INTEGRATING GEOSTATISTICAL MAPS AND INFECTIOUS DISEASE TRANSMISSION MODELS USING ADAPTIVE MULTIPLE IMPORTANCE SAMPLING

• 成果类型: Article
• 署名作者: Retkute, Renata; Touloupou, Panayiota; Basanez, Maria-Gloria; Hollingsworth, T. Deirdre; Spencer, Simon E. F.
• 署名单位: University of Cambridge; University of Birmingham; Imperial College London; Imperial College London; University of Oxford; University of Warwick; University of Warwick
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1486
• 发表日期: 2021
• 页码: 1980-1998
• 关键词: africa; onchocerciasis; elimination; PREVALENCE; frequency; inference

摘要

The Adaptive Multiple Importance Sampling algorithm (AMIS) is an iterative technique which recycles samples from all previous iterations in order to improve the efficiency of the proposal distribution. We have formulated a new statistical framework, based on AMIS, to take the output from a geostatistical model of infectious disease prevalence, incidence or relative risk, and project it forward in time under a mathematical model for transmission dynamics. We adapted the AMIS algorithm so that it can sample from multiple targets simultaneously by changing the focus of the adaptation at each iteration. By comparing our approach against the standard AMIS algorithm, we showed that these novel adaptations greatly improve the efficiency of the sampling. We tested the performance of our algorithm on four case studies: ascariasis in Ethiopia, onchocerciasis in Togo, human immunodeficiency virus (HIV) in Botswana, and malaria in the Democratic Republic of the Congo.