All of the costs associated with decontamination following the 2001 anthrax letter attacks were summarized, estimated, and aggregated based on existing literature and news media reports. A comprehensive list of all affected structures was compiled. Costs were analyzed by building class and decontamination type. Sampling costs and costs of worker relocation were also included. Our analysis indicates that the total cost associated with decontamination was about $320 million.

It is well established in the epidemiological literature that individual behaviors have a significant effect on the spread of infectious diseases. Agent-based models are increasingly being recognized as the next generation of epidemiological models. In this research, we use the ability of agent-based models to incorporate behavior into simulations by examining the relative importance of vaccination and social distancing, two common measures for controlling the spread of infectious diseases, with respect to seasonal influenza. We modeled health behaviour using the result of a Health Belief Model study focused on influenza. We considered a control and a treatment group to explore the effect of education on people's health-related behaviors patterns. The control group reflects the behavioral patterns of students based on their general knowledge of influenza and its interventions while the treatment group illustrates the level of behavioral changes after individuals have been educated by a health care expert. The results of this study indicate that self-initiated behaviors are successful in controlling an outbreak in a high contact rate location such as a university. Self-initiated behaviors resulted in a population attack rate decrease of 17% and a 25% reduction in the peak number of cases. The simulation also provides significant evidence for the effect of an HBM theory-based educational program to increase the rate of applying the target interventions (vaccination by 22% percent and social distancing by 41%) and consequently to control the outbreak.

The use of wireless devices around critical-care medical equipment poses the risk that the electric field strength will exceed the immunity of the equipment, potentially resulting in equipment failure and possibly harming the patient. As medical staff and visitors carrying wireless transmitters move through the hospital, there may be times when number and type of transmitters near a medical device produce an electric field that exceeds the equipment's immunity level. This paper presents a method for estimating the risk that the immunity level will be exceeded from wireless transmitters carried by medical staff throughout a 24-hour period. The method is applied to a simplified scenario of three patients in beds and twelve transmitter locations surrounding the beds, with eight medical staff on the floor. The risk of electromagnetic interference can be reduced with a suitable management policy. This paper describes a method for evaluating the usefulness of a management policy quantitatively, and then compares three policies: unrestricted use of wireless devices, restricted use, and a ban on wireless devices.

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Bayesian hierarchical models are built to fit multiple health endpoints from a doseresponse study of a chemical contaminant, perchlorate. Perchlorate exposure results in iodine uptake inhibition in the thyroid, with health effects manifested by changes in blood hormone concentrations and histopathological effects on the thyroid. We propose empirical models to fit blood hormone concentration and thyroid histopathology data for rats exposed to perchlorate in the 90-day study of Springborn Laboratories Inc. (1998), based upon a mechanistic model derived from the assumed toxicological relationships between dose and the various endpoints. All of the models are fit in a Bayesian framework, and predictions about each endpoint in response to dose are simulated based on the posterior predictive distribution. A hierarchical model tries to exploit possible similarities between different combinations of sex and exposure duration, and it allows us to produce more stable estimates of dose-response curves. We also illustrate how the Bayesian model specification allows us to address additional questions that arise after the analysis.