國家衛生研究院 NHRI:Item 3990099045/12761
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 12145/12927 (94%)
Visitors : 909549      Online Users : 787
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/12761


    Title: Real-time dengue forecast for outbreak alerts in Southern Taiwan
    Authors: Cheng, YC;Lee, FJ;Hsu, YT;Slud, EV;Hsiung, CA;Chen, CH;Liao, CL;Wen, TH;Chang, CW;Chang, JH;Wu, HY;Chang, TP;Lin, PS;Ho, HP;Hung, WF;Chou, JD;Tsou, HH
    Contributors: Institute of Population Health Sciences;National Mosquito-Borne Diseases Control Research Center;National Institute of Infectious Diseases and Vaccinology
    Abstract: Dengue fever is a viral disease transmitted by mosquitoes. In recent decades, dengue fever has spread throughout the world. In 2014 and 2015, southern Taiwan experienced its most serious dengue outbreak in recent years. Some statistical models have been established in the past, however, these models may not be suitable for predicting huge outbreaks in 2014 and 2015. The control of dengue fever has become the primary task of local health agencies. This study attempts to predict the occurrence of dengue fever in order to achieve the purpose of timely warning. We applied a newly developed autoregressive model (AR model) to assess the association between daily weather variability and daily dengue case number in 2014 and 2015 in Kaohsiung, the largest city in southern Taiwan. This model also contained additional lagged weather predictors, and developed 5-day-ahead and 15-day-ahead predictive models. Our results indicate that numbers of dengue cases in Kaohsiung are associated with humidity and the biting rate (BR). Our model is simple, intuitive and easy to use. The developed model can be embedded in a "real-time" schedule, and the data (at present) can be updated daily or weekly based on the needs of public health workers. In this study, a simple model using only meteorological factors performed well. The proposed real-time forecast model can help health agencies take public health actions to mitigate the influences of the epidemic.
    Date: 2020-07
    Relation: PLoS Neglected Tropical Diseases. 2020 Jul 27;17(7):Article number e0008434.
    Link to: http://dx.doi.org/10.1371/journal.pntd.0008434
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1935-2735&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000556904700002
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85088811723
    Appears in Collections:[Hsiao-Hui Sophie Tsou] Periodical Articles
    [Chao A. Hsiung] Periodical Articles
    [Chun-Hong Chen] Periodical Articles
    [Ching-Len Liao] Periodical Articles
    [Pei-Sheng Lin] Periodical Articles

    Files in This Item:

    File Description SizeFormat
    NPH2020062901.pdf3011KbAdobe PDF377View/Open


    All items in NHRI are protected by copyright, with all rights reserved.

    Related Items in TAIR

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback