was to develop a community-based landslide disaster preparedness model. Methods:Research and Development research design, procedural model. The first year-1 is the model development stage, the second year is making module products and the third year is testing the effectiveness of module products. The population is all HH in the four major populations, 3498 HH. The sample size is 99 HH, the sampling technique is proportionate sampling technique. The independent variables are knowledge and attitude systems, policies and guidelines, intervening variable emergency response plans, early warning systems, and human resource mobilization, the dependent variable is disaster preparedness. The data collection instrument is a questionnaire. Statistical analysis is a structural model path analysis. Results: The results of the statistical analysis of the major paths of influence on preparedness are the influence of policies on preparedness 0.021165, mobilization of 0.458644, knowledge of attitudes 0.052477, early warning 0.232141, emergency response plans 0.055921. The conclusion of this study is that efforts to reduce disaster risk through building community preparedness can be built through policies, knowledge and attitude systems, emergency response plans, early warnings and resource mobilization. Conclution:The most dominant factor is the resource mobilization factor. The recommendation from the results of the study is that further research needs to explore other factors because the factors analyzed with low results are less than 50%

Substance Use-related Emergency Department Visits and Resource Utilization.

IntroductionSubstance use-related visits to the emergency department (ED) have been linked to higher service delivery costs, although little is known about the specific services used. Our goal In this study was to describe the recent trends of substance use-related ED visits and assess the association between substance use and specific ED resource utilization.MethodsWe performed a retrospective, cross-sectional study using the National Hospital Ambulatory Medical Care Survey (NHAMCS) data from 2013–2018. All ED visits in the United States for patients ≥18 years of age were included. The primary exposure was having substance use included as a chief complaint or diagnosis, which we identified using the International Classification of Diseases, 9th and 10th revisions, codes. The primary outcome was the use of diagnostic services (including laboratory studies and cardiac monitoring) or imaging studies in the ED.ResultsThe study sample included 95,506 visits in the US, extrapolating to over 619 million ED visits nationwide. The total number of ED visits remained stable during the study period, but substance use-related visits increased by 45%, with these visits making up 2.93% of total ED visits in 2013 and 4.25% in 2018. This increase was primarily driven by stimulant-, sedative- (opioids and benzodiazepines), and hallucinogen-related visits. Mental health-related visits rose in parallel by 66% during the same period. Compared to non-substance use-related visits, substance use-related visits were more likely to undergo any diagnostic study (adjusted odds ratio [aOR] 1.28; 95% confidence interval (CI): 1.11–1.47; P = 0.001), toxicology screening (aOR 10.15; 95% CI: 8.84–11.66), but less likely to have imaging studies (aOR 0.62; 95% CI: 0.56–0.68; P <0.0001). In stratified analyses, substance use-related visits with concurrent mental health disorders were more likely to undergo imaging studies (aOR 1.56; 95% CI: 1.09–2.22), while findings were opposite for those without concurrent mental health disorders (aOR 0.64; 95% CI: 0.51–0.71; P for interaction <0.0001).ConclusionSubstance use- and mental health-related ED visits are rising, and they are associated with increased resource utilization. Further studies are needed to provide more guidance in the approach to acute services in this vulnerable population.

A comprehensive framework for assessing navigation risk and deploying maritime emergency resources in the South China Sea

Owing to increasing global maritime accidents, it is highly important to optimize the allocation of limited maritime search and rescue (SAR) resources to protect crew and ships from the catastrophic influences of marine disasters. This study proposes a comprehensive framework for assessing navigation risk and deploying maritime SAR resources. The proposed framework is based on a combination of multiple criteria decision making, geospatial techniques, and game theory. First, a composite risk index is introduced and calculated for the responsibility area of each maritime SAR base by applying spatial multi-criteria decision analysis, which represents the possibility of the occurrence of accidents in the responsibility area. Next, the deployment of SAR resources for each base is likened to the playing of a cooperative game. The risk index value is used to calculate each base's Banzhaf value. Finally, the deployment of SAR resources in all bases is determined based on the Banzhaf values of each base. The northeastern South China Sea was chosen as the area for a case study to verify the usefulness of the proposed framework. The results show that the proposed approach is feasible to plan the allocation of SAR resources and to improve navigation safety.

