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- W2313872813 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." @default.
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- W2313872813 date "2015-02-10" @default.
- W2313872813 modified "2023-10-01" @default.
- W2313872813 title "Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications" @default.
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- W2313872813 doi "https://doi.org/10.1080/03610918.2014.977914" @default.
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