中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 互联网大数据与安全信息学研究中心
Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016)

文献类型：期刊论文

作者Zhu, Binghua3,4; Wang, Ligui4; Wang, Haiying2; Cao, Zhidong5; Zha, Lei4; Li, Ze4; Ye, Zhongyang4; Zhang, Jinping3; Song, Hongbin4; Sun, Yansong1
刊名PLOS ONE
出版日期2019-12-09
卷号14期号:12页码:12
ISSN号1932-6203
DOI10.1371/journal.pone.0225811
通讯作者Zhang, Jinping(jinpingzhang305@126.com) ; Song, Hongbin(hongbinsong@263.net) ; Sun, Yansong(sunys1964@hotmail.com)
英文摘要Introduction In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016. Methods Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman's rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors. Results We found the minimum temperature suitable for dengue transmission was.18 degrees C, and as 97.91% of cases occurred when the minimum temperature was above 18 degrees C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60. Conclusion Our model was accurate and timely, with consideration of interactive effects between meteorological factors.
WOS关键词CLIMATE VARIABILITY ; AEDES-ALBOPICTUS ; TEMPERATURE ; VECTOR ; TRANSMISSION ; VARIABLES ; PROVINCE ; SURVIVAL ; AEGYPTI ; VIRUS
资助项目Mega-projects of Science and Technology Research[2017ZX10303401] ; Beijing Nova Program[Z171100001117102] ; Military Medical Science and Technology Youth Cultivation Program[19QNP113] ; Military Logistics Research Program[BWS14C051]
WOS研究方向Science & Technology - Other Topics
语种英语
出版者PUBLIC LIBRARY SCIENCE
WOS记录号WOS:000534024700011
资助机构Mega-projects of Science and Technology Research ; Beijing Nova Program ; Military Medical Science and Technology Youth Cultivation Program ; Military Logistics Research Program
源URL[http://ir.ia.ac.cn/handle/173211/39450]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Zhang, Jinping; Song, Hongbin; Sun, Yansong
作者单位1.Acad Mil Med Sci, Coll Mil Med, Beijing, Peoples R China
2.Natl Def Univ PLA, Joint Serv Inst, Beijing, Peoples R China
3.305 Hosp PLA, Beijing, Peoples R China
4.Chinese PLA Ctr Dis Control & Prevent, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Binghua,Wang, Ligui,Wang, Haiying,et al. Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016)[J]. PLOS ONE,2019,14(12):12.
APA Zhu, Binghua.,Wang, Ligui.,Wang, Haiying.,Cao, Zhidong.,Zha, Lei.,...&Sun, Yansong.(2019).Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016).PLOS ONE,14(12),12.
MLA Zhu, Binghua,et al."Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016)".PLOS ONE 14.12(2019):12.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。