Optimal treatment allocations in space and time for online control of anemerging infectious disease

Journal of the Royal Statistical Society. Series C: Applied Statistics
By: , and 

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Abstract

A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up‐to‐date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at outbreak. We derive a Bayesian on‐line estimator of the optimal allocation strategy that combines simulation–optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Optimal treatment allocations in space and time for online control of anemerging infectious disease
Series title Journal of the Royal Statistical Society. Series C: Applied Statistics
DOI 10.1111/rssc.12266
Volume 67
Issue 4
Year Published 2019
Language English
Publisher Wiley
Contributing office(s) Coop Res Unit Atlanta
Description 45 p.
First page 743
Last page 789