Bacillus anthracis Bioterrorism Research Priorities for Public Health Response

This meeting provided a forum for 132 representatives from the Department of Health and Human Services (Centers for Disease Control and Prevention (CDC), Food and Drug Administration (FDA), and National Institutes of Health (NIH)), Environmental Protection Agency (EPA), Department of Defense (DoD), Department of Energy (DoE), US Postal Service, State Health Departments, universities and other organizations to identify, prioritize, and coordinate near-term Bacillus anthracis bioterrorism research for public health response. During the recent anthrax bioterrorism investigation CDC and its partners identified a number of areas where additional research may be useful in improving public health response.The disciplines and specific expertise required to approach many of these areas are varied and exist within multiple federal government entities and elsewhere. To address those research questions that are most critical to improving public health response to B. anthracis-related bioterrorism, CDC convened this meeting to obtain input on critical research priorities and coordinate with federal partners and other stakeholders in planning and conduct of applied research that needs to be initiated within the next 12 months.The workshop format consisted of two plenary sessions in which experts provided summaries of the existing science in key topic areas. Background talks were given on the \u201cEvaluation of B. anthracis containing powders or substances\u201d by Mathew Shaw, Battelle Memorial Institute; \u201cEpidemiologic Investigation\u201d by Philip Brachman, Emory University, School of Public Health; \u201cEnvironmental Assessment\u201d by Edwin Kilbourne, Agency for Toxic Substances and Drug Research; \u201cSurveillance\u201d by Ruth Berkelman, Emory University; \u201cIntroduction to issues in Diagnosis, Treatment, and Prevention of Anthrax\u201d by Art Friedlander, US Army Medical Research Institute for Infectious Diseases (USAMRIID), DoD; \u201cDiagnosis\u201d by Susan Alpert, C.R. Bard, Inc.; \u201cTreatment\u201d by Dennis Stevens, Veterans Affairs Medical Center, University of Washington; \u201cPost-exposure prophylaxis\u201d by Diane Murphy, Food and Drug Administration; and \u201cRemediation\u201d by Dorothy Cantor, Environmental Protection Agency.Following the first plenary session, participants were divided into eight pre-assigned working groups. The eight working groups included: 1) Evaluation of B. anthracis containing powders or substances, 2) Epidemiological investigation, 3) Environmental assessment, 4) Surveillance, 5) Diagnosis, 6) Treatment, 7) Post-exposure prophylaxis, and 8) Remediation. Each of the 8 working groups had pre-assigned co-leaders, one from outside of CDC and one from CDC. Each of the CDC co-leads were senior scientists who had been heavily involved in the anthrax bioterrorism investigation and response. Lists of specific questions were given to each of the working groups to help stimulate discussion and provide direction based on observations during the anthrax bioterrorism investigation. During the second plenary session each of the groups presented interim results of discussion for input from the larger group of meeting participants. In the second working group session, groups were asked to prepare a written report of their group\u2019s top three research priorities.Executive Summary -- Working Group Reports on Top Three Priority Priorities: Evaluation of B. anthracis Containing Powders or Substances, Epidemiological Investigation, Environmental Assessment, Surveillance, Diagnosis, Treatment, Post-exposure Prophylaxis, Remediation -- Agenda -- Meeting Guidance and Suggestions for Working Group Sessions I and II -- Questions for the Working Groups -- List of Suggested References -- List of Meeting Participants & Contact Information [deleted from online document per request] \u2013 Acknowledgements.200

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

In the Wake of the Storm: Environment, Disaster, and Race After Katrina

Studies evidence of environmental disparities by which poor and minority communities are disproportionately exposed to disasters, are less prepared, and have less access to relief agencies. Makes recommendations for preparedness and environmental justice

Health horizons: Future trends and technologies from the European Medicines Agency’s horizon scanning collaborations

In medicines development, the progress in science and technology is accelerating. Awareness of these developments and their associated challenges and opportunities is essential for medicines regulators and others to translate them into benefits for society. In this context, the European Medicines Agency uses horizon scanning to shine a light on early signals of relevant innovation and technological trends with impact on medicinal products. This article provides the results of systematic horizon scanning exercises conducted by the Agency, in collaboration with the World Health Organization (WHO) and the European Commission’s Joint Research Centre’s (DG JRC). These collaborative exercises aim to inform policy-makers of new trends and increase preparedness in responding to them. A subset of 25 technological trends, divided into three clusters were selected and reviewed from the perspective of medicines regulators. For each of these trends, the expected impact and challenges for their adoption are discussed, along with recommendations for developers, regulators and policy makers

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Alfred P. Sloan Foundation - 2006 Annual Report

Contains program information, grantee profiles, grants list, and financial statements

