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

The Adaptive Multiple Importance Sampling algorithm (AMIS) is an it-erative 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 geosta-tistical model of infectious disease prevalence, incidence or relative risk, and project it forward in time under a mathematical model for transmission dy-namics. We adapted the AMIS algorithm so that it can sample from multiple targets simultaneously by changing the focus of the adaptation at each itera-tion. By comparing our approach against the standard AMIS algorithm, we showed that these novel adaptations greatly improve the efficiency of the sam-pling. We tested the performance of our algorithm on four case studies: as-cariasis in Ethiopia, onchocerciasis in Togo, human immunodeficiency virus (HIV) in Botswana, and malaria in the Democratic Republic of the Congo.