A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka

Prasad Liyanage, Hasitha Tissera, Maquins Sewe, Mikkel Quam, Ananda Amarasinghe, Paba Palihawadana, Annelies Wilder-Smith, Valérie R. Louis, Yesim Tozan and Joacim Rocklöv
Additional contact information
Prasad Liyanage: Ministry of Health, Colombo 01000, Sri Lanka
Hasitha Tissera: Ministry of Health, Colombo 01000, Sri Lanka
Maquins Sewe: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 87 Umeå, Sweden
Mikkel Quam: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 87 Umeå, Sweden
Ananda Amarasinghe: Ministry of Health, Colombo 01000, Sri Lanka
Paba Palihawadana: Ministry of Health, Colombo 01000, Sri Lanka
Annelies Wilder-Smith: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 87 Umeå, Sweden
Valérie R. Louis: Institute of Public Health, University of Heidelberg Medical School, D-69120 Heidelberg, Germany
Yesim Tozan: College of Global Public Health, New York University, New York, NY 10003, USA
Joacim Rocklöv: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 87 Umeå, Sweden

IJERPH, 2016, vol. 13, issue 11, 1-21

Abstract: Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka.

Keywords: dengue; vector control; Oceanic Niño Index; rainfall; temperature; weather; climate (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:13:y:2016:i:11:p:1087-:d:82188

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