A disaster response model driven by spatial-temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts

A disaster response model driven by spatial-temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts.</p

Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm

Assessing Requirements for Decision Support Systems in Humanitarian Operations

Efficient and effective decision making in the chaotic environment of humanitarian relief distribution (HRD) is a challenging task. Decision makers, in such situations, are required to concentrate on numerous attributes classified by three decision factors: objectives, variables, and constraints. Recent HRD literature mainly focuses on optimizing procedures while neglecting the quantification of influential requirements (factors) for information systems to provide decision-making support. This article addresses this gap by accumulating those affecting attributes from the literature. It investigates their practical implications in HRD by measuring the preferences of a Delphi panel of 23 experts. The results quantify the importance of each attribute – along with the newly added ones by the experts – in the proposed process model for HRD in a large-scale sudden onset. Our work provides future researchers not only with a comprehensive set of practically feasible decision-making factors in HRD but also with an understanding of their influences or correlations

Making decisions for effective humanitarian actions: a conceptual framework for relief distribution

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A risk-aversion approach for the Multiobjective Stochastic Programming problem

Multiobjective stochastic programming is a field well located to tackle problems arising in emergencies, given that uncertainty and multiple objectives are usually present in such problems. A new concept of solution is proposed in this work, especially designed for risk-aversion solutions. A linear programming model is presented to obtain such solution.Comment: 29 pages, 3 figures, 17 table

Antibiotic Resistance in Syria: A Local Problem Turns Into a Global Threat

Pharmaceutical sector of Syrian Arab Republic before the war was characterized by bold and successful development since the late 1980s. With the beginning of war in the country back in March 2011, momentum has changed significantly. Traumatism, communicable diseases related to morbidity and mortality as well as wound infections became particularly hot public health concern. This relates not only to the direct victims of military conflict but also to the displaced civilians, refugees, and ordinary citizens alike. Evolving legislative framework in Syria since 1980s tolerated dispensing of antibiotics without appropriate prescription. Such practice led to spreading of antibiotic resistance among the local bacteria frequently causing both community-acquired and nosocomial infections. Laboratory findings of resistant bacteria strains among the Syrian refugees in some European countries serve as evidence of concern spreading far beyond Middle East. Practice of self-diagnosis and self-medication with antibiotics by patients themselves and restraint to pharmacist advice is widespread. A number of recommendations is presented to stakeholders to compact antibiotic resistance after the peace is established in the country. The successful implementation of such recommendations is the way to preserve shrinking golden reserve of highly potent antibiotics as it is the last defense line against resistant bacterial strains causing severe life—threatening infections

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

PurposeThe purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.Design/methodology/approachThe authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.FindingsThe findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.Originality/valueThis is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains