ArticleStatistical methods in medical research2019
Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020Pooled it
- Compartmental modeling for pandemic data analysis: The gap between statistics and models.Heliyon · 2024Article
- The Impact of Heating, Ventilation, and Air-Conditioning Design Features on the Transmission of Viruses, Including SARS-CoV-2: Overview of Reviews.Interactive journal of medical research · 2022Review
- Parameter estimation and uncertainty quantification using information geometry.Journal of the Royal Society, Interface · 2022Review
- Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model.International journal of environmental research and public health · 2021Article
- Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.Briefings in bioinformatics · 2021Review
- Tracking and predicting U.S. influenza activity with a real-time surveillance network.PLoS computational biology · 2020Article
- Use of daily Internet search query data improves real-time projections of influenza epidemics.Journal of the Royal Society, Interface · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and
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