Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis
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-dimension...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-01-01
|
Series: | Frontiers in Public Health |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2020.603872/full |
id |
doaj-735719e5edbc42e18d977f5eb74da278 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaobo Liu Keke Liu Yujuan Yue Haixia Wu Shu Yang Yuhong Guo Dongsheng Ren Ning Zhao Jun Yang Qiyong Liu |
spellingShingle |
Xiaobo Liu Keke Liu Yujuan Yue Haixia Wu Shu Yang Yuhong Guo Dongsheng Ren Ning Zhao Jun Yang Qiyong Liu Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis Frontiers in Public Health dengue influencing factors principal component analysis mosquito-borne disease control |
author_facet |
Xiaobo Liu Keke Liu Yujuan Yue Haixia Wu Shu Yang Yuhong Guo Dongsheng Ren Ning Zhao Jun Yang Qiyong Liu |
author_sort |
Xiaobo Liu |
title |
Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis |
title_short |
Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis |
title_full |
Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis |
title_fullStr |
Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis |
title_full_unstemmed |
Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis |
title_sort |
determination of factors affecting dengue occurrence in representative areas of china: a principal component regression analysis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Public Health |
issn |
2296-2565 |
publishDate |
2021-01-01 |
description |
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. |
topic |
dengue influencing factors principal component analysis mosquito-borne disease control |
url |
https://www.frontiersin.org/articles/10.3389/fpubh.2020.603872/full |
work_keys_str_mv |
AT xiaoboliu determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT kekeliu determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT yujuanyue determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT haixiawu determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT shuyang determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT yuhongguo determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT dongshengren determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT ningzhao determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT junyang determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis AT qiyongliu determinationoffactorsaffectingdengueoccurrenceinrepresentativeareasofchinaaprincipalcomponentregressionanalysis |
_version_ |
1724333705468575744 |
spelling |
doaj-735719e5edbc42e18d977f5eb74da2782021-01-18T05:51:13ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-01-01810.3389/fpubh.2020.603872603872Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression AnalysisXiaobo Liu0Keke Liu1Yujuan Yue2Haixia Wu3Shu Yang4Yuhong Guo5Dongsheng Ren6Ning Zhao7Jun Yang8Qiyong Liu9State 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, ChinaProvincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaState 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, ChinaState 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, ChinaThe Collaboration Unit for Field Epidemiology of State Key Laboratory of Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang, ChinaState 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, ChinaState 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, ChinaState 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, ChinaInstitute for Environmental and Climate Research, Jinan University, Guangzhou, ChinaState 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, ChinaBackground: 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.https://www.frontiersin.org/articles/10.3389/fpubh.2020.603872/fulldengueinfluencing factorsprincipal component analysismosquito-borne diseasecontrol |