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.