Community and infrastructure preparedness towards the implementation of sister village disaster mitigation program

As one of the most active volcanoes in Indonesia, Merapi Volcano has potential risk towards the inhabitants living nearby. After the 2010 paroxysmal eruption, Sister Village program was established in Magelang Regency as an alternative strategy to a better disaster management by evacuating people living in the highest volcanic disaster-prone area (affected villages) to safer zones (buffer villages). By taking location in Tamanagung Village, Muntilan Sub-district, Magelang Regency as buffer village, this research tries to understand how the Sister Village program is implemented in this area, specifically to understand community and infrastructure preparedness. The study used both quantitative and qualitative data taken directly in the field by using questionnaires towards 40 respondents, checklists, and interview with keypersons. Community preparedness were analyzed using four parameters: knowledge, policy and guideline, emergency response plan and resource mobilization. The results showed that among four parameters, only knowledge parameter showing a good preparedness, while the four others still need to be improved. The use of information technology by the public, such as Whatsapp application, is widely growing. In terms of infrastructure preparedness in the buffer village, some enhancements in terms of capacity, infrastructure safety, health aspects, and comfort are still necessary.

An Analysis of Hospital Costs for Childhood Cancer Care.

This study used a linked dataset consisting of all childhood cancers recorded over the course of 10 years in New South Wales (NSW), Australia, to evaluate the hospital and emergency department costs (from a payer perspective) and resources used by patients with childhood cancer. We also analyzed determinants responsible for high-frequency hospital admissions, hospital length of stay (LoS), and hospital costs. We analyzed linked data at the individual patient level for a retrospective cohort of 2,966 patients with cancer aged <18 years with a diagnosis date between 2001 and 2012 from the NSW Central Cancer Registry, Australia. We reported costs and use of hospitalization and emergency department presentation 1 year before the date of diagnosis, 1 year after diagnosis, and 2 to 5 years after diagnosis. We also examined the association between cancer types and hospital admission and hospital costs from the payer perspective. Patient characteristics associated with the frequency of hospital admissions, hospital LoS, and hospital costs were also determined using a generalized linear model. Most hospital admission costs occurred in the first year after diagnosis, accounting for >70% of hospital costs within 5 years after diagnosis. The estimated median annual cost of hospitalization in the first year after diagnosis was A$88,964 (interquartile range [IQR], A$34,399-A$163,968) for patients diagnosed at age 0 to 14 years and A$23,384 (IQR, A$5,585-A$91,565) for those diagnosed at age 15 to 17 years. Higher frequency of hospital admissions, hospital LoS, and hospital costs were significantly associated with younger age at cancer diagnosis, cancer metastases, and living in remote/disadvantaged socioeconomic areas. Our study represents one of the first in Australia to include detailed hospitalization cost information for all childhood cancer cases. This study highlights the high hospital use by pediatric patients and the importance of early diagnosis. Our findings also demonstrate the health inequities experienced by patients from remote areas and the lowest socioeconomic areas.

How to Improve the Cooperation Mechanism of Emergency Rescue and Optimize the Cooperation Strategy in China: A Tripartite Evolutionary Game Model.

To reveal the interaction and influence mechanism between emergency rescue entities, and to explore and optimize a cooperation mechanism of emergency rescue entities, a tripartite evolutionary game model of emergency rescue cooperation based on government rescue teams, social emergency organizations, and government support institutions was constructed. The stability of each game subject’s strategy choice was explored. Simulation analysis was applied to investigate the influence mechanism of key parameters on the evolution of the game subject’s strategy combination. The research results show that government rescue teams, social emergency organizations, and government support institutions have consistent political demands and rescue targets in emergency rescue cooperation. The game subjects are driving forces for each other to choose positive strategies. The game evolution process of the emergency cooperation model shows a “mobilization-coordination” feature. At the same time, the emergency capital stock formed based on trust relationships, information matching, and institutional norms between game subjects can promote the evolution of the game system toward (1,1,1). In addition, for government organizations with limited emergency resources, the average allocation of emergency resources is not the optimal solution for emergency rescue efficiency. However, it is easier to achieve the overall target of emergency rescue cooperation by investing limited emergency resources in key variables that match the on-site situation. On this basis, combined with the practice of emergency rescues in emergencies, countermeasures and solutions are proposed to optimize the mechanism and improve the efficiency of emergency rescue cooperation.

