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- Biosecurity (2)
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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Modeling The Optimum Duration Of Antibiotic Prophylaxis In An Anthrax Outbreak, Ron Brookmeyer, Elizabeth Johnson, Robert Bollinger
Modeling The Optimum Duration Of Antibiotic Prophylaxis In An Anthrax Outbreak, Ron Brookmeyer, Elizabeth Johnson, Robert Bollinger
Ron Brookmeyer
A critical consideration in effective and measured public health responses to an outbreak of inhalational anthrax is the optimum duration of antibiotic prophylaxis. We develop a competing-risks model to address the duration of antibiotic prophylaxis and the incubation period that accounts for the risks of spore germination and spore clearance. The model predicts the incubation period distribution, which is confirmed by empirical data. The optimum duration of antibiotic prophylaxis depends critically on the dose of inhaled spores. At high doses, we show that exposed persons would need to remain on antibiotic prophylaxis for at least 4 months, and considerable morbidity …
Statistical Models And Bioterrorism: Application To The U.S. Anthrax Outbreak, Ron Brookmeyer, Natalie Blades
Statistical Models And Bioterrorism: Application To The U.S. Anthrax Outbreak, Ron Brookmeyer, Natalie Blades
Ron Brookmeyer
In the fall of 2001 an outbreak of inhalational anthrax occurred in the United States that was the result of bioterrorism. Letters contaminated with anthrax spores were sent through the postal system. In response to the outbreak, public health officials treated over 10,000 persons with antibiotic prophylaxis in the hopes of preventing further morbidity and mortality. No persons receiving the antibiotics subsequently developed disease. The question arises as to how many cases of disease may actually have been prevented by the public health intervention of antibiotic prophylaxis. A statistical model is developed to answer this question by relating to the …
Assessment Of Model Development Techniques And Evaluation Methods For Binary Classification In The Credit Industry, Satish Nargundkar, Jennifer Priestley
Assessment Of Model Development Techniques And Evaluation Methods For Binary Classification In The Credit Industry, Satish Nargundkar, Jennifer Priestley
Jennifer L. Priestley
We examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs associated with misclassification.
Absorptive Capacity, Causal Ambiguity And Outcome Ambiguity: The Network Effect And Knowledge Transfer Difficulty Among Four Network Forms, Subhashish Samaddar, Jennifer Priestley
Absorptive Capacity, Causal Ambiguity And Outcome Ambiguity: The Network Effect And Knowledge Transfer Difficulty Among Four Network Forms, Subhashish Samaddar, Jennifer Priestley
Jennifer L. Priestley
No abstract is currently available.
Comparison Between Analytical Probabilistic And Computer Simulation Models, Using Current Design Examples, Barry A. Palynchuk Phd
Comparison Between Analytical Probabilistic And Computer Simulation Models, Using Current Design Examples, Barry A. Palynchuk Phd
Barry A. Palynchuk PhD
Closed form analytical expressions have been developed to solve several basic problems related to the hydrologic design of storm water management facilities. The collection of these analytical expressions is referred to as the Analytical Probabilistic Storm Water Models (APSWM). APSWM can be used as an alternative to the design storm simulation models in the design and analysis of storm water management facilities. In this paper, a comparison is made between APSWM and design storm simulation models as applied to an actual design case. Conventional outputs such as runoff volume, peak discharge, and peak discharge from detention ponds are reviewed and …
Development Of A Regression Model For Estimating The Effects Of Assumption Violations On Type I Error Rates In The Student's T Test: Implications For Practitioners, John Fraas, Isadore Newman, Rosalie J. Hall
Development Of A Regression Model For Estimating The Effects Of Assumption Violations On Type I Error Rates In The Student's T Test: Implications For Practitioners, John Fraas, Isadore Newman, Rosalie J. Hall
John W. Fraas
No abstract provided.
Ordinary Least Squares Regression, Discriminant Analysis,And Logisitic Regression: Questions Researchers And Practioners Should Address When Selecting An Analytic Technique, John Fraas, Isadore Newman
Ordinary Least Squares Regression, Discriminant Analysis,And Logisitic Regression: Questions Researchers And Practioners Should Address When Selecting An Analytic Technique, John Fraas, Isadore Newman
John W. Fraas
No abstract provided.
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Michael Stanley Smith
With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something …
Long-Run Models Of Oil Stock Prices, Alessandro Lanza, Matteo Manera, Massimo Giovannini, Margherita Grasso
Long-Run Models Of Oil Stock Prices, Alessandro Lanza, Matteo Manera, Massimo Giovannini, Margherita Grasso
Matteo Manera
The identification of the forces that drive oil stock prices is extremely important given the size of the Oil&Gas industry and its links with the energy sector and the environment. In the next decade oil companies will have to deal with international policies to contrast climate change. This issue is likely to affect companies’ shareholder values. In this paper we focus on the long-run financial determinants of the stock prices of six major oil companies (Bp, Chevron-Texaco, Eni, Exxon-Mobil, Royal Dutch Shell, Total-Fina-Elf) using multivariate cointegration techniques and vector error correction models. Weekly oil stock prices are analyzed together with …