Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • M. H. Ling
  • S. Y. Wong
  • K. L. Tsui


Original languageEnglish
Pages (from-to)631-639
Journal / PublicationCommunications in Statistics: Simulation and Computation
Issue number1
Online published10 Feb 2015
Publication statusPublished - 2017


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 article, 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.

Research Area(s)

  • Age-dependent heterogeneity, Infectious disease, SEIR model, Stochastic simulation, Transmission dynamic

Citation Format(s)