IDEAS home Printed from https://ideas.repec.org/a/plo/pntd00/0008434.html
   My bibliography  Save this article

Real-time dengue forecast for outbreak alerts in Southern Taiwan

Author

Listed:
  • Yu-Chieh Cheng
  • Fang-Jing Lee
  • Ya-Ting Hsu
  • Eric V Slud
  • Chao A Hsiung
  • Chun-Hong Chen
  • Ching-Len Liao
  • Tzai-Hung Wen
  • Chiu-Wen Chang
  • Jui-Hun Chang
  • Hsiao-Yu Wu
  • Te-Pin Chang
  • Pei-Sheng Lin
  • Hui-Pin Ho
  • Wen-Feng Hung
  • Jing-Dong Chou
  • Hsiao-Hui Tsou

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.Author summary: Meteorological conditions are the most frequently mentioned factors in the study of dengue fever. Some of the main factors other than the purely meteorological about which the public-health authorities might have data, such as numbers of cases or other current measurements of dengue outbreaks in neighboring cities, had been used in some of the past dengue studies. In this study, we developed models for predicting dengue case number based on past dengue case data and meteorological data. The goal of the models is to provide early warning of the occurrence of dengue fever to assist public health agencies in preparing an epidemic response plan.

Suggested Citation

  • Yu-Chieh Cheng & Fang-Jing Lee & Ya-Ting Hsu & Eric V Slud & Chao A Hsiung & Chun-Hong Chen & Ching-Len Liao & Tzai-Hung Wen & Chiu-Wen Chang & Jui-Hun Chang & Hsiao-Yu Wu & Te-Pin Chang & Pei-Sheng L, 2020. "Real-time dengue forecast for outbreak alerts in Southern Taiwan," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(7), pages 1-18, July.
  • Handle: RePEc:plo:pntd00:0008434
    DOI: 10.1371/journal.pntd.0008434
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0008434
    Download Restriction: no

    File URL: https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0008434&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pntd.0008434?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    3. Erin A Mordecai & Jeremy M Cohen & Michelle V Evans & Prithvi Gudapati & Leah R Johnson & Catherine A Lippi & Kerri Miazgowicz & Courtney C Murdock & Jason R Rohr & Sadie J Ryan & Van Savage & Marta S, 2017. "Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(4), pages 1-18, April.
    4. Elodie Descloux & Morgan Mangeas & Christophe Eugène Menkes & Matthieu Lengaigne & Anne Leroy & Temaui Tehei & Laurent Guillaumot & Magali Teurlai & Ann-Claire Gourinat & Justus Benzler & Anne Pfannst, 2012. "Climate-Based Models for Understanding and Forecasting Dengue Epidemics," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(2), pages 1-19, February.
    5. Hai-Yan Xu & Xiuju Fu & Lionel Kim Hock Lee & Stefan Ma & Kee Tai Goh & Jiancheng Wong & Mohamed Salahuddin Habibullah & Gary Kee Khoon Lee & Tian Kuay Lim & Paul Anantharajah Tambyah & Chin Leong Lim, 2014. "Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 8(5), pages 1-11, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ting-Wu Chuang & Luis Fernando Chaves & Po-Jiang Chen, 2017. "Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-20, June.
    2. Maneerat, Somsakun & Daudé, Eric, 2016. "A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas," Ecological Modelling, Elsevier, vol. 333(C), pages 66-78.
    3. Abdalgader, Tarteel & Banerjee, Malay & Zhang, Lai, 2022. "Spatially weak syncronization of spreading pattern between Aedes Albopictus and dengue fever," Ecological Modelling, Elsevier, vol. 473(C).
    4. Haogao Gu & Ross Ka-Kit Leung & Qinlong Jing & Wangjian Zhang & Zhicong Yang & Jiahai Lu & Yuantao Hao & Dingmei Zhang, 2016. "Meteorological Factors for Dengue Fever Control and Prevention in South China," IJERPH, MDPI, vol. 13(9), pages 1-12, August.
    5. Jue Tao Lim & Yiting Han & Borame Sue Lee Dickens & Lee Ching Ng & Alex R Cook, 2020. "Time varying methods to infer extremes in dengue transmission dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    6. Yebin Chen & Zhigang Zhao & Zhichao Li & Weihong Li & Zhipeng Li & Renzhong Guo & Zhilu Yuan, 2019. "Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China," IJERPH, MDPI, vol. 16(14), pages 1-14, July.
    7. Ana C Piovezan-Borges & Francisco Valente-Neto & Wanderli P Tadei & Neusa Hamada & Fabio O Roque, 2020. "Simulated climate change, but not predation risk, accelerates Aedes aegypti emergence in a microcosm experiment in western Amazonia," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-12, October.
    8. Felipe J. Colón-González & Rory Gibb & Kamran Khan & Alexander Watts & Rachel Lowe & Oliver J. Brady, 2023. "Projecting the future incidence and burden of dengue in Southeast Asia," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    9. Ioana Gutu & Daniela Tatiana Agheorghiesei & Alexandru Tugui, 2023. "Assessment of a Workforce Sustainability Tool through Leadership and Digitalization," IJERPH, MDPI, vol. 20(2), pages 1-30, January.
    10. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    11. S. A. Abu Bakar & Saralees Nadarajah & Z. A. Absl Kamarul Adzhar, 2018. "Loss modeling using Burr mixtures," Empirical Economics, Springer, vol. 54(4), pages 1503-1516, June.
    12. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    13. Herbert Hoijtink & Meinte Vollema, 2003. "Contemporary Extensions of the Rasch Model," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(3), pages 263-276, August.
    14. Jaewoong Yun, 2023. "Strategies for Improving the Sustainability of Fare-Free Policy for the Elderly through Preferences by Travel Modes," Sustainability, MDPI, vol. 15(20), pages 1-14, October.
    15. Malerba, Martino E. & Connolly, Sean R. & Heimann, Kirsten, 2015. "An experimentally validated nitrate–ammonium–phytoplankton model including effects of starvation length and ammonium inhibition on nitrate uptake," Ecological Modelling, Elsevier, vol. 317(C), pages 30-40.
    16. Sakirul Khan & Sheikh Mohammad Fazle Akbar & Takaaki Yahiro & Mamun Al Mahtab & Kazunori Kimitsuki & Takehiro Hashimoto & Akira Nishizono, 2022. "Dengue Infections during COVID-19 Period: Reflection of Reality or Elusive Data Due to Effect of Pandemic," IJERPH, MDPI, vol. 19(17), pages 1-12, August.
    17. Shengzhang Dong & George Dimopoulos, 2023. "Aedes aegypti Argonaute 2 controls arbovirus infection and host mortality," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    18. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    19. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    20. Eunha Shim, 2017. "Cost-effectiveness of dengue vaccination in Yucatán, Mexico using a dynamic dengue transmission model," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-17, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pntd00:0008434. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosntds (email available below). General contact details of provider: https://journals.plos.org/plosntds/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.