Emerging Threats of Synthetic Biology and Biotechnology

Synthetic biology is a field of biotechnology that is rapidly growing in various applications, such as in medicine, environmental sustainability, and energy production. However these technologies also have unforeseen risks and applications to humans and the environment. This open access book presents discussions on risks and mitigation strategies for these technologies including biosecurity, or the potential of synthetic biology technologies and processes to be deliberately misused for nefarious purposes. The book presents strategies to prevent, mitigate, and recover from ‘dual-use concern’ biosecurity challenges that may be raised by individuals, rogue states, or non-state actors. Several key topics are explored including opportunities to develop more coherent and scalable approaches to govern biosecurity from a laboratory perspective up to the international scale and strategies to prevent potential health and environmental hazards posed by deliberate misuse of synthetic biology without stifling innovation. The book brings together the expertise of top scholars in synthetic biology and biotechnology risk assessment, management, and communication to discuss potential biosecurity governing strategies and offer perspectives for collaboration in oversight and future regulatory guidance

Robust Optimization for Supply Chain Applications: Facility Location and Drone Delivery Problems

RÉSUMÉ: Les décisions concernant la localisation des infrastructures dans les chaînes d’approvisionnement sont d’une importance stratégique: la construction d’une nouvelle infrastructure est généralement coûteuse et l’impact de cette décision est durable. Une fois qu’une nouvelle installation sera ouverte, elle devrait rester opérationnelle pendant plusieurs années. Cependant, des facteurs environnementaux, tels que les déplacements de population et les catastrophes naturelles, peuvent affecter le fonctionnement des installations. Par exemple, le déplacement de la population peut modifier les modèles de demande, ce qui influence davantage les décisions d’allocation entre les clients et les installations. Les catastrophes naturelles peuvent diminuer partiellement ou complètement la capacité d’une installation, entraînant des décisions de réaffectation ou des pertes de ventes. Toutes ces incertitudes peuvent faire en sorte qu’une décision optimale d’aujourd’hui ne donne pas de bons résultats à l’avenir. Ainsi, il est important de considérer les incertitudes potentielles dans la phase de conception des chaînes d’approvisionnements, tout en prenant explicitement en compte les réaffectations possibles des clients comme décisions de recours dans la phase d’exécution. Dans la première moitié de cette thèse, nous étudions trois problèmes de localisation d’établissements sous risques de perturbations, où chaque travail a un objectif différent. Plus précisément, l’étude du chapitre 3 se concentre principalement sur l’amélioration des algorithmes; le travail du chapitre 4 considère simultanément plusieurs types d’incertitudes; et le chapitre 5 étudie un problème de conception de réseau à trois échelons soumis à des perturbations. Nous adoptons des méthodes d’optimisation robuste (OR) en deux étapes, où les décisions de localisation des installations sont prises ici et maintenant et les décisions de recours pour réaffecter les clients sont prises après que les informations d’incertitude sur la disponibilité des installations et la demande des clients ont été révélées. Nous implémentons des méthodes exactes et approximatives pour résoudre les modèles robustes. Les résultats démontrent que le cadre OR proposé peut améliorer la fiabilité des systèmes de chaîne d’approvisionnement avec seulement une légère augmentation du coût normal (le coût du scénario sans interruption). Les différents modèles construits dans cette thèse peuvent également être utilisés comme outils d’aide à la décision pour voir le compromis entre coût et fiabilité. Outre la planification stratégique, nous avons étudié également les problèmes de niveau opérationnel : problèmes de livraison à l’aide de drones. La livraison par drone est connue comme contributeur potentiel à l’amélioration de l’efficacité et à la résolution des problèmes de livraison du dernier kilomètre. Pour cette raison, le routage des drones est devenu un domaine de recherche très actif ces dernières années. Contrairement au problème de routage des véhicules, cependant, la conception des itinéraires des drones est difficile en raison de multiples caractéristiques opérationnelles, notamment les opérations multi-voyages, la planification de la recharge et le calcul de la consommation d’énergie. Pour combler certaines lacunes importantes dans la littérature, le chapitre 6 résout un problème de routage de drone multi-voyages, où la consommation d’énergie des drones est affectée par la charge utile et la distance de déplacement alors que de telles relations sont non linéaires. Pour aborder la fonction d’énergie non linéaire (convexe), nous proposons deux types de coupes (cuts) qui sont incorporées dans le schéma de branchement et de coupes (branch-and-cut). Nous utilisons une formulation à 2 indices pour modéliser le problème et également générer des instances de référence pour l’évaluation d’algorithmes. Les tests numériques indiquent que même si le modèle d’origine est non linéaire, notre approche est efficace à la fois en termes d’algorithme et de qualité de solution. La livraison par drones peut également être affectée par diverses incertitudes, telles que des conditions de vent incertaines et des obstacles imprévisibles. Motivé par les problèmes de retard des drones résultant de l’incertitude du vent, notre travail dans le chapitre 7 vise à optimiser de manière robuste le risque de retard pour un problème de programmation de drones avec des temps de voyage incertains. À cette fin, nous utilisons un cadre d’optimisation robuste aux distributions pour modéliser le problème. Comme les données historiques sur le vent sont souvent disponibles, nous utilisons des techniques d’apprentissage automatique pour partitionner les données pour la construction de l’ensemble d’ambiguïté. À partir des données météorologiques réelles, nous observons que les conditions de vent l’après-midi dépendent des conditions de vent du matin. Par conséquent, nous proposons une description de l’ambiguïté en ensemble à deux périodes pour modéliser la distribution conjointe des temps de parcours incertains. Nous proposons également un modèle de planification des drones à deux périodes, où les décisions de programmation dans l’après-midi s’adapteraient aux résultats des informations météorologiques observées le matin. En utilisant des données météorologiques réelles, nous validons que le modèle d’optimisation robuste adaptatif peut réduire efficacement le retard dans les tests hors échantillon par rapport à d’autres méthodes de référence.