ArticleInfectious Disease Modelling2017
Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A Primer for parameter uncertainty, identifiability, and forecasts.
Article in Infectious Disease Modelling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 2 of them syntheses that pooled it.
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.
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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
153 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Combining and comparing regional SARS-CoV-2 epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and global sensitivity analysis.Frontiers in public health · 2022Pooled it
- Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020Pooled it
- Resurgence of large-scale influenza A (H1N1) outbreaks: Modeling the interplay of transmission, loss of immunity, and vaccination.Infectious Disease Modelling · 2027Article
- Modelling plant disease spread via high-resolution human mobility networks.Journal of the Royal Society, Interface · 2026Article
- Cost-Effectiveness of Adult Hepatitis A Vaccination Strategies in Korea Under an Aging Susceptibility Profile.Applied health economics and health policy · 2026Article
- Mathematical modeling for analyzing mass drug administration operational factors for efficient malaria incidence reduction in southern Senegal.Infectious Disease Modelling · 2026Article
- Identifying the viral and epidemiological factors behind the apparent global extinction of influenza B/Yamagata.medRxiv : the preprint server for health sciences · 2026Article
- A novel mathematical model of seasonality in Oropouche virus transmission in Amazonas, Brazil.Journal of mathematical biology · 2026Article
- Impact of the first booster vaccine against SARS-CoV-2 in Chile.Infectious Disease Modelling · 2026Article
- Impacts of relaxed mask policies on COVID-19 epidemics: a modeling study in South Korea.BMC infectious diseases · 2026Article
- Real-time prediction of influenza and respiratory syncytial virus epidemics in primary care using the Gompertz model.Scientific reports · 2026Article
- Modelling tuberculosis transmission dynamics in Malaysia.Frontiers in public health · 2026Article
- Immunogenic cell death unlocks the potential for combined radiation and immunotherapy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- Modeling and Characterizing the Growth of the Texas-New Mexico Measles Outbreak of 2025.Epidemiologia (Basel, Switzerland) · 2025Article
- Revealing hidden drivers of Lassa fever through a model-informed approach for reproducing and predicting disease dynamics and guiding control strategies.Scientific reports · 2025Article
- Mathematical models to characterize the early phase of the COVID-19 pandemic in New Mexico, USA.Mathematical biosciences and engineering : MBE · 2025Article
- HPV transmission and optimal control of cervical cancer in China.Scientific reports · 2025Article
- State-space modelling for infectious disease surveillance data: Dynamic regression and covariance analysis.Infectious Disease Modelling · 2025Article
93 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
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Abstract
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in the observed data at different spatial and temporal scales, generate estimates of key kinetic parameters, assess the impact of interventions, optimize the impact of control strategies, and generate forecasts. We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models to time series data describing the temporal progression of case counts relating to population growth or infectious disease transmission dynamics. In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters, this frequentist approach relies on modeling the error structure in the data. We discuss issues related to parameter identifiability, uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
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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.