IDEAS home Printed from https://ideas.repec.org/a/plo/pmed00/1000168.html
   My bibliography  Save this article

Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis

Author

Listed:
  • Michael A Johansson
  • Derek A T Cummings
  • Gregory E Glass

Abstract

Michael Johansson and colleagues use wavelet analysis to show that there is limited evidence for a multiyear relationship between climate and dengue incidence in Puerto Rico, Mexico, and Thailand.Background: The mosquito-borne dengue viruses are a major public health problem throughout the tropical and subtropical regions of the world. Changes in temperature and precipitation have well-defined roles in the transmission cycle and may thus play a role in changing incidence levels. The El Niño Southern Oscillation (ENSO) is a multiyear climate driver of local temperature and precipitation worldwide. Previous studies have reported varying degrees of association between ENSO and dengue incidence. Methods and Findings: We analyzed the relationship between ENSO, local weather, and dengue incidence in Puerto Rico, Mexico, and Thailand using wavelet analysis to identify time- and frequency-specific association. In Puerto Rico, ENSO was transiently associated with temperature and dengue incidence on multiyear scales. However, only local precipitation and not temperature was associated with dengue on multiyear scales. In Thailand, ENSO was associated with both temperature and precipitation. Although precipitation was associated with dengue incidence, the association was nonstationary and likely spurious. In Mexico, no association between any of the variables was observed on the multiyear scale. Conclusions: The evidence for a relationship between ENSO, climate, and dengue incidence presented here is weak. While multiyear climate variability may play a role in endemic interannual dengue dynamics, we did not find evidence of a strong, consistent relationship in any of the study areas. The role of ENSO may be obscured by local climate heterogeneity, insufficient data, randomly coincident outbreaks, and other, potentially stronger, intrinsic factors regulating transmission dynamics. : Please see later in the article for the Editors' Summary Background: Every year, as many as 50–100 million people become infected with one of four closely related dengue viruses through the bite of a female Aedes aegypti mosquito that has acquired the virus by feeding on infected human blood. Dengue is endemic (always present) in many tropical and subtropical countries but its incidence (the number of new cases in a population over a given time period) follows a seasonal pattern. This is because the abundance of Ae. aegypti is regulated by rainfall, which provides breeding sites and stimulates egg hatching, and by temperature, which influences the insects' survival and their rate of development and reproduction. Temperature also affects the mosquitoes' ability to transmit dengue virus—higher temperatures increase transmission rates. Although some people who become infected with dengue have no symptoms, many develop dengue fever, a severe, flu-like illness that lasts a few days. Other people—more than half a million a year—develop dengue hemorrhagic fever, a potentially fatal condition. There is no vaccine to prevent dengue and no specific treatment for the disease, but standard medical care can prevent most deaths from dengue. Why Was This Study Done?: As well as seasonal variations in the incidence of dengue, large dengue outbreaks (epidemics) occur every few years. To help with health care planning, public health officials would like a way to predict when these epidemics are likely to occur, but to develop such a system requires a good understanding of the factors that lead to major epidemics. Although variations in host–virus interactions (for example, changes in host immunity to dengue) almost certainly play an important role in the timing of dengue epidemics, interannual changes in temperature and rainfall could also be involved. One major cause of global interannual weather variation is the El Niño Southern Oscillation (ENSO), a climate cycle centered on the Pacific Ocean that repeats every 3–4 years. Previous studies have reported varying degrees of association between ENSO and dengue. In this study, the researchers reanalyze the relationship between ENSO, local weather, and dengue incidence in three dengue-endemic countries using “wavelet analysis.” This mathematical technique can separate the effects of seasonal weather variations on dengue incidence from those of interannual weather fluctuations. What Did the Researchers Do and Find?: The researchers retrieved data on the incidence of dengue fever and dengue hemorrhagic fever in Puerto Rico, Thailand and Mexico since the mid 1980s from national surveillance systems. They also collected historical weather data for each country and information on ENSO. They then used these data and wavelet analysis to investigate the relationship between ENSO, local weather, and dengue incidence in each country on the annual scale and on the multiyear scale. On the annual scale, temperature, rainfall, and dengue incidence were strongly associated in all three countries. On the multiyear scale, ENSO was associated with temperature and with dengue incidence in Puerto Rico, but only for part of the study period. Only local rainfall was associated with the incidence of dengue in that country. The lack of a direct path of association from ENSO to either weather variable to dengue incidence suggests that the ENSO–dengue association may be a spurious result. In Thailand, ENSO was associated with both temperature and rainfall, and rainfall was associated with dengue incidence. However, detailed analysis suggests that this latter association was also probably spurious. Finally, there was no association between any of the variables in Mexico on the multiyear scale. What Do These Findings Mean?: Although these findings show a strong associations between both temperature and rainfall and dengue incidence on the annual scale in Puerto Rico, Thailand, and Mexico, they provide little evidence for any relationship between ENSO, climate, and dengue incidence. Multiyear climate variability may play a role in interannual variations in dengue incidence, the researchers suggest, but their study does not provide any evidence for a strong and consistent relationship between climate variability and dengue incidence. It is possible that the effects of ENSO on dengue incidence are being masked by local variations in weather or by stronger factors regulating disease transmission such as host–virus or host–vector interactions. Future studies into the relationship between dengue outbreaks and multiyear climate variability will need to include these and other factors. For now, however, information on ENSO cannot be used to design an early warning system for dengue outbreaks. Additional Information: Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000168.

