Efficient heterogeneous sampling for stochastic simulation with an illustration in healthcare applications

Man Ho Alpha LING, Shui Yee Zoie WONG, Kwok Leung TSUI

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Abstract

In modeling disease transmission, contacts are assumed to have different infection rates. A proper simulation must model the heterogeneity in the transmission rates. In this paper, we present a computationally efficient algorithm that can be applied to a population with heterogeneous transmission rates. We conducted a simulation study to show that the algorithm is more efficient than other algorithms for sampling the disease transmission in a subset of the heterogeneous population. We use a valid stochastic model of pandemic influenza to illustrate the algorithm and to estimate the overall infection attack rates of influenza A (H1N1) in a Canadian city. Copyright © 2017 Taylor & Francis Group, LLC.
Original languageEnglish
Pages (from-to)631-639
JournalCommunications in Statistics - Simulation and Computation
Volume46
Issue number1
Early online dateFeb 2015
DOIs
Publication statusPublished - 2017

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Sampling
Stochastic models
Set theory

Citation

Ling, M. H., Wong, S. Y., & Tsui, K. L. (2017). Efficient heterogeneous sampling for stochastic simulation with an illustration in healthcare applications. Communications in Statistics - Simulation and Computation, 46(1), 631-639.

Keywords

  • Stochastic simulation
  • Infectious disease
  • Transmission dynamic
  • SEIR model
  • Age-dependent heterogeneity
  • Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications