Preparing for a “dirty bomb” attack: the optimum mix of medical countermeasure resources

Abstract Background In radiological emergencies with radionuclide incorporation, decorporation treatment is particularly effective if started early. Treating all people potentially contaminated (“urgent treatment”) may require large antidote stockpiles. An efficacious way to reduce antidote requirements is by using radioactivity screening equipment. We analyzed the suitability of such equipment for triage purposes and determined the most efficient mix of screening units and antidote daily doses. Methods The committed effective doses corresponding to activities within the detection limits of monitoring portals and mobile whole-body counters were used to assess their usefulness as triage tools. To determine the optimal resource mix, we departed from a large-scale scenario (60,000 victims) and based on purchase prices of antidotes and screening equipment in Germany, we calculated efficiencies of different combinations of medical countermeasure resources by data envelopment analysis. Cost-effectiveness was expressed as the costs per life year saved and compared to risk reduction opportunities in other sectors of society as well as the values of a statistical life. Results Monitoring portals are adequate instruments for a sensitive triage after cesium-137 exposure with a high screening throughput. For the detection of americium-241 whole-body counters with a lower daily screening capacity per unit are needed. Assuming that 1% of the potentially contaminated patients actually need decorporation treatment, an efficient resource mix includes 6 monitoring portals and 25 mobile whole-body counters. The optimum mix depends on price discounts and in particular the fraction of victims actually needing treatment. The cost-effectiveness of preparedness for a “dirty bomb” attack is less than for common health care, but costs for a life year saved are less than for many risk-reduction interventions in the environmental sector. Conclusion To achieve economic efficiency a high daily screening capacity is of major importance to substantially decrease the required amount of antidote doses. Among the determinants of the number of equipment units needed, the fraction of the potentially contaminated victims that actually needs treatment is the most difficult to assess. Judging cost-effectiveness of the preparedness for “dirty bomb” attacks is an issue of principle that must be dealt with by political leaders.

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Analysis of the antidote requirements and outcomes of different radionuclide decorporation strategies for a scenario of a “dirty bomb” attack

American Journal of Disaster Medicine ◽

10.5055/ajdm.2017.0276 ◽

2017 ◽

Vol 12 (4) ◽

pp. 227-241 ◽

Cited By ~ 2

Author(s):

Alexis Rump, MD, PhD, MHBA ◽

Daniela Stricklin, PhD ◽

Andreas Lamkowski, MD ◽

Stefan Eder, MD ◽

Michael Abend, MD, PhD ◽

...

Keyword(s):

Treatment Strategy ◽

Treatment Duration ◽

Large Scale ◽

Precautionary Approach ◽

Dirty Bomb ◽

Cesium 137 ◽

Bomb Attack ◽

The Impact ◽

Radionuclide Incorporation ◽

Dose Reductions

Objective: In radiological emergencies, there is a risk of radionuclide incorporation. The radiological doses absorbed can be reduced by decorporation treatment. Antidote requirements depend on the scenario and treatment strategy (“urgent approach”: immediate treatment of all patients with possible incorporation; “precautionary approach”: treatment only after confirmation of incorporation). We calculated the number of daily antidote doses for different scenarios and the differences in outcome for both treatment strategies.Design: The number of potentially contaminated victims was varied from 1,000 to 60,000 (a maximum that might seem plausible for “dirty bomb” scenarios in Germany), the proportion of patients actually needing decorporation treatment from 0.1 percent to 100 percent, the radioactive screening capacities from 250 to 2,500 people/day and treatment duration from 10 to 90 days. The outcomes were assessed as total statistical lifetime saved assuming an inhalation of 1 mCi cesium-137 and the achievable dose reductions by a Prussian Blue treatment.Results: Assuming 1 percent of the potentially contaminated people actually needing treatment, applying an “urgent approach” the requirements for 1,000 victims range from 1,100 to 3,400 and for 60,000 victims from 489,000 to 4,400,000 daily doses, depending on treatment duration and screening capacities. The “urgent approach” is associated with larger stockpile requirements than the “precautionary approach”, up to several hundred times in large-scale scenarios if the proportion of people actually needing treatment is low. The impact of the screening capacities is particularly important in large-scale scenarios, a low proportion of people needing treatment and extended treatment duration. The outcome is better for an “urgent approach” particularly in large-scale scenarios and low screening capacities.Conclusions: If only a small fraction of the victims actually needs treatment, their timely identification by enhancing screening capacities may be the most efficacious way to reduce antidote requirements. In large-scale scenarios, it might be necessary to abandon the medically preferable “urgent approach” for an antidote-sparing “precautionary approach”.

