[1][2] Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study.
[3] It is also important in all mathematical modelling studies of epidemics.
[4] Sensitivity analysis can be used in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study.
Given the significant uncertainty at play, the use of sensitivity analysis to apportion the output uncertainty into input parameters is crucial in the context of Decision-making.
Examples of applications of sensitivity analysis to modelling of COVID-19 are [6] and.