Dengue fever and El Nino-Southern Oscillation in Queensland, Australia: A time series predictive model

Hu, Wenbiao, Clements, Archie, Williams, Gail and Shilu, Tong (2010) Dengue fever and El Nino-Southern Oscillation in Queensland, Australia: A time series predictive model. Occupational and Environmental Medicine, 67 3: 307-311. doi:10.1136/oem.2008.044966


Author Hu, Wenbiao
Clements, Archie
Williams, Gail
Shilu, Tong
Title Dengue fever and El Nino-Southern Oscillation in Queensland, Australia: A time series predictive model
Formatted title
Dengue fever and El Niño-Southern Oscillation in Queensland, Australia: A time series predictive model
Journal name Occupational and Environmental Medicine   Check publisher's open access policy
ISSN 1351-0711
1470-7926
Publication date 2010-05
Year available 2009
Sub-type Article (original research)
DOI 10.1136/oem.2008.044966
Volume 67
Issue 3
Start page 307
End page 311
Total pages 5
Editor Loomis, Dana.
Place of publication London, United Kingdom
Publisher B M J Group
Collection year 2011
Language eng
Subject C1
9604 Control of Pests, Diseases and Exotic Species
111799 Public Health and Health Services not elsewhere classified
Abstract Background: It remains unclear over whether it is possible to develop an epidemic forecasting model for transmission of Dengue fever in Queensland, Australia. Objectives: To examine the potential impact of El Niño/Southern Oscillation (ENSO) on the transmission of dengue fever in Queensland, Australia and explore the possibility of developing a forecast model of dengue fever. Methods: Data on the Southern Oscillation Index (SOI), an indicator of ENSO activity, were obtained from the Australian Bureau of Meteorology. Numbers of dengue fever cases notified and the numbers of postcode areas (PA) with dengue fever cases between January 1993 and December 2005 were obtained from the Queensland Health and relevant population data were obtained from the Australia Bureau of Statistics. A multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) model was developed and validated by dividing the data file into two datasets: the data from January 1993 - December 2003 were used to construct a model and those from January 2004 - December 2005 were used to validate it. Results: A decrease in the average SOI (i.e., warmer conditions) during the preceding 3 – 12 months was significantly associated with an increase in the monthly numbers of PA with dengue fever cases (β = − 0.038; p = 0.019). Predicted values from the SARIMA model were consistent with the observed values in the validation dataset (root-mean-square percentage error: 1.93%). Conclusions: Climate variability is directly and/or indirectly associated with dengue transmission and the development of a SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia. Copyright Article author (or their employer) 2009. Produced by BMJ Publishing Group Ltd under licence.
Keyword Ross River virus
Plasmodium-falciparum malaria
Weather-based prediction
Epidemic-prone regions
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 9 October 2009

 
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Created: Tue, 23 Mar 2010, 12:37:53 EST by Barry Peacock on behalf of School of Public Health