To explore the paths of driving factor combinations leading to the emergence of medical surge scenarios in the future crisis environment of "disease X" outbreak, thus providing references for enhancing the overall resilience of the medical and health service system.

From July to September 2024, 144 members of front-line medical staff in Heilongjiang province who had participated in the prevention and control of the COVID-19 epidemic were selected through the Questionnaire Star platform by the purposive sampling method. Seven variables–the transmission capacity of the infectious disease, the pathogenicity of pathogens, the immunity of residents, the number of medical staff, the reserve of emergency materials, the frequency of emergency rescue and skill training, and the effectiveness of patient triage and referral mechanisms–were selected based on the public safety triangle theoretical model. The fuzzy-set qualitative comparative analysis was performed to explore the configuration path.

The results of necessity analysis showed that the consistency of the transmission capacity of the infectious disease, pathogenicity of pathogens, the immunity of residents, the number of medical staff, the reserve of emergency materials, the frequency of emergency rescue and skill training, and the effectiveness of patient triage and referral mechanisms were 0.546, 0.578, 0.485, 0.562, 0.491, 0.530, and 0.563, respectively (all < 0.9). There were no necessary conditions for a medical surge scenario in a future crisis environment of "disease X" outbreak. The conditional configuration analysis produced a total of 5 paths and 2 configurations, i.e., the weak epidemic-low bearing-weak response interweave driving surge type and the strong epidemic surge type with relatively sufficient preparation, with the overall consistency of 0.886 and the overall coverage of 0.532.

The occurrence of medical surge scenarios is the result of a combination of complex factors, and medical institutions must improve their response capabilities in all aspects such as personnel reserve and allocation, triage and referral, and material reserve.

To systematically analyze the factors influencing the leadership competency of primary-level health emergency administrators in the field of infectious disease prevention and control, as well as their underlying mechanisms, thereby providing evidence-based references for enhancing the leadership competency of primary-level health emergency administrators.

From November 2019 to March 2021, this study adopted a multi-stage stratified random sampling method to randomly select 152 primary-level health emergency administrators from Jiangsu province, Hubei province, Hunan province, Guangdong province, Anhui province, and Inner Mongolia Autonomous Region, as the research subjects. Structural equation modeling (SEM) combined with the bootstrap method was utilized to analyze and validate the paths and mediating effects of these influencing factors.

Through the survey of 152 primary-level health emergency administrators, this study found that human resource support had a positive impact on organizational climate (β = 0.461, P < 0.001). Organizational climate (β = 0.712, P < 0.001) and material resources support (β = 0.248, P = 0.004) had positive impacts on external social support. External social support had a positive impact on leadership competency (β = 0.519, P < 0.001). Through the mediating effect test, two mediating paths were identified. Mediating path 1 was material resources support → external social support → leadership competency (with an effect value of 0.129; 95%CI: 0.005–0.258), and mediating path 2 was human resource support → organizational climate → external social support → leadership competency (with an effect value of 0.170; 95%CI: 0.094–0.303).

The characteristics of infectious disease prevention and control reinforce the inevitability of the indirect transmission of the mechanisms influencing health emergency leadership competency. Leadership competency is formed through complex interactions between individuals and their environment. The sustainable improvement of leadership competency among primary-level health emergency administrators needs to be achieved by optimizing resource allocation and organizational climate, as well as improving the social support network.

To construct a demand-oriented fairness optimization model to provide decision-making support for scientific allocation in response to the shortage and regional inequity in personnel allocation in disease prevention and control institutions of China.

On the basis of the provincial panel data from 2006 to 2019, the entropy weight method was used to integrate the indicators of infectious disease dimension (morbidity/mortality of five key infectious diseases, such as plague and cholera) and population dimension (such as density, dependency ratio, and topographic relief) for building the regional priority index. The index was embedded into the population-weighted Gini coefficient function, and thus the improved fairness metrics was proposed. A multi-objective optimization model with the dual objectives of minimizing the fairness coefficient and minimizing the amount of personnel adjustment was established, and the nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was used to solve the Pareto optimal solution sets.

