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Occup Environ Med doi:10.1136/oem.2008.044966
  • Original article

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

  1. Wenbiao Hu1,*,
  2. Archie Clements1,
  3. Gail Williams1,
  4. Shilu Tong2
  1. 1 The University of Queensland, Australia;
  2. 2 Queensland University of Technology, Australia
  1. Correspondence to: Wenbiao Hu, School of Population Health, The University of Queensland, Herston Road, Herston, Brisbane, 4006, Australia; w.hu{at}sph.uq.edu.au
  • Received 30 November 2008
  • Accepted 16 September 2009
  • Published Online First 9 October 2009

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.

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