Data-driven AI emergency planning in process industry

Whether it is through crisis mapping or event simulation, Artificial Intelligence (AI) is a pioneering new method of emergency planning. It uses the analysis of information or data from deep learning to predict the evacuation routes, allocate emergency resources reasonably and estimate the location of disaster. To overcome the difficulties of handling many resources emergency information and the variability of production environment, AI provides advanced analytics tools for processing and analyzing big data of emergency management. This paper presents a comprehensive survey of emergency planning technologies based on AI and discusses their applications in making emergency planning robust and efficiency. The development of AI technologies of emergency planning and their advantages over conventional data-driven emergency technologies are firstly discussed. Several representative emergency planning technologies based on deep learning models are compared. Finally, future trends and opportunities related to AI for emergency planning technologies are summarized.

Functionality of Scarce Healthcare Resource Triage Teams During the COVID-19 Pandemic: A Multi-Institutional Simulation Study.

High-fidelity triage-team simulation study. Healthcare institutions in Washington state. Triage teams, consisting of at least two senior clinicians and a bioethicist. Participants reviewed a limited amount of deidentified information for a diverse sample of critically ill patients. Teams then assigned each patient to one of five prioritization categories defined by likelihood of survival to hospital discharge. The process was refined based on observation and participant feedback after which a second phase of simulations was conducted. Feasibility was assessed by the time required for teams to perform their task. Prognostic accuracy was assessed by comparing teams' prediction about likelihood of survival to hospital discharge with real-world discharge outcomes. Agreement between the teams on prognostic categorization was evaluated using kappa statistics. Eleven triage team simulations (eight in phase 1 and three in phase 2) were conducted from December 2020 to February 2021. Overall, teams reviewed a median of 23 patient cases in each session (interquartile range [IQR], 17-29) and spent a median of 102 seconds (IQR, 50-268) per case. The concordance between expected survival and real-world survival to discharge was 71% (IQR, 64-76%). The overall agreement between teams for placement of patients into prognostic categories was moderate (weighted kappa = 0.53). These findings support the potential feasibility, accuracy, and effectiveness of institutional triage teams informed by a limited set of patient information items as part of a strategy for allocating scarce resources in healthcare emergencies. Additional work is needed to refine the process and adapt it to local contexts.

Montana County ends decade‐long contract with MH provider

One of Montana's leading providers of behavioral health services will no longer have an operating contract with Gallatin County as of Feb. 2, the Montana Free Press reported Jan.12. Citing several breaches of its agreement, Gallatin County commissioners and County Attorney Marty Lambert notified Western Montana Mental Health Center in a letter that the agreement would be terminated in 15 business days. “Gallatin County hereby advises all of you that the WMMHC has, on multiple occasions, breached its contractual obligations to the County, including but not limited to, the pro‐visions outlined below,” stated an accompanying Jan. 10 letter addressed to WMMHC Chairperson Levi Anderson. Gallatin County has been paying the WMMHC $29,417.50 monthly to provide a range of services. One is the provision of at least one bed for temporary, involuntary holds at the WMMHC's Hope House in Bozeman for people whose mental health crisis presents a danger to themselves or others. County officials say Hope House has been insufficiently operated at various times in recent years. Ending the more than 10‐year contract, Lambert said, “finally allows us to move forward with the provision of these very important crisis services which have only been sporadically met by Western for quite some time.” The WMMHC's Anderson attributes much of the difficulty at Hope House to low staffing levels exacerbated by the COVID‐19 pandemic — an issue he says is not unique to his organization. “The entire State of Montana is experiencing a drastic workforce shortage and behavioral health providers, WMMHC included, are struggling with workforce,” Anderson said in an email, adding that his organization has had to be “nimble” to modify services during the pandemic. “The Gallatin County Commission and County Attorney are alleging a breach of service based on these pandemic‐related operational challenges that have been experienced by many organizations, including the County themselves.” Lambert said the county does not expect to enter into a new contract with any other behavioral health care provider, which would require issuing a request for proposals to interested organizations, before the WMMHC's contract ends. The county's agreement with Bozeman Health, he said, will provide an emergency resource for people in crisis until the county can find new providers to work with long term.

