Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 14, Number 1 (2020), 74-93.
Efficient real-time monitoring of an emerging influenza pandemic: How feasible?
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.
Ann. Appl. Stat., Volume 14, Number 1 (2020), 74-93.
Received: June 2018
Revised: March 2019
First available in Project Euclid: 16 April 2020
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Birrell, Paul J.; Wernisch, Lorenz; Tom, Brian D. M.; Held, Leonhard; Roberts, Gareth O.; Pebody, Richard G.; De Angelis, Daniela. Efficient real-time monitoring of an emerging influenza pandemic: How feasible?. Ann. Appl. Stat. 14 (2020), no. 1, 74--93. doi:10.1214/19-AOAS1278. https://projecteuclid.org/euclid.aoas/1587002665
- Efficient real-time monitoring of an emerging influenza epidemic: How feasible? Web appendix. Additional supporting tables, plots and mathematical detail omitted from the main manuscript for brevity.