Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

Paul J Birrell; Lorenz Wernisch; Brian D M Tom; Leonhard Held; Gareth O Roberts; Richard G Pebody; Daniela De Angelis

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arXiv · 1608.05292

Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

Paul J Birrell·Lorenz Wernisch·Brian D M Tom·Leonhard Held·Gareth O Roberts·Richard G Pebody·Daniela De Angelis

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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 non-identifiability.

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Paul J Birrell, Lorenz Wernisch, Brian D M Tom, Leonhard Held, Gareth O Roberts, Richard G Pebody, Daniela De Angelis. 2016-08-18. Efficient real-time monitoring of an emerging influenza epidemic: how feasible?. https://arxiv.org/abs/1608.05292

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