EFFICIENT REAL-TIME MONITORING OF AN EMERGING INFLUENZA PANDEMIC: HOW FEASIBLE?

• Published in: The annals of applied statistics 2020-03, Vol.14 (1), p.74-93
• Main Authors: Birrell, Paul J., Wernisch, Lorenz, Tom, Brian D. M., Held, Leonhard, Roberts, Gareth O., Pebody, Richard G., De Angelis, Daniela
• Format: Article
• Language: English
• ISSN: 1932-6157; 1941-7330
• DOI: 10.1214/19-AOAS1278

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.