Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China

Abstract

Background

This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China.

Methods

Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF.

Results

Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01).

Conclusions

The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention.

Author summary

Dengue fever (DF) as a mosquito-borne viral disease remains a challenge for the prevention and control caused by the increased population, global development, human movement, and urbanization in the last five decades. The largest DF outbreak occurred with more than 40,000 cases in Guangdong in 2014 since DF re-emerged in China. The accurately spatiotemporal identification of DF transmission and the related socio-environmental factors are considered to be important for the strategy decision-making of the official government. This study first identified the spatiotemporal pattern and socio-environmental factors associated with DF occurrence at street and daily level in Guangzhou, China from 2006 to 2014, using spatiotemporal scan statistical methods. The results suggested that DF control should be targeted in the southwest of Guangzhou during autumn, particularly 75 high risk streets. We found that the aged population, floating population, low-education population and the non-agricultural population significantly contributed to the DF clustering risk at street level. Finally, a spread trend of DF toward southwest part of Guangzhou was noticed. These results could be implemented towards prevention and control measures of DF in high-risk areas in Guangzhou.