ArticleEuropean journal of operational research2023
Global sensitivity analysis in epidemiological modeling.
Article in European journal of operational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- A compartmental model of variant coexistence, dynamics and dominance in infectious diseases: Case for SARS-CoV-2 in Abu Dhabi.Infectious Disease Modelling · 2026Article
- Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions.Journal of the Royal Society, Interface · 2026Article
- Global and Regional Trends in Multiple Sclerosis Incidence with Projections to 2030: A Joinpoint Regression Analysis of the GBD Data 2021 by Socio-Demographic Index and WHO Regions.Annals of global health · 2026Article
- Impact of the Ripple Effect on the Resilience of Multimodal Container Port Operations: A System Dynamics Simulation Approach.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- The interplay of influenza and COVID-19 in Germany, January 2020 - December 2022: a study of competitive disease dynamics with quarantine measures and partial cross-immunity.BMC public health · 2025Article
- Direction of impact for explainable risk assessment modeling.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers.Environmental science & technology · 2024Article
- Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle.Scientific reports · 2024Article
- Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle.Research square · 2024Article
- Predicting Tuberculosis Incidence and Its Trend in Tigray, Ethiopia: A Reality-Counterfactual Modeling Approach.Infection and drug resistance · 2024Article
- Analysis of the dynamics of a vector-borne infection with the effect of imperfect vaccination from a fractional perspective.Scientific reports · 2023Article
- How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden.International journal of disaster risk reduction : IJDRR · 2023Article
- There is a need for more precise models to assess the determinants of health crises like COVID-19.Frontiers in public health · 2023Article
- Introduction to the special issue on the role of operational research in future epidemics/ pandemics.European journal of operational research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.
Indexed as
Identifiers
What OpenQuestion holds
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.