Purpose We introduce PRE-CISE, a pre-calibration workflow that integrates coverage analysis, local sensitivity, and collinearity diagnostics to streamline model calibration and transparently address nonidentifiability. We demonstrate the benefits of PRE-CISE using a four-state Sick-Sicker Markov testbed and a COVID-19 case study.

Methods PRE-CISE begins with a coverage analysis to verify that model outputs generated with parameter sets drawn from their prior distribution span calibration targets, followed by local sensitivities to quantify the influence of parameters on model outputs, guiding the resizing of the prior distribution bounds to improve coverage. Identifiability is then assessed via collinearity analysis; large indices indicate practical nonidentifiability. For the testbed model, we calibrated 3 parameters to survival, prevalence, and the proportion of Sick to Sicker at 10, 20, and 30 years. For the COVID-19 model, we calibrated 11 parameters to match daily confirmed incident cases. Bayesian calibration was conducted on both analyses.

Results Coverage analyses flagged initial misfits; local sensitivities identified the Sick-to-Sicker transition probability has a greater effect on model outputs, and resizing its prior distribution bounds improved coverage. Collinearity analyses showed that combining multiple calibration targets across time points enabled recovery of all three parameters. In the COVID-19 model, local sensitivity analyses prioritized time-varying detection rates and contact-reduction effects, reducing the search space, thereby improving calibration efficiency. Daily incident case calibration targets yielded collinearity indices below practical thresholds (e.g., < 15) for all parameter combinations, whereas weekly calibration targets were larger and closer to the cutoff.

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.

The authors have declared no competing interest.

Dr. Alarid-Escudero was supported by NCI grants U01-CA253913 and U01-CA265750 as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). Drs. Goldhaber-Fiebert and Alarid-Escudero were supported by NIDA grant R37DA015612. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NIH, NIDA, NCI, or CISNET.

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