ArticleThe annals of applied statistics2020
Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Article
- Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring.Journal of statistical planning and inference · 2022Article
- Modelling the effect of a border closure between Switzerland and Italy on the spatiotemporal spread of COVID-19 in Switzerland.Spatial statistics · 2022Article
- When did coronavirus arrive in Europe?Statistical methods & applications · 2022Article
- Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?The annals of applied statistics · 2020Article
- A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data.Frontiers in public health · 2020Article
- Joining and splitting models with Markov melding.Bayesian analysis · 2019Article
- Evidence Synthesis for Stochastic Epidemic Models.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.