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March 2020 Efficient real-time monitoring of an emerging influenza pandemic: How feasible?
Paul J. Birrell, Lorenz Wernisch, Brian D. M. Tom, Leonhard Held, Gareth O. Roberts, Richard G. Pebody, Daniela De Angelis
Ann. Appl. Stat. 14(1): 74-93 (March 2020). DOI: 10.1214/19-AOAS1278

Abstract

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here, we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter nonidentifiability.

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Paul J. Birrell. Lorenz Wernisch. Brian D. M. Tom. Leonhard Held. Gareth O. Roberts. Richard G. Pebody. Daniela De Angelis. "Efficient real-time monitoring of an emerging influenza pandemic: How feasible?." Ann. Appl. Stat. 14 (1) 74 - 93, March 2020. https://doi.org/10.1214/19-AOAS1278

Information

Received: 1 June 2018; Revised: 1 March 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200162
MathSciNet: MR4085084
Digital Object Identifier: 10.1214/19-AOAS1278

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 1 • March 2020
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