Synopsis
Recent outbreak of Human Influenza A H1N1 virus infection commands statistics playing an important role on guidance of prevention and treatment. Viral Dynamic Model, a set of ordinary differential equations (ODE) which describes interaction between virus and the immune system, has been proved useful in understanding the pathogenesis of virus infection and developing treatment strategy for many viral infection diseases, such as HIV, HCV, HBV and so on. In order to estimate biological/clinical meaningful parameters in various dynamic models, many statistical approaches have been developed in the last decade, from simple nonlinear least square (NLS) approach to more general nonlinear Mixed-effect modeling approach. However, for a general nonlinear ODE model, no close form solution is available and it has to be solved numerically. In such a situation, a general approach has to be developed to deal with this complexity. Two iomarkers, viral load and number of immune cells, are critical data source for dynamical models.
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About the Author
Dr. Lu is an expert in longitudinal data analysis, dynamic network modeling and Bayesian modeling. He has developed various statistical methods and applied them to infectious disease modeling and other dynamic processes. He has published many articles in prestigious journals such as Journal of the American Statistical Association and Biometrics.
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Statistical Methods for Dynamic Models with Application
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recent outbreak of Human Influenza A H1N1 virus infection commands statistics playing an important role on guidance of prevention and treatment. Viral Dynamic Model, a set of ordinary differential equations (ODE) which describes interaction between virus and the immune system, has been proved useful in understanding the pathogenesis of virus infection and developing treatment strategy for many viral infection diseases, such as HIV, HCV, HBV and so on. In order to estimate biological/clinical meaningful parameters in various dynamic models, many statistical approaches have been developed in the last decade, from simple nonlinear least square (NLS) approach to more general nonlinear Mixed-effect modeling approach. However, for a general nonlinear ODE model, no close form solution is available and it has to be solved numerically. In such a situation, a general approach has to be developed to deal with this complexity. Two iomarkers, viral load and number of immune cells, are critical data source for dynamical models. 100 pp. Englisch. Seller Inventory # 9783659537103
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Lu TaoDr. Lu is an expert in longitudinal data analysis, dynamic network modeling and Bayesian modeling. He has developed various statistical methods and applied them to infectious disease modeling and other dynamic processes. He has. Seller Inventory # 158596148
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recent outbreak of Human Influenza A H1N1 virus infection commands statistics playing an important role on guidance of prevention and treatment. Viral Dynamic Model, a set of ordinary differential equations (ODE) which describes interaction between virus and the immune system, has been proved useful in understanding the pathogenesis of virus infection and developing treatment strategy for many viral infection diseases, such as HIV, HCV, HBV and so on. In order to estimate biological/clinical meaningful parameters in various dynamic models, many statistical approaches have been developed in the last decade, from simple nonlinear least square (NLS) approach to more general nonlinear Mixed-effect modeling approach. However, for a general nonlinear ODE model, no close form solution is available and it has to be solved numerically. In such a situation, a general approach has to be developed to deal with this complexity. Two iomarkers, viral load and number of immune cells, are critical data source for dynamical models.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. Seller Inventory # 9783659537103
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Statistical Methods for Dynamic Models with Application
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recent outbreak of Human Influenza A H1N1 virus infection commands statistics playing an important role on guidance of prevention and treatment. Viral Dynamic Model, a set of ordinary differential equations (ODE) which describes interaction between virus and the immune system, has been proved useful in understanding the pathogenesis of virus infection and developing treatment strategy for many viral infection diseases, such as HIV, HCV, HBV and so on. In order to estimate biological/clinical meaningful parameters in various dynamic models, many statistical approaches have been developed in the last decade, from simple nonlinear least square (NLS) approach to more general nonlinear Mixed-effect modeling approach. However, for a general nonlinear ODE model, no close form solution is available and it has to be solved numerically. In such a situation, a general approach has to be developed to deal with this complexity. Two iomarkers, viral load and number of immune cells, are critical data source for dynamical models. Seller Inventory # 9783659537103
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Statistical Methods for Dynamic Models with Application
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Taschenbuch. Condition: Neu. Statistical Methods for Dynamic Models with Application | Tao Lu | Taschenbuch | 100 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659537103 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 107817416
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