ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2018
Modeling and inference for infectious disease dynamics: a likelihood-based approach.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Temperature-based spatiotemporal heterogeneity of hand, foot, and mouth disease transmission in China.BMC infectious diseases · 2025Article
- Modeling the 2014-2015 Vesicular Stomatitis Outbreak in the United States Using an SEIR-SEI Approach.Viruses · 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
- Anchoring the mean generation time in the SEIR to mitigate biases in ℜRoyal Society open science · 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
- Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics.Journal of mathematical biology · 2022Article
- Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data.PLoS computational biology · 2022Article
- Integrating data mining and transmission theory in the ecology of infectious diseases.Ecology letters · 2020Article
- Panel Data Analysis via Mechanistic Models.Journal of the American Statistical Association · 2019Article
- Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models.Theoretical biology & medical modelling · 2019Article
- Modeling and inference for infectious disease dynamics: a likelihood-based approach.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
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1 author.
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Abstract
Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed-form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood maximization, promoting the study of novel data-motivated mechanistic models over the last decade. Reviewing these models is the focus of this paper. In particular, we highlight statistical aspects of these models like overdispersion, which is key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration.
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