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Estimation of radiation-induced health hazards from a “dirty bomb” attack with radiocesium under different assault and rescue conditions

Military Medical Research ◽

10.1186/s40779-021-00349-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Alexis Rump ◽

Stefan Eder ◽

Cornelius Hermann ◽

Andreas Lamkowski ◽

Patrick Ostheim ◽

...

Keyword(s):

Radiation Effects ◽

Terrorist Attack ◽

Scenario Planning ◽

Health Hazards ◽

External Irradiation ◽

Red Bone Marrow ◽

Dirty Bomb ◽

Cesium 137 ◽

Bomb Attack ◽

The U.S

AbstractIn the case of a terrorist attack by a “dirty bomb”, blast injuries, external irradiation and the incorporation of radioactivity are to be expected. Departing from information about the radiological attack scenario with cesium-137 in the U.S. National Scenario Planning Guide, we estimated the radiological doses absorbed. Similar calculations were performed for a smaller plume size and a detonation in a subway. For conditions as described in the U.S. scenario, the committed effective dose amounted to a maximum of 848 mSv, even for very unfavorable conditions. Red bone marrow equivalent doses are insufficient to induce acute radiation sickness (ARS). In the case of a smaller plume size, the ARS threshold may be exceeded in some cases. In a subway bombing, doses are much higher and the occurrence of ARS should be expected. The health hazards from a dirty bomb attack will depend on the location and the explosive device. The derived Haddon matrix indicates that preparing for such an event includes education of all the medical staff about radiation effects, the time lines of radiation damages and the treatment priorities. Further determinants of the outcome include rapid evacuation even from difficult locations, the availability of a specific triage tool to rapidly identify victims at risk for ARS, the availability of an antidote stockpile and dedicated hospital beds to treat seriously irradiated victims.

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Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification

Remote Sensing ◽

10.3390/rs12172670 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2670

Author(s):

Maria Aspri ◽

Grigorios Tsagkatakis ◽

Panagiotis Tsakalides

Keyword(s):

Resource Allocation ◽

Land Cover ◽

Network Architecture ◽

Large Scale ◽

Land Cover Classification ◽

Processing Power ◽

Proposed Model ◽

Efficient Resource ◽

Optimal Resource ◽

Machine Communication

Deep Neural Networks (DNNs) have established themselves as a fundamental tool in numerous computational modeling applications, overcoming the challenge of defining use-case-specific feature extraction processing by incorporating this stage into unified end-to-end trainable models. Despite their capabilities in modeling, training large-scale DNN models is a very computation-intensive task that most single machines are often incapable of accomplishing. To address this issue, different parallelization schemes were proposed. Nevertheless, network overheads as well as optimal resource allocation pose as major challenges, since network communication is generally slower than intra-machine communication while some layers are more computationally expensive than others. In this work, we consider a novel multimodal DNN based on the Convolutional Neural Network architecture and explore several different ways to optimize its performance when training is executed on an Apache Spark Cluster. We evaluate the performance of different architectures via the metrics of network traffic and processing power, considering the case of land cover classification from remote sensing observations. Furthermore, we compare our architectures with an identical DNN architecture modeled after a data parallelization approach by using the metrics of classification accuracy and inference execution time. The experiments show that the way a model is parallelized has tremendous effect on resource allocation and hyperparameter tuning can reduce network overheads. Experimental results also demonstrate that proposed model parallelization schemes achieve more efficient resource use and more accurate predictions compared to data parallelization approaches.

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Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

Volume 2: Biomedical and Biotechnology ◽

10.1115/imece2012-88901 ◽

2012 ◽

Author(s):

D. Keith Walters ◽

Greg W. Burgreen ◽

Robert L. Hester ◽

David S. Thompson ◽

David M. Lavallee ◽

...

Keyword(s):

Steady State ◽

Boundary Conditions ◽

Large Scale ◽

Whole Body ◽

Patient Specific ◽

Cfd Simulations ◽

Reduced Order ◽

Scale Models ◽

Steady State Simulation ◽

Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

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