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Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand

Author

Listed:
  • Derek A.T. Cummings

    (Johns Hopkins University
    Johns Hopkins Bloomberg School of Public Health)

  • Rafael A. Irizarry

    (Johns Hopkins Bloomberg School of Public Health)

  • Norden E. Huang

    (NASA Goddard Space Flight Center)

  • Timothy P. Endy

    (United States Army Medical Research Institute in Infectious Disease)

  • Ananda Nisalak

    (Armed Forces Research Institute of Medical Sciences)

  • Kumnuan Ungchusak

    (Ministry of Public Health)

  • Donald S. Burke

    (Johns Hopkins Bloomberg School of Public Health)

Abstract

Dengue fever is a mosquito-borne virus that infects 50–100 million people each year1. Of these infections, 200,000–500,000 occur as the severe, life-threatening form of the disease, dengue haemorrhagic fever (DHF)2. Large, unanticipated epidemics of DHF often overwhelm health systems3. An understanding of the spatial–temporal pattern of DHF incidence would aid the allocation of resources to combat these epidemics. Here we examine the spatial–temporal dynamics of DHF incidence in a data set describing 850,000 infections occurring in 72 provinces of Thailand during the period 1983 to 1997. We use the method of empirical mode decomposition4 to show the existence of a spatial–temporal travelling wave in the incidence of DHF. We observe this wave in a three-year periodic component of variance, which is thought to reflect host–pathogen population dynamics5,6. The wave emanates from Bangkok, the largest city in Thailand, moving radially at a speed of 148 km per month. This finding provides an important starting point for detecting and characterizing the key processes that contribute to the spatial–temporal dynamics of DHF in Thailand.

Suggested Citation

  • Derek A.T. Cummings & Rafael A. Irizarry & Norden E. Huang & Timothy P. Endy & Ananda Nisalak & Kumnuan Ungchusak & Donald S. Burke, 2004. "Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand," Nature, Nature, vol. 427(6972), pages 344-347, January.
    • Handle: RePEc:nat:nature:v:427:y:2004:i:6972:d:10.1038_nature02225
    DOI: 10.1038/nature02225
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    Cited by:

    1. Chia-Hsien Lin & Tzai-Hung Wen, 2011. "Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue," IJERPH, MDPI, vol. 8(7), pages 1-18, July.
    2. 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.
    3. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    4. Mavalankar, Dileep & Puwar, Tapasvi & Dipti Govil & Tiina M Murtola & S S Vasan, 2009. "A Preliminary Estimate of Immediate Cost of Chikungunya and Dengue to Gujarat, India," IIMA Working Papers WP2009-01-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Mohan, Nishith & Kumari, Nitu, 2021. "Positive steady states of a SI epidemic model with cross diffusion," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    6. Campi, Gaetano & Bianconi, Antonio, 2022. "Periodic recurrent waves of Covid-19 epidemics and vaccination campaign," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    7. Erickson, Richard A. & Presley, Steven M. & Allen, Linda J.S. & Long, Kevin R. & Cox, Stephen B., 2010. "A dengue model with a dynamic Aedes albopictus vector population," Ecological Modelling, Elsevier, vol. 221(24), pages 2899-2908.
    8. Sun, Xiaolei & Tang, Ling & Yang, Yuying & Wu, Dengsheng & Li, Jianping, 2014. "Identifying the dynamic relationship between tanker freight rates and oil prices: In the perspective of multiscale relevance," Economic Modelling, Elsevier, vol. 42(C), pages 287-295.
    9. Vanessa Racloz & Rebecca Ramsey & Shilu Tong & Wenbiao Hu, 2012. "Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(5), pages 1-9, May.
    10. Tzong-Shiann Ho & Ting-Chia Weng & Jung-Der Wang & Hsieh-Cheng Han & Hao-Chien Cheng & Chun-Chieh Yang & Chih-Hen Yu & Yen-Jung Liu & Chien Hsiang Hu & Chun-Yu Huang & Ming-Hong Chen & Chwan-Chuen Kin, 2020. "Comparing machine learning with case-control models to identify confirmed dengue cases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(11), pages 1-21, November.
    11. Mohammad Reza Davahli & Krzysztof Fiok & Waldemar Karwowski & Awad M. Aljuaid & Redha Taiar, 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
    12. Catherine A. Lippi & Anna M. Stewart-Ibarra & Ángel G. Muñoz & Mercy J. Borbor-Cordova & Raúl Mejía & Keytia Rivero & Katty Castillo & Washington B. Cárdenas & Sadie J. Ryan, 2018. "The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012," IJERPH, MDPI, vol. 15(4), pages 1-15, April.
    13. Jiao Zhang & Qingcheng Zeng, 2017. "Modelling the volatility of the tanker freight market based on improved empirical mode decomposition," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1655-1667, April.
    14. Phaisarn Jeefoo & Nitin Kumar Tripathi & Marc Souris, 2010. "Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand," IJERPH, MDPI, vol. 8(1), pages 1-24, December.
    15. Nicholas G Reich & Stephen A Lauer & Krzysztof Sakrejda & Sopon Iamsirithaworn & Soawapak Hinjoy & Paphanij Suangtho & Suthanun Suthachana & Hannah E Clapham & Henrik Salje & Derek A T Cummings & Just, 2016. "Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(6), pages 1-17, June.
    16. Albert C Yang & Jong-Ling Fuh & Norden E Huang & Ben-Chang Shia & Chung-Kang Peng & Shuu-Jiun Wang, 2011. "Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-6, January.
    17. Quirine A ten Bosch & Brajendra K Singh & Muhammad R A Hassan & Dave D Chadee & Edwin Michael, 2016. "The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(5), pages 1-25, May.
    18. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    19. Frey, Erwin, 2010. "Evolutionary game theory: Theoretical concepts and applications to microbial communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(20), pages 4265-4298.

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