ArticleJournal of the Royal Society, Interface2017
Monte Carlo profile confidence intervals for dynamic systems.
Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Quantifying the impact of vaccination on pertussis dynamics in Sweden.PLoS computational biology · 2026Article
- Noisy periodicity in tropical respiratory disease dynamics.medRxiv : the preprint server for health sciences · 2026Article
- Impact of the COVID-19 pandemic on tuberculosis epidemiology in California, Florida, New York, and Texas.Annals of the American Thoracic Society · 2026Article
- Supporting evidence-based decisions about the geographic and demographic extensions of seasonal malaria chemoprevention in Benin: A modelling study.PLOS global public health · 2025Article
- Inference on spatiotemporal dynamics for coupled biological populations.Journal of the Royal Society, Interface · 2024Article
- Informing policy via dynamic models: Cholera in Haiti.PLoS computational biology · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.PLoS computational biology · 2023Article
- Likelihood-based estimation and prediction for a measles outbreak in Samoa.Infectious Disease Modelling · 2023Article
- Bagged filters for partially observed interacting systems.Journal of the American Statistical Association · 2023Article
- Mechanisms for the circulation of influenza A(H3N2) in China: A spatiotemporal modelling study.PLoS pathogens · 2022Article
- Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study.The Lancet. Global health · 2022Article
- Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data.PLoS computational biology · 2022Article
- Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Modelling the effects of the contaminated environments on tuberculosis in Jiangsu, China.Journal of theoretical biology · 2021Article
- Synchronized nonpharmaceutical interventions for the control of COVID-19.Nonlinear dynamics · 2021Article
- Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter.Statistics and computing · 2020Article
- Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany.Biostatistics (Oxford, England) · 2020Article
- Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.Wellcome open research · 2020Article
- Panel Data Analysis via Mechanistic Models.Journal of the American Statistical Association · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation using models for which the likelihood function is intractable. When Monte Carlo error can be made small, by sufficiently exhaustive computation, then the standard theory and practice of likelihood-based inference applies. As datasets become larger, and models more complex, situations arise where no reasonable amount of computation can render Monte Carlo error negligible. We develop profile likelihood methodology to provide frequentist inferences that take into account Monte Carlo uncertainty. We investigate the role of this methodology in facilitating inference for computationally challenging dynamic latent variable models. We present examples arising in the study of infectious disease transmission, demonstrating our methodology for inference on nonlinear dynamic models using genetic sequence data and panel time-series data. We also discuss applicability to nonlinear time-series and spatio-temporal data.
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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.