REVIEW AND COMPARISON OF THE DEMAND ANALYSIS METHODS OF MARITIME EMERGENCY RESOURCES

The demand analysis method of maritime emergency resources is the key technology to promote a reasonable emergency resource allocation during maritime emergency management. It is widely used to improve the efficiency of maritime emergency rescue and reduce the loss of maritime accidents. However, it requires a scientific and effective method of the demand analysis of maritime emergency resources. This paper aims to analyze the underlying modeling paradigms and to assess the extent to which the demand analysis methods of maritime emergency resources can meet the requirements. Focusing on the demand analysis methods, this paper provides a broad overview of the current literature on maritime emergency resources of the last decades, by considering the models’ purposes, theoretical frameworks, factors, and outputs. The results indicate that the existing methods can be classified into three concepts: the linear regression theory, Back Propagation (BP) Neural Network, and Case-based Reasoning (CBR) technology. Combined with the characteristics of China's maritime emergency management field, the interaction between theoretical framework and applications is not sufficiently understood and thus needs to be further studied. Being familiar with knowledge gaps acts as a catalyst for further research on scientific and efficient demand analysis methods of maritime emergency resources in various navigation conditions.

Alternative Water Resources Selection to Supply Drinking Water in Flood Disasters by Multicriteria Decision-Making Techniques (DANP and VIKOR).

Background Emergency is generally caused by natural disaster and infectious disease outbreaks, or it is man-made. Floods are natural phenomena that generally appear in multiple parts of the world. Flooding is one of the most destructive naturally occurring environmental hazards and can cause public, infrastructural, and environmental damage. The purpose of this study is to select alternative water resources for supplying Bandar Abbas in flood disasters by multicriteria decision-making techniques. Methods Information required includes possible water resources alternative for flood, quantitative and qualitative characteristics of the water resources, climatic circumstances, and demographic information used in organizations data and previous studies. After selecting and proposing water resources alternative for Bandar Abbas in flood, the subcriteria were weighed applying DANP (DEMATEL-ANP) techniques and water resources were prioritized with the VIKOR technique. According to the network structure and internal and external dependence of the criteria and subcriteria, the advantages of DANP in calculating weights have been used to adapt to more real-world problems. The VIKOR technique was developed for multicriteria optimization of complex systems. Results After reviewing and extracting the criteria from various studies, 9 main criteria and 44 subcriteria were defined to select water resources in disasters and emergencies. According to field studies and related organizations' information, the proposed water resources for Bandar Abbas to use in flood disasters include humidity, sea (Persian Gulf), Sarkhoon plain, and wastewater treatment plant of Bandar Abbas. Conclusion Results showed that the optimal water resources for Bandar Abbas in flood disasters are the sea and wastewater treatment plant effluent (after advanced treatment). The study proposed appropriate model to select optimal water resources for various natural disasters in different geographical areas. This model can help officials and decision-makers to plan for drinking water supply from disaster-prone areas before disasters occur.

