ArticleEpidemics2014
Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy.
Article in Epidemics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Calibration of individual-based models to epidemiological data: A systematic review.PLoS computational biology · 2020Pooled it
- Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions.Journal of the Royal Society, Interface · 2026Article
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- Decoding uncertainty for clinical decision-making.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Feasibility of ending tuberculosis in Shangrao City through active intervention measures: a mathematical study.Frontiers in public health · 2025Article
- Quantitative measurement of influenza virus transmission in animal model: an overview of current state.Biophysical reviews · 2023Review
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- Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management.Socio-economic planning sciences · 2022Article
- A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data.Evolving systems · 2022Article
- Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model.PLoS computational biology · 2021Article
- Estimating the distribution of time to extinction of infectious diseases in mean-field approaches.Journal of the Royal Society, Interface · 2020Article
- The EAT-Lancet Commission's Dietary Composition May Not Prevent Noncommunicable Disease Mortality.The Journal of nutrition · 2020Article
- Modelling microbial infection to address global health challenges.Nature microbiology · 2019Review
- Article
- Bayesian Methods for Calibrating Health Policy Models: A Tutorial.PharmacoEconomics · 2017Article
- Article
- Model-Informed Risk Assessment and Decision Making for an Emerging Infectious Disease in the Asia-Pacific Region.PLoS neglected tropical diseases · 2016Article
- Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.PLoS neglected tropical diseases · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions. However, typical sensitivity analyses of deterministic dynamic infectious disease models focus on model architecture and the relative importance of parameters but neglect parameter uncertainty when reporting model predictions. Consequently, model results that identify point estimates of intervention levels necessary to terminate transmission yield limited insight into the probability of success. We apply probabilistic uncertainty analysis to a dynamic model of influenza transmission and assess global uncertainty in outcome. We illustrate that when parameter uncertainty is not incorporated into outcome estimates, levels of vaccination and treatment predicted to prevent an influenza epidemic will only have an approximately 50% chance of terminating transmission and that sensitivity analysis alone is not sufficient to obtain this information. We demonstrate that accounting for parameter uncertainty yields probabilities of epidemiological outcomes based on the degree to which data support the range of model predictions. Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health.
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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.