Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 631-639 |
| Journal / Publication | Communications in Statistics: Simulation and Computation |
| Volume | 46 |
| Issue number | 1 |
| Online published | 10 Feb 2015 |
| Publication status | Published - 2017 |
Link(s)
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 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)
Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications. / Ling, M. H.; Wong, S. Y.; Tsui, K. L.
In: Communications in Statistics: Simulation and Computation, Vol. 46, No. 1, 2017, p. 631-639.
In: Communications in Statistics: Simulation and Computation, Vol. 46, No. 1, 2017, p. 631-639.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
