Integrating Geostatistical Maps and Infectious Disease Ttransmission Models Using Adaptive Multiple Importance Sampling

• Activity Number: 8 - The Best of AOAS
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Renata Retkute* and Panayiota Touloupou and Maria-Gloria Basanez and Simon E.F. Spencer and Christopher A Gilligan
• Companies: University of Cambridge and University of Birmingham and Imperial College London and University of Warwick and University of Cambridge
• Keywords: Disease transmission; Geostatistical maps; Importance Sampling

Abstract

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. A range of current AMIS applications to human and plant diseases will be discussed.