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

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.

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Publication Details

Journal
medRxiv
Published
2026-03-02
DOI
https://doi.org/10.64898/2026.02.27.26346591
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Fernando Alarid-Escudero, J. Goldhaber-Fiebert
medRxiv
COVID-19 epidemiological studies
article

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

Fernando Alarid-Escudero, J. Goldhaber-Fiebert
article en

Abstract

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.

medRxiv
Center for Policy Analysis (US), Stanford University (US)
Openalex Percentile: Top 43%
COVID-19 epidemiological studies
DOIOpen Access PDFOpenAlex
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PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model calibration — Fernando Alarid-Escudero, J. Goldhaber-Fiebert · medRxiv (2026) | TGRS Research Map | TGRS