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The Separatrix Algorithm for synthesis and analysis of stochastic simulations with applications in disease modeling.

Klein DJ, Baym M, Eckhoff P - PLoS ONE (2014)

Bottom Line: Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties.Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice.The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria.

View Article: PubMed Central - PubMed

Affiliation: Institute for Disease Modeling, Bellevue, Washington, United States of America.

ABSTRACT
Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties. To facilitate the decision making process in the context of a goal-oriented objective (e.g., eradicate polio by [Formula: see text]), stochastic models can be used to map the probability of achieving the goal as a function of parameters. Each run of a stochastic model can be viewed as a Bernoulli trial in which "success" is returned if and only if the goal is achieved in simulation. However, each run can take a significant amount of time to complete, and many replicates are required to characterize each point in parameter space, so specialized algorithms are required to locate desirable interventions. To address this need, we present the Separatrix Algorithm, which strategically locates parameter combinations that are expected to achieve the goal with a user-specified probability of success (e.g. 95%). Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice. The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria.

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One-dimensional hyperbolic tangent performance.For the one-dimensional hyperbolic tangent test function (18), the Separatrix Algorithm outperforms Latin hypercube sampling and traditional BDOE on a likelihood-based performance metric (19).
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pone-0103467-g003: One-dimensional hyperbolic tangent performance.For the one-dimensional hyperbolic tangent test function (18), the Separatrix Algorithm outperforms Latin hypercube sampling and traditional BDOE on a likelihood-based performance metric (19).

Mentions: The function denoted by in the denominator of the log is the beta function. For this one-dimensional example, we used a single evaluation point on the separatrix, . The Separatrix Algorithm outperforms LHS and traditional BDOE, as can be seen in Figure 3.


The Separatrix Algorithm for synthesis and analysis of stochastic simulations with applications in disease modeling.

Klein DJ, Baym M, Eckhoff P - PLoS ONE (2014)

One-dimensional hyperbolic tangent performance.For the one-dimensional hyperbolic tangent test function (18), the Separatrix Algorithm outperforms Latin hypercube sampling and traditional BDOE on a likelihood-based performance metric (19).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4117517&req=5

pone-0103467-g003: One-dimensional hyperbolic tangent performance.For the one-dimensional hyperbolic tangent test function (18), the Separatrix Algorithm outperforms Latin hypercube sampling and traditional BDOE on a likelihood-based performance metric (19).
Mentions: The function denoted by in the denominator of the log is the beta function. For this one-dimensional example, we used a single evaluation point on the separatrix, . The Separatrix Algorithm outperforms LHS and traditional BDOE, as can be seen in Figure 3.

Bottom Line: Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties.Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice.The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria.

View Article: PubMed Central - PubMed

Affiliation: Institute for Disease Modeling, Bellevue, Washington, United States of America.

ABSTRACT
Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties. To facilitate the decision making process in the context of a goal-oriented objective (e.g., eradicate polio by [Formula: see text]), stochastic models can be used to map the probability of achieving the goal as a function of parameters. Each run of a stochastic model can be viewed as a Bernoulli trial in which "success" is returned if and only if the goal is achieved in simulation. However, each run can take a significant amount of time to complete, and many replicates are required to characterize each point in parameter space, so specialized algorithms are required to locate desirable interventions. To address this need, we present the Separatrix Algorithm, which strategically locates parameter combinations that are expected to achieve the goal with a user-specified probability of success (e.g. 95%). Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice. The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria.

Show MeSH
Related in: MedlinePlus