The Experts below are selected from a list of 71274 Experts worldwide ranked by ideXlab platform
Akira Sasaki - One of the best experts on this subject based on the ideXlab platform.
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modeling host parasite coevolution a nested approach based on Mechanistic Models
Journal of Theoretical Biology, 2002Co-Authors: Michael A Gilchrist, Akira SasakiAbstract:In this study we introduce a Mechanistic framework for modeling host–parasite coevolution using a nested modeling approach. The first step in this approach is to construct a Mechanistic model of the parasite population dynamics within a host. The second step is to define an epidemiological model which is used to derive the fitness functions for both the host and the parasite. The within-host model is then nested within the epidemiological model by linking the epidemiological parameters such as the transmission rate of the infection or the additional host mortality rate to the dynamics of the within-host model. Nesting the within host model into an epidemiological model allows us to evaluate the fitness functions for each interactor which in turn allows us to determine the coevolutionary dynamics of the system. This nested approach has the advantage over other approaches in that Mechanistic descriptions of the host–parasite biology are used to derive, rather than impose, life-history trade-offs. We illustrate this framework by analysing a simple host–parasite system. In this particular system we find that the coevolutionary equilibrium is always stable and that host survivorship and parasite fitness vary greatly with the cost of the immune response and parasite growth. r 2002 Elsevier Science Ltd. All rights reserved.
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modeling host parasite coevolution a nested approach based on Mechanistic Models
Journal of Theoretical Biology, 2002Co-Authors: Michael A Gilchrist, Akira SasakiAbstract:In this study we introduce a Mechanistic framework for modeling host-parasite coevolution using a nested modeling approach. The first step in this approach is to construct a Mechanistic model of the parasite population dynamics within a host. The second step is to define an epidemiological model which is used to derive the fitness functions for both the host and the parasite. The within-host model is then nested within the epidemiological model by linking the epidemiological parameters such as the transmission rate of the infection or the additional host mortality rate to the dynamics of the within-host model. Nesting the within-host model into an epidemiological model allows us to evaluate the fitness functions for each interactor which in turn allows us to determine the coevolutionary dynamics of the system. This nested approach has the advantage over other approaches in that Mechanistic descriptions of the host-parasite biology are used to derive, rather than impose, life-history trade-offs. We illustrate this framework by analysing a simple host-parasite system. In this particular system we find that the coevolutionary equilibrium is always stable and that host survivorship and parasite fitness vary greatly with the cost of the immune response and parasite growth.
Aaron A King - One of the best experts on this subject based on the ideXlab platform.
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panel data analysis via Mechanistic Models
Journal of the American Statistical Association, 2019Co-Authors: Edward L Ionides, Carles Breto, Aaron A KingAbstract:AbstractPanel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a di...
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panel data analysis via Mechanistic Models
arXiv: Methodology, 2018Co-Authors: Edward L Ionides, Carles Breto, Aaron A KingAbstract:Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel Models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel Models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process Models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of Models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising for large panel datasets.
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never mind the length feel the quality the impact of long term epidemiological data sets on theory application and policy
Trends in Ecology and Evolution, 2010Co-Authors: Pejman Rohani, Aaron A KingAbstract:Infectious diseases have been a prime testing ground for ecological theory. However, the ecological perspective is increasingly recognized as essential in epidemiology. Long-term, spatially resolved reliable data on disease incidence and the ability to test them using Mechanistic Models have been critical in this cross-fertilization. Here, we review some of the key intellectual developments in epidemiology facilitated by long-term data. We identify research frontiers at the interface of ecology and epidemiology and their associated data needs.
Jorg Schultz - One of the best experts on this subject based on the ideXlab platform.
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word formation is aware of morpheme family size
PLOS ONE, 2014Co-Authors: Daniela Keller, Jorg SchultzAbstract:Words are built from smaller meaning bearing parts, called morphemes. As one word can contain multiple morphemes, one morpheme can be present in different words. The number of distinct words a morpheme can be found in is its family size. Here we used Birth-Death-Innovation Models (BDIMs) to analyze the distribution of morpheme family sizes in English and German vocabulary over the last 200 years. Rather than just fitting to a probability distribution, these Mechanistic Models allow for the direct interpretation of identified parameters. Despite the complexity of language change, we indeed found that a specific variant of this pure stochastic model, the second order linear balanced BDIM, significantly fitted the observed distributions. In this model, birth and death rates are increased for smaller morpheme families. This finding indicates an influence of morpheme family sizes on vocabulary changes. This could be an effect of word formation, perception or both. On a more general level, we give an example on how Mechanistic Models can enable the identification of statistical trends in language change usually hidden by cultural influences.