----------ABSTRACT: Facility location decision is strategic: The construction of a new facility is typically costly and the impact of the decision is long-lasting. Once a new facility is opened, it is expected to remain in operation for several years. However, environmental factors, such as population shift and natural disasters, may affect facilities’ operations. For example, population shift may change demand patterns, which further influence the allocation decisions between customers and facilities. Natural disasters may diminish a facility’s capacity partially or completely, resulting in reassignment decisions or lost sales. All these uncertainties may cause today’s optimal decision to perform poorly in the future. Thus, it is important to consider potential uncertainties in the supply chain design phase, while explicitly taking into account the possible customer reassignments as recourse decisions in the execution phase. In the first half of this thesis, we study three facility location problems under disruption risks, where each work has a different focus. Specifically, the study in Chapter 3 mainly focuses on algorithmic improvement; the work in Chapter 4 considers multiple types of uncertainties simultaneously; and Chapter 5 studies a three-echelon network design problem under disruptions. We adopt the two-stage robust optimization (RO) method for these problems, where facility location decisions are made here-and-now and recourse decisions to reassign customers are made after the uncertainty information on the facility availability and customer demand has been revealed. We implement both exact and approximate methods to solve the robust models. Results demonstrate that the proposed RO framework can improve supply chain systems’ reliability with only a slight increase in the nominal cost (the cost of the disruption-free scenario). The various robust models constructed in this thesis can also be used as decision support tools to see the trade-off between cost and reliability. Besides strategic planning, we also study operational level problems in this thesis—drone delivery problems. Drone delivery is known as a potential contributor in improving efficiency and alleviating last-mile delivery problems. For this reason, drone routing and scheduling has become a highly active area of research in recent years. Unlike the vehicle routing problem, however, designing drones’ routes is challenging due to multiple operational characteristics including multi-trip operations, recharge planning, and energy consumption calculation. To fill some important gaps in the literature, Chapter 6 solves a multi-trip drone routing problem, where drones’ energy consumption is affected by payload and travel distance whereas such relationships are nonlinear. To tackle the nonlinear (convex) energy function, we propose two types of cuts that are incorporated into the branch-and-cut scheme. We use a 2-index formulation to model the problem and also generate benchmark instances for algorithm evaluation. Numerical tests indicate that even though the original model is nonlinear, our approach is effective in both computational efficiency and solution quality. Drone delivery can also be affected by various uncertainties, such as uncertain wind conditions and unpredictable obstacles. Motivated by the drone lateness issues resulting from wind uncertainty, our work in Chapter 7 aims to robustly optimize the lateness risk for a drone scheduling problem with uncertain travel times. To that end, we use a distributionally robust optimization framework to model the problem. As historical wind data is often available, we use machine learning techniques to partition the data for the construction of the ambiguity set. From the actual weather data, we observe that the wind conditions in the afternoon are dependent on the wind conditions in the morning. Accordingly, we propose a two-period cluster-wise ambiguity set to model the joint distribution of uncertain travel times. We also propose a two-period drone scheduling model, where the scheduling decisions in the afternoon would adapt to the outcome of the weather information observed in the morning. Using actual weather data, we validate that the adaptive robust optimization model can effectively reduce lateness in out-of-sample tests in comparison with other benchmark methods. Keyword: Facility location; disruption risk; demand uncertainty; two-stage robust optimization; column-and-constraint generation; drone delivery; nonlinear energy consumption; branch-and-cut; uncertain weather condition; cluster-wise ambiguity set; distributionally robust optimizatio

Ethical issues of synthetic biology: a personalist perspective

The main objective of this thesis is to assess the bioethical issues raised by Synthetic Biology from a specific bioethical approach, personalism, specifically ontological personalism, a philosophy that shows the objective value of the person on the basis of its ontological structure. The person, as a being endowed with reason, freedom and awareness, has a special value which is above that of other beings.El objetivo principal de este trabajo es evaluar las cuestiones bioéticas planteadas por la Biología Sintética desde un enfoque bioético específico, el personalismo, específicamente el personalismo ontológico, una filosofía que muestra el valor objetivo de la persona sobre la base de su estructura ontológica.Ciencias ExperimentalesPrograma Oficial de Doctorado en Bioétic