Bayesian inference for stochastic multitype epidemics in structured populations via random graphs

• 成果类型: Article
• 署名作者: Demiris, N; O'Neill, PD
• 署名单位: University of Nottingham; MRC Biostatistics Unit
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2005.00524.x
• 发表日期: 2005
• 页码: 731-745
• 关键词: household; transmission; community; disease; infection; MODEL

摘要

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.