The priority index showed that Sichuan, Xinjiang, and Guangxi were the areas with high demand for disease prevention and control personnel. Through the Pareto frontier, 80 sets of optimal solutions were obtained, revealing a significant trade-off between fairness and implementation cost. The cost of maximizing fairness (reducing the Gini coefficient value by 0.066 8) was the largest adjustment amount (19 410 people). It was possible to only alleviate the fairness issue to a certain extent (reducing the Gini coefficient by 0.014 4) with the minimum adjustment amount (1 660 people).

This study innovatively incorporates the quantification of demand differences into the assessment of resource allocation fairness and employees a multi-objective genetic algorithm to generate the Pareto solution sets, providing a quantitative tool for differentiated personnel allocation strategies in disease prevention and control institutions.

To develop an R program for processing complex work unit entries in the Notifiable Disease Surveillance and Reporting System, enabling active searching of school-based cluster epidemics.

Taking the data of acute hemorrhagic conjunctivitis, pertussis, and other infectious diarrhea diseases in Guangdong province, 2024 as an example, we designed an active searching algorithm in R 4.1.0. The program standardizes unstructured school names via text mining algorithms, counts cases by normalized school name, actively search for school-based cluster epidemics with the fixed threshold method, and compare them with the epidemics reported in the Public Health Emergency Management Information System (PHEMIS).

When being aligned with PHEMIS reporting thresholds, the R program showed the positive predictive value of 100.00%. Among the reported school-based cluster epidemics, 67.65% (23/34) were detected by the R program. The R program identified 60.87% (14/23) cluster epidemics later than PHEMIS but achieved synchronized reporting of 39.13% (9/23) cluster epidemics. Moreover, the R program revealed 11 clusters meeting PHEMIS criteria but not reported. When the thresholds were lowered, more potential cluster epidemics were detected, and 2 signals were earlier than PHEMIS alerts.

This active searching framework can serve as a powerful supplement to PHEMIS, demonstrating operational value for early detection and containment of school-based cluster epidemics.

To develop an imported dengue fever prediction model based on multi-fusion machine learning, aiming to enhance the scientific basis and foresight of infectious disease prevention and control at Guangzhou port.

Data on imported dengue fever cases at Guangzhou port from 2015 to 2019 were collected, along with meteorological indicators, inbound passenger volume, flight traffic, vegetation index, and vector density. After data cleaning and standardization, three base models (BP neural network, random forest, and CatBoost) were constructed. Weighted averaging and stacking ensemble strategies were then adopted to build dual-fusion and triple-fusion models. Model performance was evaluated based on mean squared error (MSE) and coefficient of determination (R²).

The fusion models outperformed single models overall, demonstrating higher predictive accuracy and stability. Among them, the stacked ensemble of random forest and CatBoost achieved the best performance, with an MSE of 4.749 and an R² of 0.601, significantly improving upon the best single model. SHAP analysis further revealed that humidity, sunshine duration, and diurnal temperature variation were the main driving factors, while inbound passenger volume also contributed substantially to predictions.

Multi-fusion machine learning models provide significant advantages in predicting imported dengue cases, offering strong support for early warning and the formulation of prevention strategies in port public health.

To construct an evaluation indicator system for the synergistic prevention and control capabilities of public health risks at the human-animal interface based on the One Health concept and validate its application in typical regions, providing a tool for public health capability assessment and interdepartmental collaboration improvement in China.

From August to December 2024, an initial framework was developed via literature analysis. A comprehensive evaluation indicator system for synergistic prevention and control capabilities of public health risks at the human-animal interface was subsequently constructed through two rounds of Delphi expert consultation, with indicator weights determined by the analytic hierarchy process (AHP). Field surveys were conducted in Jiangxi and Hainan provinces via questionnaires, and the collected data were analyzed in Excel 16.72 and SPSS 26.0.

