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Statistical modeling of Dengue transmission dynamics with environmental factors

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  • Wang, Lengyang
  • Zhang, Mingke

Abstract

Dengue fever is one of the most common mosquito-borne infectious diseases in tropical regions. Understanding the dynamics of dengue transmission can help provide timely early warnings, thereby reducing mortality. However, previous studies have failed to simulate faithfully dengue dynamics and answer questions pertinent to outbreaks. By incorporating environmental factors into a time-series-susceptible-infectious-recovered (TSIR) model, a new substantive model, to analyze their impact on transmission, is proposed. The newly proposed environmental-time-series-susceptible-infectious-recovered (ETSIR) model can highlight statistically their significance on dengue transmission, thus providing deeper insight into the transmission and addressing several epidemiological puzzles.

Suggested Citation

  • Wang, Lengyang & Zhang, Mingke, 2025. "Statistical modeling of Dengue transmission dynamics with environmental factors," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:csdana:v:203:y:2025:i:c:s0167947324001646
    DOI: 10.1016/j.csda.2024.108080
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    References listed on IDEAS

    as
    1. Huang, Lei & Jiang, Hui & Wang, Huixia, 2019. "A novel partial-linear single-index model for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 110-122.
    2. Anderson Fernandes Brito & Lais Ceschini Machado & Rachel J. Oidtman & Márcio Junio Lima Siconelli & Quan Minh Tran & Joseph R. Fauver & Rodrigo Dias de Oliveira Carvalho & Filipe Zimmer Dezordi & Myl, 2021. "Lying in wait: the resurgence of dengue virus after the Zika epidemic in Brazil," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. repec:plo:pntd00:0002503 is not listed on IDEAS
    4. Jamie M. Caldwell & A. Desiree LaBeaud & Eric F. Lambin & Anna M. Stewart-Ibarra & Bryson A. Ndenga & Francis M. Mutuku & Amy R. Krystosik & Efraín Beltrán Ayala & Assaf Anyamba & Mercy J. Borbor-Cord, 2021. "Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Yien Ling Hii & Huaiping Zhu & Nawi Ng & Lee Ching Ng & Joacim Rocklöv, 2012. "Forecast of Dengue Incidence Using Temperature and Rainfall," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(11), pages 1-9, November.
    6. Fang Guo & Pei Zhang & Vivian Do & Jakob Runge & Kun Zhang & Zheshen Han & Shenxi Deng & Hongli Lin & Sheikh Taslim Ali & Ruchong Chen & Yuming Guo & Linwei Tian, 2024. "Ozone as an environmental driver of influenza," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    7. Yoon Ling Cheong & Katrin Burkart & Pedro J. Leitão & Tobia Lakes, 2013. "Assessing Weather Effects on Dengue Disease in Malaysia," IJERPH, MDPI, vol. 10(12), pages 1-16, November.
    8. Yingcun Xia & Julia R. Gog & Bryan T. Grenfell, 2005. "Semiparametric estimation of the duration of immunity from infectious disease time series: influenza as a case‐study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 659-672, June.
    9. Rebecca K. Borchering & Angkana T. Huang & Luis Mier-y-Teran-Romero & Diana P. Rojas & Isabel Rodriguez-Barraquer & Leah C. Katzelnick & Silvio D. Martinez & Gregory D. King & Stephanie C. Cinkovich &, 2019. "Impacts of Zika emergence in Latin America on endemic dengue transmission," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    10. repec:plo:pntd00:0000996 is not listed on IDEAS
    11. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
    Full references (including those not matched with items on IDEAS)

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