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.
The authors gratefully acknowledge funding of the NTD Modeling Consortium by the Bill and Melinda Gates Foundation [OPP1184344, OPP1186851, OPP1156227]. MGB acknowledges joint centre funding [MR/R015600/1] by the U.K. Medical Research Council (MRC) and the U.K. Department for International Development (DFID) under the MRC/DFID Concordat agreement which is also part of the European and Developing Countries Clinical Trials Partnership (EDCTP2) program supported by the European Union.
The views, opinions, assumptions, or any other information set out in this article should not be attributed to the Bill and Melinda Gates Foundation or any person connected with the Bill and Melinda Gates Foundation.
"Integrating geostatistical maps and infectious disease transmission models using adaptive multiple importance sampling." Ann. Appl. Stat. 15 (4) 1980 - 1998, December 2021. https://doi.org/10.1214/21-AOAS1486