Journal
ANNALS OF APPLIED STATISTICS
Volume 15, Issue 4, Pages 1980-1998Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/21-AOAS1486
Keywords
Epidemiology; Disease mapping; Parameter estimation; Importance sampling
Categories
Funding
- NTD Modeling Consortium by the Bill and Melinda Gates Foundation [OPP1184344, OPP1186851, OPP1156227, MR/R015600/1]
- U.K. Medical Research Council (MRC) [MR/R015600/1]
- U.K. Department for International Development (DFID) under the MRC/DFID Concordat agreement
- European Union
- Bill and Melinda Gates Foundation [OPP1156227] Funding Source: Bill and Melinda Gates Foundation
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The AMIS algorithm is an iterative technique that improves the efficiency of the proposal distribution by recycling samples from previous iterations. A new statistical framework based on AMIS is used to project disease prevalence or incidence under a mathematical model for transmission dynamics, with novel adaptations significantly enhancing sampling efficiency. The algorithm was tested on various infectious diseases in different countries, demonstrating its effectiveness.
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.
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