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Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Author

Listed:
  • 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
  • Lee Ching Ng

Abstract

Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaike's Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations.Author Summary: As dengue virus transmission is through a human-to-mosquito-to-human cycle, the influence of meteorological factors on dengue is likely to be associated with their impact on mosquito populations and behavior. Other than the influence of weather factors, the shift of dominant serotypes and pre-emptive measures taken against dengue vectors may possibly affect the dengue transmission trend. In this study, we investigate the impact of weather factors on dengue in tropical Singapore, taking into consideration the disease epidemiology and profile of virus serotypes. We found that absolute humidity, as a composite index of mean temperature and relative humidity, is a more stable and better predictor for modeling dengue incidence than the other unique weather variables when virological factors are taken into consideration. This research suggests that absolute humidity needs to be considered together with all the other contributing factors in order to make meaningful public policy recommendations for dengue control.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pntd00:0002805
    DOI: 10.1371/journal.pntd.0002805
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    References listed on IDEAS

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    1. Dharmaratne Amarakoon & Anthony Chen & Sam Rawlins & Dave Chadee & Michael Taylor & Roxann Stennett, 2008. "Dengue epidemics in the Caribbean-temperature indices to gauge the potential for onset of dengue," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 13(4), pages 341-357, May.
    2. Bernard Cazelles & Mario Chavez & Anthony J McMichael & Simon Hales, 2005. "Nonstationary Influence of El Niño on the Synchronous Dengue Epidemics in Thailand," PLOS Medicine, Public Library of Science, vol. 2(4), pages 1-1, April.
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    1. 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.
    2. Daniel Adyro Martínez-Bello & Antonio López-Quílez & Alexander Torres-Prieto, 2017. "Bayesian dynamic modeling of time series of dengue disease case counts," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-19, July.
    3. 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.
    4. 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.
    5. 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.
    6. Jue Tao Lim & Borame Sue Dickens & Sun Haoyang & Ng Lee Ching & Alex R Cook, 2020. "Inference on dengue epidemics with Bayesian regime switching models," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-15, May.
    7. Mazni Baharom & Norfazilah Ahmad & Rozita Hod & Fadly Syah Arsad & Fredolin Tangang, 2021. "The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review," IJERPH, MDPI, vol. 18(21), pages 1-22, October.
    8. Vurukonda Sathish & Siuli Mukhopadhyay & Rashmi Tiwari, 2022. "Autoregressive and moving average models for zero‐inflated count time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 190-218, May.
    9. Jue Tao Lim & Borame Sue Lee Dickens & Lawrence Zheng Xiong Chew & Esther Li Wen Choo & Joel Ruihan Koo & Joel Aik & Lee Ching Ng & Alex R Cook, 2020. "Impact of sars-cov-2 interventions on dengue transmission," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(10), pages 1-17, October.
    10. Hao Gui & Sylvia Gwee & Jiayun Koh & Junxiong Pang, 2021. "Weather Factors Associated with Reduced Risk of Dengue Transmission in an Urbanized Tropical City," IJERPH, MDPI, vol. 19(1), pages 1-17, December.
    11. 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.
    12. 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.

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