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Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model
  1. Wenbiao Hu1,
  2. Archie Clements1,2,
  3. Gail Williams1,
  4. Shilu Tong3
  1. 1School of Population Health, The University of Queensland, Queensland, Australia
  2. 2Australian Centre for International and Tropical Health, Queensland Institute of Medical Research, Queensland, Australia
  3. 3School of Public Health, Queensland University of Technology, Queensland, Australia
  1. Correspondence to Dr Wenbiao Hu, School of Population Health, The University of Queensland, Herston Road, Herston, Queensland 4006, Australia; w.hu{at}sph.uq.edu.au

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.

  • Dengue fever
  • SARIMA
  • Southern Oscillation Index
  • prediction
  • climate
  • epidemiology
  • public health
  • communicable diseases
  • time series study

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Footnotes

  • Competing interests None to declare.

  • Ethics approval This study was conducted with the approval of the University of Queensland, Australia.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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