Stochastic and deterministic models for agricultural production networks

Bai, Ping; Banks, Harvey Thomas; Dediu, Sava; Govan, Anjela; Lloyd, Alun L.; Nguyen, Hoan K.; Olufsen, Mette S.; Rempała, Grzegorz A.; Slenning, Barrett D.

doi:10.3934/mbe.2007.4.373

Cited by 26 publications

(2 citation statements)

References 40 publications

(53 reference statements)

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“…Thomaseth and Cobeli extended the classical sensitivity functions to 'generalized sensitivity functions' (GSFs) which assess information gain about the parameters from the measurements. This method has been widely used to assess identifiability of dynamical systems [10,[17][18][19][20], where regions of high information gain show a sharp increase in the GSFs while oscillations imply correlation with other parameters. There are two drawbacks of GSFs: first, that they are designed to start at 0 and end at 1, which leads to the so-called 'force-toone' phenomenon, where even in the absence of information about the parameters the GSFs are forces to end at a value of 1; and second, oscillations in GSFs can be hard to interpret in terms of identifying which sets of parameters are correlated.…”

Section: Introductionmentioning

confidence: 99%

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

Pant¹

2018

J. R. Soc. Interface.

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A new class of functions, called the ‘information sensitivity functions’ (ISFs), which quantify the information gain about the parameters through the measurements/observables of a dynamical system are presented. These functions can be easily computed through classical sensitivity functions alone and are based on Bayesian and information-theoretic approaches. While marginal information gain is quantified by decrease in differential entropy, correlations between arbitrary sets of parameters are assessed through mutual information. For individual parameters, these information gains are also presented as marginal posterior variances, and, to assess the effect of correlations, as conditional variances when other parameters are given. The easy to interpret ISFs can be used to (a) identify time intervals or regions in dynamical system behaviour where information about the parameters is concentrated; (b) assess the effect of measurement noise on the information gain for the parameters; (c) assess whether sufficient information in an experimental protocol (input, measurements and their frequency) is available to identify the parameters; (d) assess correlation in the posterior distribution of the parameters to identify the sets of parameters that are likely to be indistinguishable; and (e) assess identifiability problems for particular sets of parameters.

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Section: Introductionmentioning

confidence: 99%

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

Pant¹

2018

J. R. Soc. Interface.

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“…We define the system sensitivity in a way that differs from previous authors (41). Thus, rather than defining it as the 2- norm of all individual sensitivities to a given parameter, we define it here as the geometric mean of the individual sensitivities.…”

Section: Models and Methodsmentioning

confidence: 99%

A Sensitivity Analysis Comparison of Three Models for the Dynamics of Germinal Centers

et al. 2019

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Germinal centers (GCs) are transient anatomical microenvironments where antibody affinity maturation and memory B cells generation takes place. In the past, models of Germinal Center (GC) dynamics have focused on understanding antibody affinity maturation rather than on the main mechanism(s) driving their rise-and-fall dynamics. Here, based on a population dynamics model core, we compare three mechanisms potentially responsible for this GC biphasic behavior dependent on follicular dendritic cell (FDC) maturation, follicular T helper (Tfh) cell maturation, and antigen depletion. Analyzing the kinetics of B and T cells, as well as its parameter sensitivities, we found that only the FDC-maturation-based model could describe realistic GC dynamics, whereas the simple Tfh-maturation and antigen-depletion mechanisms, as implemented here, could not. We also found that in all models the processes directly related to Tfh cell kinetics have the highest impact on GC dynamics. This suggests the existence of some still unknown mechanism(s) tuning GC dynamics by affecting Tfh cell response to proliferation-inducing stimuli.

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Economic Impact of a Livestock Attack

Hagerman

McCarl

2008

Wiley Handbook of Science and Technology for Homeland Security

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This article describes the importance and challenges of modeling the economics of attacks to livestock agriculture through the introduction of an animal or zoonotic disease. The value of including economic evaluation is presented. The various types of economic impact categories needed to do a disease assessment are discussed, as well as what determines the severity of impacts in a particular situation. The article concludes with a short discussion of how to determine which economic loss categories to focus on, and the need for additional focus on key loss areas.

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Stochastic and deterministic models for agricultural production networks

Cited by 26 publications

References 40 publications

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

A Sensitivity Analysis Comparison of Three Models for the Dynamics of Germinal Centers

Economic Impact of a Livestock Attack

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