Assessing the spatial patterns and driving mechanism of dengue fever risk in high density cities: The case of Guangzhou

Coupled processes of climate change and urbanization drive the worldwide epidemic of dengue fever, an infectious disease mainly transmitted by Aedes aegypti and Ae. albopictus. Geographic climate background may determine inter-city differences in mosquito transmission risk, but in the intra-city, climate conditions incline to interweave with intricate urban morphologies and shape more microscopic and heterogeneous urban environments to be conducive to mosquito breeding, maintenance and sprawl. Evaluating and identifying spatial patterns and driving mechanism of dengue outbreaks are critical initiatives to support public hygiene action but have received limited attention. This study comply the hazard-exposure-vulnerability framework and applied geographic and landscape ecology methods to assess the risk of dengue fever and spatially non-stationary associations with major risk factors in Guangzhou. Firstly, we set positive and negative ovitraps to seize the Ae. albopictus throughout the city, and use MaxEnt model to predict the spatial distribution probability of Ae. albopictus. Secondly, bivariate spatial autocorrelation analysis was used to measure the relationship between mosquito hazard and population exposure in Guangzhou, and a fine-scale assessment of dengue fever risk was realized by overlaying with the vulnerability analysis. The predicted dengue risk is highly consistent with the spatial distribution of real dengue cases. Finally, we detected the spatial heterogeneity of major risk factors with dengue outbreaks using the MGWR model. Urban morphologies, vegetation cover and land use types well explained the spatial pattern of dengue outbreaks in Guangzhou, and differences in socioeconomic levels mainly drive the spatially heterogeneous association between risk factors and dengue fever. These findings reveal the essential role of urban design in public health management and provide references for dengue risk prediction, prevention and control in high-density cities.