Sensitivity analysis of infectious disease models: methods, advances and their application

By Jianyong Wu, Radhika Dhingra, Manoj Gambhir, Justin V. Remais. Journal of the Royal Society Interface, 2013

310 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Public Health, Environmental and Occupational Health, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7f0a818c5ff912779dbefe6a4a6e41876b415f89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Mikhail Prokopenko

Citations in the corpus

• Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “e deviation indicates that the dependency between the input and output is nonlinear. In the Morris method, a large deviation may also indicate that the input parameter interacts with other parameters [65]. Importantly, the responses are not directly comparable across the output variables, and instea”
• Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “…SA approaches for aerodynamic optimization; Perz et al. (2013) reviewed the global SA and UA methods applied to ecological resilience; Tian (2013) summarized the application of SA methods to building energy analysis; and Wu et al. (2013) review recent advances in SA for infectious disease models.”