Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model. Issue 5 (9th October 2009)

• Record Type: Journal Article
• Authors: Hu, Wenbiao; Clements, Archie; Williams, Gail; Tong, Shilu
• Is Part Of: Occupational and environmental medicine. Volume 67:Issue 5(2010)
• Page Start: 307
• Page End: 311
• Publication Date: 2009-10-09
• Subjects: Dengue fever -- SARIMA -- Southern Oscillation Index -- prediction -- climate -- epidemiology -- public health -- communicable diseases -- time series study; Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oem.2008.044966
• Languages: English
• ISSNs: 1351-0711

Abstract

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 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 El Niño/Southern Oscillation activity, were obtained from the Australian Bureau of Meteorology. Numbers of dengue fever cases notified and the numbers of postcode areas 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 model was developed and validated by dividing the data file into two datasets: the data from January 1993 to December 2003 were used to construct a model and those from January 2004 to December 2005 were used to validate it. Results: A decrease in the average SOI (ie, warmer conditions) during the preceding 3–12 months was significantly associated with an increase in the monthly numbers of postcode areas with dengue fever cases (β=−0.038; p = 0.019). Predicted values from the Seasonal Auto-regressive Integrated Moving Average 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 an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.