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Activity Number: 57 - Developments in Bayesian Spatial and Spatio-Temporal Modeling of Small Area Health Data
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329421
Title: A Spatiotemporal Recommendation Engine for Malaria Control
Author(s): Qian Guan* and Brian Reich and Eric Laber
Companies: North Carolina State University and North Carolina State University and North Carlina State University
Keywords: spatiotemporal; policy; malaria

The malaria research community has made great strides in mapping disease prevalence, modeling its transmission, developing and testing effective interventions such as bed nets, and implementing these interventions in practice. We propose to build on this work to develop a real-time recommendation engine (RE) for precision interventions to help policy-makers decide how to allocate their limited resources. Because intervention effects can vary spatiotemporally and spillover into neighboring regions, targeted interventions that are optimized to maximize efficacy over the entire spatial domain are key to improving eradication efforts. The truly optimal strategy for spatiotemporal resource allocation is a massively complex mathematical function of many geographic and epidemiologic inputs, requiring extensive computation due to the large number of spatial locations under consideration, and is essentially a black box that generates no new knowledge. In contrast, we propose an approximate strategy that lays bare the contribution to the RE of various risk factors, permits straightforward computation, and is amenable to visualization, scrutiny, and stakeholder input.

Authors who are presenting talks have a * after their name.

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