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Introduction: As the frequency of infectious diseases rises, it's more important than ever to pay attention to the competency level of front-line nurses as the primary force in front-line rescue, which has an impact on the quality of anti-epidemic response. This paper aims to construct the competency evaluation index system for front-line nurses during the outbreak of major infectious diseases. Materials and methods: This study combined literature review, critical incident technique interviews, and semi-structured in-depth interviews, as well as two rounds of Delphi expert correspondence, to construct a competence evaluation index system for front-line nurses during the outbreak of major infectious diseases. The study used purposive sampling to select 26 experts from 11 provinces and cities across China to conduct two rounds of Delphi expert consultation, and the indicators were selected based on the mean importance score > 3.5 and the coefficient of variation < 0.25, and the weights of the indicators were calculated by the Analytic Hierarchy Process. The effective recovery rates of the two rounds of correspondence questionnaires were 93.1% and 96%. Results: The effective recovery rates of the two rounds of correspondence questionnaires were 93.1% and 96%, the authority coefficients of experts were 0.96 and 0.98, the Kendall's coordination coefficients of the first, second, and third level indexes were 0.281, 0.132, and 0.285 (P < 0.001), 0.259, 0.158, and 0.415 (P < 0.001). The final index system includes 4 primary indicators (Knowledge System of Infectious Diseases, Nursing Skills for Infectious Diseases, Related Professional Abilities for Infectious Diseases, and Comprehensive Quality), 10 secondary indicators, and 64 tertiary indicators. Conclusion: The competency evaluation index system of front-line nurses during the outbreak of major infectious diseases is scientific, reasonable, and practical, which can provide a scientific basis for nursing managers to accurately understand, describe, analyze, and evaluate the competence level of nursing staff and scientifically implement the allocation of human resources in the future, as well as serve as a content framework for subsequent training programs. [ABSTRACT FROM AUTHOR]

• A comprehensive measure of model predictability signature is developed. • A list of satellite, reanalysis and ground-based precipitation products is assessed. • BATEA excels MCMC for indirect measurements of precipitation in general. • Model predictability signature has potential on merging precipitation estimates. Quantifying and reducing the errors and uncertainties in high-resolution gridded precipitation products are essential for hydrological modelling to generate realistic parameter estimates and accurate model predictions. Formal Bayesian inference provides a powerful tool to quantify rainfall uncertainty and other uncertainty factors in hydrological modelling. An efficient way to reduce rainfall uncertainty in hydrological modelling is to merge precipitation estimates from multiple sources prior to their real-world applications. However, it remains a challenging task to optimally determine the weightings of different precipitation estimates based on the predictive ability of a hydrological model, especially from a probabilistic perspective. This study addresses this issue by evaluating and merging six gridded precipitation products using formal Bayesian inference schemes, including the Markov Chain Monte Carlo (MCMC) approach and Bayesian total error analysis (BATEA). MCMC describes the total predictive uncertainty using residual error models. In contrast, BATEA utilizes the storm depth multiplier approach to separately account for rainfall uncertainty in probabilistic uncertainty quantification. A novel signature of model predictability that combines the reliability and precision of probabilistic model predictions is developed to quantify the predictive ability of a hydrological model from precipitation estimates. Using the proposed predictability signature, we examined the benefit of optimally merging multi-source precipitation estimates in improving the accuracy of hydrologic simulations by applying the method to the Danjiangkou Reservoir region (DRR) between 1998 and 2007. The analysis demonstrates that (1) BATEA, compared to MCMC, can provide more realistic estimates of model parameters and tighter predictive intervals of daily streamflow when using rain gauge observations as well as gridded precipitation products; and (2) the proposed predictability signature is efficient for optimally merging precipitation measurements as it can produce tighter uncertainty bands and better streamflow simulations than individual precipitation estimates. This study provides insights into improved quantification and reduction of uncertainty in multi-source precipitation products for hydrological modelling. [ABSTRACT FROM AUTHOR]

• A comprehensive measure of model predictability signature is developed. • A list of satellite, reanalysis and ground-based precipitation products is assessed. • BATEA excels MCMC for indirect measurements of precipitation in general. • Model predictability signature has potential on merging precipitation estimates. Quantifying and reducing the errors and uncertainties in high-resolution gridded precipitation products are essential for hydrological modelling to generate realistic parameter estimates and accurate model predictions. Formal Bayesian inference provides a powerful tool to quantify rainfall uncertainty and other uncertainty factors in hydrological modelling. An efficient way to reduce rainfall uncertainty in hydrological modelling is to merge precipitation estimates from multiple sources prior to their real-world applications. However, it remains a challenging task to optimally determine the weightings of different precipitation estimates based on the predictive ability of a hydrological model, especially from a probabilistic perspective. This study addresses this issue by evaluating and merging six gridded precipitation products using formal Bayesian inference schemes, including the Markov Chain Monte Carlo (MCMC) approach and Bayesian total error analysis (BATEA). MCMC describes the total predictive uncertainty using residual error models. In contrast, BATEA utilizes the storm depth multiplier approach to separately account for rainfall uncertainty in probabilistic uncertainty quantification. A novel signature of model predictability that combines the reliability and precision of probabilistic model predictions is developed to quantify the predictive ability of a hydrological model from precipitation estimates. Using the proposed predictability signature, we examined the benefit of optimally merging multi-source precipitation estimates in improving the accuracy of hydrologic simulations by applying the method to the Danjiangkou Reservoir region (DRR) between 1998 and 2007. The analysis demonstrates that (1) BATEA, compared to MCMC, can provide more realistic estimates of model parameters and tighter predictive intervals of daily streamflow when using rain gauge observations as well as gridded precipitation products; and (2) the proposed predictability signature is efficient for optimally merging precipitation measurements as it can produce tighter uncertainty bands and better streamflow simulations than individual precipitation estimates. This study provides insights into improved quantification and reduction of uncertainty in multi-source precipitation products for hydrological modelling. [ABSTRACT FROM AUTHOR]