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word formation is aware of morpheme family size
PLOS ONE, 2014Co-Authors: Daniela Keller, Jorg SchultzAbstract:Words are built from smaller meaning bearing parts, called morphemes. As one word can contain multiple morphemes, one morpheme can be present in different words. The number of distinct words a morpheme can be found in is its family size. Here we used Birth-Death-Innovation Models (BDIMs) to analyze the distribution of morpheme family sizes in English and German vocabulary over the last 200 years. Rather than just fitting to a probability distribution, these Mechanistic Models allow for the direct interpretation of identified parameters. Despite the complexity of language change, we indeed found that a specific variant of this pure stochastic model, the second order linear balanced BDIM, significantly fitted the observed distributions. In this model, birth and death rates are increased for smaller morpheme families. This finding indicates an influence of morpheme family sizes on vocabulary changes. This could be an effect of word formation, perception or both. On a more general level, we give an example on how Mechanistic Models can enable the identification of statistical trends in language change usually hidden by cultural influences.
Michael A Gilchrist - One of the best experts on this subject based on the ideXlab platform.
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modeling host parasite coevolution a nested approach based on Mechanistic Models
Journal of Theoretical Biology, 2002Co-Authors: Michael A Gilchrist, Akira SasakiAbstract:In this study we introduce a Mechanistic framework for modeling host–parasite coevolution using a nested modeling approach. The first step in this approach is to construct a Mechanistic model of the parasite population dynamics within a host. The second step is to define an epidemiological model which is used to derive the fitness functions for both the host and the parasite. The within-host model is then nested within the epidemiological model by linking the epidemiological parameters such as the transmission rate of the infection or the additional host mortality rate to the dynamics of the within-host model. Nesting the within host model into an epidemiological model allows us to evaluate the fitness functions for each interactor which in turn allows us to determine the coevolutionary dynamics of the system. This nested approach has the advantage over other approaches in that Mechanistic descriptions of the host–parasite biology are used to derive, rather than impose, life-history trade-offs. We illustrate this framework by analysing a simple host–parasite system. In this particular system we find that the coevolutionary equilibrium is always stable and that host survivorship and parasite fitness vary greatly with the cost of the immune response and parasite growth. r 2002 Elsevier Science Ltd. All rights reserved.
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modeling host parasite coevolution a nested approach based on Mechanistic Models
Journal of Theoretical Biology, 2002Co-Authors: Michael A Gilchrist, Akira SasakiAbstract:In this study we introduce a Mechanistic framework for modeling host-parasite coevolution using a nested modeling approach. The first step in this approach is to construct a Mechanistic model of the parasite population dynamics within a host. The second step is to define an epidemiological model which is used to derive the fitness functions for both the host and the parasite. The within-host model is then nested within the epidemiological model by linking the epidemiological parameters such as the transmission rate of the infection or the additional host mortality rate to the dynamics of the within-host model. Nesting the within-host model into an epidemiological model allows us to evaluate the fitness functions for each interactor which in turn allows us to determine the coevolutionary dynamics of the system. This nested approach has the advantage over other approaches in that Mechanistic descriptions of the host-parasite biology are used to derive, rather than impose, life-history trade-offs. We illustrate this framework by analysing a simple host-parasite system. In this particular system we find that the coevolutionary equilibrium is always stable and that host survivorship and parasite fitness vary greatly with the cost of the immune response and parasite growth.
Carles Breto - One of the best experts on this subject based on the ideXlab platform.
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panel data analysis via Mechanistic Models
Journal of the American Statistical Association, 2019Co-Authors: Edward L Ionides, Carles Breto, Aaron A KingAbstract:AbstractPanel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a di...
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panel data analysis via Mechanistic Models
arXiv: Methodology, 2018Co-Authors: Edward L Ionides, Carles Breto, Aaron A KingAbstract:Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel Models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel Models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process Models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of Models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising for large panel datasets.
