A Delphi–AHP and Dual-track Bayesian calibration Framework for Setting-Specific Transmission Risk Assessment of Acute Respiratory Infectious Diseases

Acute respiratory infectious diseases (ARIDs) spread rapidly across diverse settings, necessitating accurate identification of high-risk scenarios for timely intervention. This study develops a quantitative, setting-specific risk assessment framework integrating a Delphi–AHP-based indicator system with a dual-track Bayesian calibration model. Through literature review, two-round Delphi consultation, and AHP weighting, a hierarchical indicator system comprising 4 primary, 9 secondary, and 28 tertiary indicators was constructed, with infection source characteristics receiving the highest weight. The system demonstrated high internal consistency (Cronbach’s α = 0.970–0.998), acceptable structural validity (KMO = 0.808), and substantial expert consensus. Using data from 14 outbreak settings, Bayesian inference was performed via MCMC sampling and conjugate updating under both weakly informative and informative priors. The weighted risk scores showed a positive association with secondary attack rates (r = 0.622, p = 0.0176), and parameter estimates remained stable across prior specifications, indicating preliminary robustness under limited sample conditions. This integrated Delphi–AHP and Bayesian framework supports scenario-based risk differentiation for ARIDs and provides a potential foundation for future AI-assisted prediction of emerging infectious diseases, pending further validation.