Parallel Low Discrepancy Parameter Sweep for Public Health Policy

作者：Sudheer Chunduri, Meysam Ghaffari, Mehran Sadeghi Lahijani, Ashok Srinivasan, Sirish Namilae · 发表于：EasyChair preprint · 年份：2018 · DOI：10.29007/vp3d · 被引用次数：4 · 研究领域：Statistical Distribution Estimation and Applications

Numerical simulations are used to analyze the effectiveness of alternate public policy choices in limiting the spread of infections. In practice, it is usually not feasible to predict their precise impact due to inherent uncertainties, especially at the early stages of an epidemic. One option is to parameterize the sources of uncertainty and carry out a parameter sweep to identify their robustness under a variety of possible scenarios. The SPED model has used this approach successfully to analyze the robustness of different airline boarding and deplaning procedures. However, the time taken by this approach is too large to answer questions raised during the course of a decision meeting. In this paper, we use a modified approach that pre-computes simulations of passenger movement, performing only the disease-specific analysis in real time. A novel contribution of this paper lies in using a low discrepancy sequence (LDS) in the parameter sweep and demonstrating that it can lead to a reduction in analysis time by one to three orders in magnitude over the conventional lattice-based parameter sweep. However, its parallelization suffers from greater load imbalance than the conventional approach. We examine this and relate it to number-theoretic properties of the LDS. We then propose solutions to this problem. Our approach and analysis are applicable to other parameter sweep problems too. The primary contributions of this paper lie in the new approach of low discrepancy parameter swe...