A regression and value perception dual model for individual protection against Dengue

Dengue fever is an acute infectious disease transmitted by Aedes mosquitoes, posing a threat to public health. As vaccines are not widely available, non-pharmacological interventions and behavioral improvements have become key prevention and control strategies. Based on the 8-year environmental and epidemic data of Guangzhou from 2015 to 2022, a regression model was constructed to explore the effects of Aedes density, climate, and population density on dengue fever infection rate. A value perception model was established, and the subjective infection probability, the effectiveness of prevention and control measures, and the perception of health loss are the core psychological variables that drive individual protection behavior. A sequentially coupled regression and value perception dual-model framework was then constructed. In this framework, the regression model identifies aggregate environmental risk signals associated with dengue infection rates, while the value perception model uses these signals as scenario inputs for analysing subjective infection probability, perceived health loss, perceived effectiveness of protective measures, and perceived protection cost. A three-dimensional comprehensive prevention and control strategy of vector regulation, behavioral intervention, and medical response was proposed. Through theoretical scenario simulation and sensitivity analysis, the study identified how changes in subjective infection probability, perceived effectiveness of protective measures, and perceived health loss may influence individual protective tendency. The proposed sequentially coupled dual-model framework provides an interpretable approach for linking environmental risk identification with behavioural decision analysis, and its transformation into behavioral decision-making and provides evidence-based and targeted strategies for urban dengue fever prevention and control and public health practice.