Artificial Intelligence in Paediatric Emergencies: A Narrative Review

Background: The functionality of Artificial intelligence (AI) in paediatric practices has been gaining more attention for last five years. Since then, researchers have started observing that the techniques are helpful in dealing multiple facets of childhood diseases including emergency like situations. This article has been aimed to discuss the current status of usefulness of AI in paediatric emergencies. Methods: Total 22 research articles have been reviewed. Articles were searched from electronic database like Pubmed, Medline, Google scholar. Artificial intelligence, machine learning, paediatric emergencies, childhood diseases were the key words used to ease the search. Results: Out of 22, 15 were chosen as relatable to paediatric emergency situations per se. After reviewing the available literature, the utility of AI in paediatric emergencies had been discussed under four sub headings: i) Diagnosis; ii) Predictive modelling iii) Assistance in Antimicrobial stewardship iv) Management of emergency department resources. Conclusion: AI and different machine learning techniques have been proven as reliable accompaniment of paediatricians. They can provide their support in terms of early diagnosis for example the septic shock in children, prediction of disease severity like in the cases of traumatic brain injury, drug doses and emergency resource management. Lack of research on extensive data on far reaching population, legal and trust issues and unfriendly software’s are the challenges those need to be resolved for utilizing AI at its higher potential in paediatric healthcare.

On Allocation Approach of Emergency Resource of Disaster in Narrow Non-Exposed Space Based on Risk Assessment

On Allocation Approach of Emergency Resource of Disaster in Narrow Non-Exposed Space Based on Risk Assessment

A Novel Pre-Storm Island Formation Framework to Improve Distribution System Resilience Considering Tree-Caused Failures

This paper presents a new framework for island formation prior to windstorms, which considers tree-caused failures of distribution networks. In the proposed framework, both direct and indirect effects of windstorms on distribution lines are quantified. Thus, a novel discrete Markov chain model is proposed for representing the failure modes of trees in each time interval of windstorm duration. This model categorizes &#x201C;healthy&#x201D;, &#x201C;uprooted&#x201D;, &#x201C;stem breakage&#x201D;, and &#x201C;branch breakage&#x201D; states of a tree. In addition, a new line-tree interaction model is presented for calculating tree-caused failure probability of overhead lines. The results of the proposed Markov model are taken as inputs by the developed line-tree interaction model. In these models, the different characteristics of windstorms are taken into account. Tree vulnerability to windstorms is characterized by different factors such as their species, height, and critical wind speeds. Windstorm duration is sectionalized into multiple time intervals, and the proposed models are applied to trees and distribution system components in each interval. Moreover, the interdependency between the intervals is captured by the Markov model. The results of the models are used by an optimization model, thereby dividing a distribution system into multiple islands before storm onset. Subsequently, the framework is extended as a two-stage stochastic optimization problem to address the uncertainties of loads. In addition, this framework considers the allocation of mobile emergency resources. The proposed models are implemented on the IEEE 33- and 123-bus test systems, as well as a practical distribution feeder, and their effectiveness is demonstrated through several case studies.

Optimal matching of urban emergency resources under major public health events by multi-expert decision model of Grey situations

Optimal matching of urban emergency resources under major public health events by multi-expert decision model of Grey situations

Exploring Optimal Location of Emergency Resources Based Complex Network and Node Importance Analysis in Urban Rail Transit

Exploring Optimal Location of Emergency Resources Based Complex Network and Node Importance Analysis in Urban Rail Transit

Identification of Parameters Required to Reduce Emergency Response Time for An Integrated Facility

In an emergency incident time is of the crucial factor, and the basic philosophy of an emergency response agency is to respond as quickly as possible to minimize the loss of life and property damage. Emergency response operations are very important activities in the oil and gas industrial areas. As an incident in an integrated facility at oil and gas industrial area can have considerable major economic and social impact, response to such an incident must be provided in a very short time. The use of parameters plays an important role in the successful implementation of emergency response. This case study considered configuration emergency response time parameters at an integrated facility. Therefore, to predict the response time consideration must be given to the characteristics of emergency response parameters, its effectiveness and efficiency. The decisions for such emergency responses to integrated facility should consider the available emergency resources, and other factors such as emergency responders’ competency, fire protection and fire detection system, and the characteristics of surrounding affected industrial facilities. Pilot Test results showed that the top five most parameters affecting emergency response time at the case study of oil and gas integrated facility were namely location, competency, road access, emergency vehicle and frequent drill/exercise. Other analyses resulted that emergency drills and exercises parameter shall be critically examined. Drills constitute a simultaneous and comprehensive test of emergency plans, staffing levels, personnel training, procedures, facilities, equipment, and materials. Having this process, the company able to improve emergency preparedness and response management and aligned with crisis management protocol by government authority.