Are Disasters a Risk to Regional Fiscal Balance? Evidence from Indonesia

Indonesia is an archipelago country and is fairly vulnerable to disasters. While disasters generally affect government revenue and expenditure, their effects likely vary by country. This study examines the effect of disasters on the fiscal balance, revenue, and expenditure of local governments. We used panel data and fixed effects methods to estimate the degree to which disaster severity influences budgetary solvency at the district and provincial levels in Indonesia between 2010 and 2018. This study revealed that disasters can strain fiscal balance at the district and provincial levels due to a decrease in own-source revenue and an increase in social assistance expenditure, capital expenditure, consumption expenditure, and unexpected expenditure. The district expenditure most threatened by disasters is consumption expenditure, while the provincial expenditure most threatened is unexpected expenditure. We also found that an increase in capital expenditure can lead to financial burden due to delays of planned projects or post-disaster reconstruction. Based on these findings, it is clear that some forms of insurance or other financing schemes are necessary to mitigate the adverse impacts of disasters on regional fiscal balance.

Evaluation of Disaster Preparedness and Preparedness Behaviors among Pharmacists: A Cross-Sectional Study in Australia

Introduction: In a disaster aftermath, pharmacists have the potential to provide essential health services and contribute to the maintenance of the health and well-being of their community. Despite their importance in the health care system, little is known about the factors that affect pharmacists’ disaster preparedness and associated behaviors. Study Objective: The goal of this study was to determine the factors that influence disaster preparedness behaviors and disaster preparedness of Australian pharmacists. Methods: A 70-question survey was developed from previous research findings. This survey was released online and registered Australian pharmacists were invited to participate. Multiple linear regression was used to determine the factors that influenced preparedness and preparedness behaviors among pharmacists. Results: The final model of disaster preparedness indicated that 86.0% of variation in preparedness was explained by disaster experience, perceived knowledge and skills, colleague preparedness, perceived self-efficacy, previous preparedness behaviors, perceived potential disaster severity, and trust of external information sources. The final model of preparedness behaviors indicated that 71.1% of variation in previous preparedness behaviors can be explained by disaster experience, perceived institution responsibility, colleague preparedness, perceived likelihood of disaster, perceived professional responsibility, and years of practice as a pharmacist. Conclusion: This research is the first to explore the significant factors affecting preparedness behaviors and preparedness of Australian pharmacists for disasters. It begins to provide insight into potential critical gaps in current disaster preparedness behaviors and preparedness among pharmacists.

Air and ground ambulance location-allocation-routing problem for designing a temporary emergency management system after a disaster

Disasters cause a huge number of injured patients in a short time while existing emergency facilities encountered devastation and cannot respond properly. Here, the importance of implementing temporary emergency management becomes clear. This study aims to locate some temporary emergency stations across the area by maximal covering after a disaster. Furthermore, a multi-mode fleet is used for transferring patients using different modes of transportation (e.g. helicopter ambulance and bus ambulance). Since the type of patients may change over periods, medical servers can displace among temporary emergency stations dynamically according to disaster severity. For this purpose, a new bi-objective dynamic location-helicopter ambulance allocation-ambulance routing model with multi-medical servers is presented. The first objective function minimizes the operational costs related to the newly designed Emergency Medical Service along with the rate of human loss. The second objective function minimizes the critical time spent before the medical treatment. To validate the developed model, the augmented ε-constraint method is used and applied for the Tehran city, which shows the applicability of the model. Finally, two meta-heuristic algorithms are customized for large-sized problems, and the related results are compared based on multi-objective algorithms’ performance comparison metrics to find the more efficient one.

Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness

To address the shortage of relief in disaster areas during the early stages after an earthquake, a location-routing problem (LRP) was studied from the perspective of fairness. A multi-objective model for the fair LRP was developed by lexicographic order object optimal method in consideration of the urgent window constraints, partial road damage, multimodal relief delivery, disaster severity, and vulnerability of each demand node when its demand is not satisfied. The goals of this model are to minimize (1) the maximum loss of demand node, (2) the total loss of demand node, and (3) the maximum time required for the demand node to receive relief. A hybrid heuristic algorithm was proposed to solve the model. Finally, the utility and fairness of the model and algorithm were demonstrated by a case study during the first day after the great Wenchuan earthquake in China.

The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas

The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.

The Yew Disaster Severity Index: A New Tool in Disaster Metrics

ObjectivesThe Richter Scale measures the magnitude of a seismic occurrence, but it does not feasibly quantify the magnitude of the “disaster” at the point of impact in real humanitarian needs, based on United Nations International Strategy for Disaster Reduction (UNISDR; Geneva, Switzerland) 2009 Disaster Terminology. A Disaster Severity Index (DSI) similar to the Richter Scale and the Mercalli Scale has been formulated; this will quantify needs, holistically and objectively, in the hands of any stakeholders and even across timelines.BackgroundAn agreed terminology in quantifying “disaster” matters; inconsistency in measuring it by stakeholders posed a challenge globally in formulating legislation and policies responding to it.MethodsA quantitative, mathematical calculation which uses the median score percentage of 100% as a baseline, indicating the ability to cope within the local capacity, was used. Seventeen indicators were selected based on the UNISDR 2009 disaster definition of vulnerability and exposure and holistic approach as a pre-condition. The severity of the disaster is defined as the level of unmet needs. Thirty natural disasters were tested, retrospectively, and non-parametric tests were used to test the correlation of the DSI score against the indicators.ResultsThe findings showed that 20 out of 30 natural disasters tested fulfilled the inability to cope, within local capacity in disaster terminology. Non-parametric tests showed that there was a correlation between the 30 DSI scored and the indicators.ConclusionBy computing a median fit percentage score of 100% as the ability to cope, and the correlation of the 17 indicators, in this DSI Scale, 20 natural disasters fitted into the disaster definition. This DSI will enable humanitarian stakeholders to measure and compare the severity of the disaster objectively, as well as enable future response to be based on needs.YewYY, Castro DelgadoR, HeslopDJ, Arcos GonzálezP. The Yew Disaster Severity Index: a new tool in disaster metrics. Prehosp Disaster Med. 2019;34(1):8–19.