Modeling and understanding virus spreading is a crucial issue in computer security. Epidemiological models have been proposed to deal with this problem. We investigate the dynamics of computer virus spreading by considering an stochastic susceptible-infected-removed-susceptible (SIRS) model where immune computers send warning signals to reduce the propagation of the virus among the rest of the computers in the network. We perform an exhaustive analysis of the main indicators of the spread and persistence of the infection. To this end, we provide a detailed study of the quasi-stationary distribution, the number of cases of infection, the extinction time and the hazard time.

Stochastic modeling of computer virus spreading with warning signals

https://www.mssanz.org.au/modsim05/papers/smajgl.pdf

Water policy impact assessment – combining modelling techniques in the Great Barrier Reef region

There is more to modeling than the act of modeling. To be an effective modeler requires understanding the different types of models and when one type of model is more appropriate than another, how to select a model from a family of similar models, how to evaluate a model’s goodness of fit, how to present modeling results both verbally and graphically to others, and to do all these tasks within an ethical framework. This chapter lays the groundwork for the rest of the book and covers these topics with an eye toward applying these concepts across the other chapters.

The Art of Modeling

A world wide web-based grazing simulation model (GRASIM).

Codeine's metabolic fate in the body is complex, and detailed quantitative knowledge of it, and that of its metabolites is lacking among prescribers. We aimed to develop a codeine pharmacokinetic pathway model for codeine and its metabolites that incorporates the effects of genetic polymorphisms. We studied the phenotype-specific time courses of plasma codeine, codeine-6-glucoronide, morphine, morphine-3-glucoronide, and morphine-6-glucoronide. A codeine pharmacokinetic pathway model accurately fit the time courses of plasma codeine and its metabolites. We used this model to build a population pharmacokinetic codeine pathway model. The population model indicated that about 10% of a codeine dose was converted to morphine in poor-metabolizer phenotype subjects. The model also showed that about 40% of a codeine dose was converted to morphine in EM subjects, and about 51% was converted to morphine in ultrarapid-metabolizers. The population model further indicated that only about 4% of MO formed from codeine was converted to morphine-6-glucoronide in poor-metabolizer phenotype subjects. The model also showed that about 39% of the MO formed from codeine was converted to morphine-6-glucoronide in extensive-metabolizer phenotypes, and about 58% was converted in ultrarapid-metabolizers. We conclude, a population pharmacokinetic codeine pathway model can be useful because beyond helping to achieve a quantitative understanding the codeine and MO pathways, the model can be used for simulation to answer questions about codeine's pharmacogenetic-based disposition in the body. Our study suggests that pharmacogenetics for personalized dosing might be most effectively advanced by studying the interplay between pharmacogenetics, population pharmacokinetics, and clinical pharmacokinetics.

CYP2D6 Phenotype-Specific Codeine Population Pharmacokinetics

Models are tools that are used to predict the structure and the behavior of a system. They are usually developed to fit a particular set of data, however, once developed, they can be applied to other studies; to predict the behavior of the system under different conditions, explore the structure of parts of the system that were not sampled, identify areas that are lacking in data, and optimize the design of new studies. For published models to be interpreted and applied widely to other studies, models need to be communicated clearly. Papers describing models should contain full descriptions of the physiological relevance and mathematical rigor of the model. A working version of the model then needs to be made available to other investigators. This manuscript examines the areas of communicating and interpreting published models and describes a facility, called a ‘model library’, that is being developed to make published models available on the Internet. With better communication, information in models can be extracted and used to bridge the gap between theory and experiment to extend our understanding of complex systems and optimize the use of experimental resources.

X.Q. Wang

Papers

Publishing, interpreting, and accessing models