Suggested Citation

  • Michael A Johansson & Derek A T Cummings & Gregory E Glass, 2009. "Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis," PLOS Medicine, Public Library of Science, vol. 6(11), pages 1-9, November.
  • Handle: RePEc:plo:pmed00:1000168
    DOI: 10.1371/journal.pmed.1000168
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000168
    Download Restriction: no

    File URL: https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1000168&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pmed.1000168?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicholas G. Reich & Justin Lessler & Krzysztof Sakrejda & Stephen A. Lauer & Sopon Iamsirithaworn & Derek A. T. Cummings, 2016. "Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 285-292, July.
    2. Taynãna C Simões & Cláudia T Codeço & Aline A Nobre & Álvaro E Eiras, 2013. "Modeling the Non-Stationary Climate Dependent Temporal Dynamics of Aedes aegypti," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-10, August.
    3. Kraisak Kesorn & Phatsavee Ongruk & Jakkrawarn Chompoosri & Atchara Phumee & Usavadee Thavara & Apiwat Tawatsin & Padet Siriyasatien, 2015. "Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    4. 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.
    5. Suwannapa Ninphanomchai & Chitti Chansang & Yien Ling Hii & Joacim Rocklöv & Pattamaporn Kittayapong, 2014. "Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences," IJERPH, MDPI, vol. 11(10), pages 1-16, October.
    6. Luong Thi Nguyen & Huy Xuan Le & Dong Thanh Nguyen & Ha Quang Ho & Ting-Wu Chuang, 2020. "Impact of Climate Variability and Abundance of Mosquitoes on Dengue Transmission in Central Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-16, April.
    7. Bernard Bett & Delia Grace & Hu Suk Lee & Johanna Lindahl & Hung Nguyen-Viet & Pham-Duc Phuc & Nguyen Huu Quyen & Tran Anh Tu & Tran Dac Phu & Dang Quang Tan & Vu Sinh Nam, 2019. "Spatiotemporal analysis of historical records (2001–2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-22, November.
    8. Sean M Moore & Andrew Monaghan & Kevin S Griffith & Titus Apangu & Paul S Mead & Rebecca J Eisen, 2012. "Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
    9. Asim Anwar & Noman Khan & Muhammad Ayub & Faisal Nawaz & Asim Shah & Antoine Flahault, 2019. "Modeling and Predicting Dengue Incidence in Highly Vulnerable Countries using Panel Data Approach," IJERPH, MDPI, vol. 16(13), pages 1-8, June.
    10. Julián Alfredo Fernández-Niño & Claudia Iveth Astudillo-García & Ietza Bojorquez-Chapela & Evangelina Morales-Carmona & Airain Alejandra Montoya-Rodriguez & Lina Sofia Palacio-Mejia, 2016. "The Mexican Cycle of Suicide: A National Analysis of Seasonality, 2000-2013," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    11. Chathurika Hettiarachchige & Stefan von Cavallar & Timothy Lynar & Roslyn I Hickson & Manoj Gambhir, 2018. "Risk prediction system for dengue transmission based on high resolution weather data," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
    12. Prasad Liyanage & Hasitha Tissera & Maquins Sewe & Mikkel Quam & Ananda Amarasinghe & Paba Palihawadana & Annelies Wilder-Smith & Valérie R. Louis & Yesim Tozan & Joacim Rocklöv, 2016. "A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka," IJERPH, MDPI, vol. 13(11), pages 1-21, November.
    13. Pablo Méndez-Lázaro & Frank E. Muller-Karger & Daniel Otis & Matthew J. McCarthy & Marisol Peña-Orellana, 2014. "Assessing Climate Variability Effects on Dengue Incidence in San Juan, Puerto Rico," IJERPH, MDPI, vol. 11(9), pages 1-20, September.

    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:pmed00:1000168. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosmedicine (email available below). General contact details of provider: https://journals.plos.org/plosmedicine/ .

    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.