PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model calibration

Purpose: We introduce PRE-CISE, a pre-calibration workflow integrating coverage analysis, local sensitivity, and collinearity diagnostics to streamline model calibration. We demonstrate PRE-CISE's benefits using two testbeds (a Sick-Sicker Markov model and an SIR transmission model) and a COVID19 case study. Methods: PRE-CISE uses coverage analysis to verify that model outputs generated from the prior distribution span calibration targets, followed by local sensitivities to quantify parameter influence, guiding the resizing of prior bounds to improve coverage. Identifiability is assessed via collinearity analysis; large indices indicate practical nonidentifiability. Bayesian calibration was used: for the testbed models (3 and 2 parameters, respectively) to match their targets; for the COVID-19 model (11 parameters) to match daily confirmed incident cases. Results: Coverage analyses flagged initial misfits; local sensitivities identified that the Sick-to-Sicker transition probability has a greater effect on model outputs, and resizing its prior distribution bounds improved coverage. Collinearity analyses indicated that using multiple calibration targets over different time points enabled identification of all three parameters. For the SIR testbed, collinearity indices declined as target points accumulated, crossing the identifiability threshold once the data spanned the epidemic peak. In the COVID-19 model, local sensitivity analyses prioritized time-varying detection rates and contact-reduction effects, reducing the search space. Daily incident case calibration targets yielded collinearity indices below practical thresholds for all parameter combinations, whereas indices for weekly calibration targets were larger. PRE-CISE achieved greater calibration efficiency gains for denser posterior sampling and more complex models. Conclusions: PRE-CISE provides a practical, transparent pathway that helps modelers refine prior distribution bounds and calibration targets before intensive calibration, improving uncertainty reporting and strengthening the reliability of model-based health policy analyses.