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.
|Journal||Communications in Statistics - Simulation and Computation|
|Early online date||Feb 2015|
|Publication status||Published - 2017|
CitationLing, 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.
- Stochastic simulation
- Infectious disease
- Transmission dynamic
- SEIR model
- Age-dependent heterogeneity
- Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications