On parameter estimation in population models II: Multi-dimensional processes and transient dynamics
On parameter estimation in population models II: Multi-dimensional processes and transient dynamics
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DOI：
10.1016/j.tpb.2008.12.002
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发表时间：
2009-03-01
影响因子：
1.4
通讯作者：
Pollett, P. K.
中科院分区：
文献类型：
--
作者：
Ross, J. V.;Pagendam, D. E.;Pollett, P. K.
最近，提出了一种计算效率高的方法，用于从离散采样的丰度数据校准广泛的一类马尔可夫过程。该方法被示出相对于一维过程，并需要假设平稳。在这里，我们表明，该方法可以直接扩展到多维过程，并开发了两个类似的非平稳过程的计算效率高的方法。这些方法说明疾病和人口模型，包括应用传染计数数据从爆发的“俄罗斯流感”（A/苏联/1977年H1N1）在教育机构。该方法还显示出提供了一种有效、简单而又严格的方法来校准具有γ分布感染期的疾病过程。(C)2009 Elsevier Inc. All rights reserved.
Recently, a computationally-efficient method was presented for calibrating a wide-class of Markov processes from discrete-sampled abundance data. The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity. Here we demonstrate that the approach may be directly extended to multi-dimensional processes, and two analogous computationally-efficient methods for non-stationary processes are developed. These methods are illustrated with respect to disease and Population models, including application to infectious Count data from an outbreak of "Russian influenza" (A/USSR/1977 H1N1) in an educational institution. The methodology is also shown to provide art efficient, simple and yet rigorous approach to calibrating disease processes with gamma-distributed infectious period. (C) 2009 Elsevier Inc. All rights reserved.