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Integrating geostatistical maps and infectious disease transmission models using adaptive multiple importance sampling. (English) Zbl 1498.62249

Summary: 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62D05 Sampling theory, sample surveys
86A32 Geostatistics
92C60 Medical epidemiology

Software:

raster; mclust
PDFBibTeX XMLCite
Full Text: DOI

References:

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