Bayesian inference for stochastic multitype epidemics in structured populations via random graphs
Bayesian inference for stochastic multitype epidemics in structured populations via random graphs
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DOI：
10.1111/j.1467-9868.2005.00524.x
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发表时间：
2005-01-01
影响因子：
5.8
通讯作者：
O'Neill, PD
中科院分区：
文献类型：
--
作者：
Demiris, N;O'Neill, PD
本文研究了用结构化群体随机流行病模型对最终结果传染病数据进行统计推断的新方法。对这种模型进行推断的一个主要障碍是，这种可能性在分析和数值上都是难以处理的。这里采用的方法是以描述个体之间潜在传染性接触的随机图的形式来估算缺失的信息。这种估算水平克服了现有方法的各种限制，并产生了关于疾病传播的更详细的信息。用真实的和试验数据说明了该方法。
The paper is concerned with new methodology for statistical inference for final outcome infectious disease data using certain structured population stochastic epidemic models. A major obstacle to inference for such models is that the likelihood is both analytically and numerically intractable. The approach that is taken here is to impute missing information in the form of a random graph that describes the potential infectious contacts between individuals. This level of imputation overcomes various constraints of existing methodologies and yields more detailed information about the spread of disease. The methods are illustrated with both real and test data.