Climate and socioeconomic factors drive heterogeneous dengue risk escalation in the Chinese population

Comprehensive Summary

This study, presented by Guang and colleagues, examined how climate and socioeconomic changes may influence current and future dengue fever risk across China and whether these effects differ geographically. The researchers developed a geographically explainable artificial intelligence (GeoXAI) model using a hazard–exposure–vulnerability framework. They integrated environmental variables, population distribution, and socioeconomic indicators to estimate dengue risk under future climate and development scenarios through 2050 and 2100. The study found that dengue risk is projected to increase across China but unevenly between regions. Rising minimum winter temperatures emerged as the strongest predictor of dengue expansion, while population density and urbanization also contributed to increasing risk. High-risk areas are expected to expand northward beyond historically affected regions, with southwestern and southeastern China showing particularly notable increases and additional relative growth occurring in lower-risk northwestern areas. Under the highest emissions scenario (SSP585), overall dengue risk increased substantially by both mid- and late-century.

Outcomes and Implications

This research is important because it identifies populations that may become vulnerable to dengue before large outbreaks occur, allowing preventive strategies to be implemented earlier. It also demonstrates the value of combining environmental and social determinants of health when predicting future disease burden. Clinically and medically, these findings support stronger infectious disease surveillance systems and improved allocation of healthcare resources in emerging risk regions. Physicians and public health professionals may need to consider dengue in broader geographic areas than previously expected, especially as changing climate conditions alter vector distribution. The work also highlights how predictive modeling and population-level forecasting can guide preventive medicine and reduce future healthcare burden.