Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis

Xiaobo Liu; Keke Liu; Yujuan Yue; Haixia Wu; Shu Yang; Yuhong Guo; Dongsheng Ren; Ning Zhao; Jun Yang; Qiyong Liu

doi:10.3389/fpubh.2020.603872

Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis

Front Public Health. 2021 Jan 18:8:603872. doi: 10.3389/fpubh.2020.603872. eCollection 2020.

Authors

Xiaobo Liu¹, Keke Liu², Yujuan Yue¹, Haixia Wu¹, Shu Yang³, Yuhong Guo¹, Dongsheng Ren¹, Ning Zhao¹, Jun Yang⁴, Qiyong Liu¹

Affiliations

¹ State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
² Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
³ The Collaboration Unit for Field Epidemiology of State Key Laboratory of Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang, China.
⁴ Institute for Environmental and Climate Research, Jinan University, Guangzhou, China.

Abstract

Background: Determination of the key factors affecting dengue occurrence is of significant importance for the successful response to its outbreak. Yunnan and Guangdong Provinces in China are hotspots of dengue outbreak during recent years. However, few studies focused on the drive of multi-dimensional factors on dengue occurrence failing to consider the possible multicollinearity of the studied factors, which may bias the results. Methods: In this study, multiple linear regression analysis was utilized to explore the effect of multicollinearity among dengue occurrences and related natural and social factors. A principal component regression (PCR) analysis was utilized to determine the key dengue-driven factors in Guangzhou city of Guangdong Province and Xishuangbanna prefecture of Yunnan Province, respectively. Results: The effect of multicollinearity existed in both Guangzhou city and Xishuangbanna prefecture, respectively. PCR model revealed that the top three contributing factors to dengue occurrence in Guangzhou were Breteau Index (BI) (positive correlation), the number of imported dengue cases lagged by 1 month (positive correlation), and monthly average of maximum temperature lagged by 1 month (negative correlation). In contrast, the top three factors contributing to dengue occurrence in Xishuangbanna included monthly average of minimum temperature lagged by 1 month (positive correlation), monthly average of maximum temperature (positive correlation), monthly average of relative humidity (positive correlation), respectively. Conclusion: Meteorological factors presented stronger impacts on dengue occurrence in Xishuangbanna, Yunnan, while BI and the number of imported cases lagged by 1 month played important roles on dengue transmission in Guangzhou, Guangdong. Our findings could help to facilitate the formulation of tailored dengue response mechanism in representative areas of China in the future.

Keywords: control; dengue; influencing factors; mosquito-borne disease; principal component analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China / epidemiology
Dengue* / epidemiology
Humans
Principal Component Analysis
Regression Analysis
Temperature