Among the primary indicators in the constructed indicator system, policy support had the highest weight (37.51%), followed by resource provision (18.58%) and comprehensive monitoring (16.08%), reflecting the central role of institutional coordination and resource collaboration in the synergistic prevention and control capabilities. The overall score for Jiangxi province was 70.94, higher than that (61.95) in Hainan province, with the main differences found in information system development and material provision. At the county level, Yugan county had the highest score (81.44), while Xinjian district had the lowest (51.58).

The indicator system developed in this study for assessing the synergistic prevention and control capabilities of public health risks at the human-animal interface is scientifically sound and operationally feasible. It can be used for assessing regional public health capabilities, identifying synergistic gaps, and supporting the formulation of differentiated strategies, providing methodological support for the implementation of the One Health concept in public health practices at the primary level.

To establish a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions, providing a tool for assessing the food safety risk monitoring capabilities of monitoring institutions.

Through literature review combined with practical monitoring work, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was preliminarily constructed across three dimensions: resource support, personnel capability, and monitoring technology. Twelve experts in relevant fields were selected for two rounds of Delphi expert consultation, and the analytic hierarchy process (AHP) was adopted to calculate the weights of indicators at each level.

The two rounds of expert consultation showed the expert positive coefficients of 100.0% and 83.33%, the expert authority coefficients of 0.803 and 0.798, and the Kendall′s coefficients of concordance of 0.187 and 0.287 (indicating good concordance among expert opinions), respectively (all P < 0.001). Following expert consultation, a comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions was established, comprising 3 first-level indicators, 9 second-level indicators, and 36 third-level indicators. In the two rounds of consultation, experts rated the importance of each indicator at (4.35 ± 0.93) and (4.42 ± 0.66) points, the representativeness at (4.19 ± 0.96) and (4.29 ± 0.66) points, and the feasibility at (4.28 ± 0.99) and (4.38 ± 0.74) points, respectively. The coefficients of variation (CV) for importance, representativeness, and feasibility scores in the first and second rounds were within the ranges of 0.08–0.45 and 0.02–0.22, 0.06–0.40 and 0.05–0.19, and 0.13–0.40 and 0.07–0.23, respectively. Within the constructed evaluation framework, first-level indicator weights ranged from 0.164 to 0.539, second-level indicator weights from 0.121 to 0.667, second-level indicator combination weights from 0.027 to 0.360, third-level indicator weights from 0.046 to 0.667, and third-level indicator combination weights from 0.002 to 0.129.

The comprehensive capability evaluation indicator system for national food safety risk monitoring technical institutions developed in this study demonstrates sound scientific validity and reliability. It can serve as a dependable, quantitative tool for assessing the overall capabilities of relevant monitoring technical institutions.

To develop an evaluation index system for assessing the development levels of community (village) public health committees, thus providing insights for optimizing urban public health committee structures and enhancing primary-level public health governance efficacy.

From March 2024 to October 2024, an evaluation index system draft was constructed through literature review, policy analysis, and focus group interviews. Two rounds of Delphi expert consultations were conducted to finalize the index system, with index weights determined via the analytic hierarchy process (AHP).

A total of twenty-five experts participated in the consultations. The finalized system comprised 5 first-level indexes, 20 second-level indexes, and 29 third-level indexes. Both consultation rounds achieved 100% expert response rates, with authority coefficients ≥ 0.70. High mean scores (from 7.40 to 9.96) were observed for index importance and feasibility, and the coefficients of variation (CV) were ≤ 0.25. The Kendall′s W coefficients improved between rounds (importance: 0.129 to 0.512; feasibility: 0.156 to 0.582) (P < 0.001).

The Delphi method-based evaluation index system demonstrates scientific validity and practical applicability for assessing the development levels of community (village) public health committees. Further empirical validation is recommended to refine its utility.