The Separatrix Algorithm for Synthesis and Analysis of Stochastic Simulations with Applications in Disease Modeling
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Klein-2014-The separatrix algor.pdf
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2.72 MB
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Author(s)
Klein, Daniel J.
Baym, Michael Hartmann
Eckhoff, Philip
Date Issued
July 2014
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Klein, Daniel J., Michael Baym, and Philip Eckhoff. “The Separatrix Algorithm for Synthesis and Analysis of Stochastic Simulations with Applications in Disease Modeling.” Edited by Guido Germano. PLoS ONE 9, no. 7 (July 31, 2014): e103467.
Version
Final published version
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 2018), 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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0103467
