Dataset Information

Global sensitivity analysis in epidemiological modeling.

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.

SUBMITTER: Lu X

PROVIDER: S-EPMC8592916 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Global sensitivity analysis in epidemiological modeling.

Lu Xuefei X Borgonovo Emanuele E

European journal of operational research 20211116 1

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 m ...[more]

PMID: 34803213

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Project description:Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish "influential" parameters to be estimated and "non-influential" parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by "expert judgment"-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

Project description:BackgroundTracheal cancer is a rare malignancy with limited research but high mortality rates. This study aims to analyse recent data to understand the global burden, trends, and risk factors for tracheal cancer, facilitating improved prevention and treatment strategies.MethodsWe conducted a study on tracheal cancer using data from the Global Cancer Observatory and the Cancer Incidence in Five Continents databases. We collected information on the incidence of tracheal cancer, risk factors, and the Human Development Index (HDI) at the country level. The univariate linear regression was used to explore the relationship between tracheal cancer and the various risk factors. We utilised joinpoint regression analysis to calculate the Average Annual Percentage Change (AAPC) in tracheal cancer incidence.ResultsThe global age-standardised rate of incidence of tracheal cancer was 2.9 per 10 million (3,472 cases in total) in 2022, with the highest regional incidence observed in Central and Eastern Europe (ASR = 9.0) and the highest national incidence in Hungary (12.5). Higher incidence was found among the males (3.8) than females (2.0); among the older adults aged 50-74 (11.9) than the younger population aged 15-49 (1.2). A higher tracheal cancer incidence ratio was associated with higher levels of smoking, alcohol drinking, diabetes, lipid disorders, and HDI. Despite the overall decreasing trends for all population groups (highest decrease in Thailand; AAPC: -15.06, 95% CI: -21.76 to -7.78, p = 0.002), there was an increase in some female populations (highest increase in Colombia, AAPC: 19.28, 95% CI: 16.48 to 22.15, p < 0.001) and younger populations (highest increase in Ireland; AAPC: 29.84, 95% CI: 25.74 to 34.06, p < 0.001).ConclusionThis study provides a comprehensive analysis of tracheal cancer, focusing on risk factors and population-level trends. There has been an overall decreasing trend in the incidence of tracheal cancer, particularly among males and older adults, while the decline is less pronounced in females and younger individuals. Further research is needed to explore the underlying drivers of these epidemiological trends.

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Global sensitivity analysis in epidemiological modeling.

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Global sensitivity analysis in epidemiological modeling.

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