Climate variability and Ross River virus transmission

Abstract

Objectives - (1) To examine the feasibility to link climate data with monthly incidence of Ross River virus (RRv). (2) to assess the impact of climate variability on the RRv transmission. Design - An ecological time-series analysis was performed on the data collected between 1985 to 1996 in Queensland, Australia.
Methods - Information on the notified RRv cases was obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. Spearman’s rank correlation analyses were performed to examine the relationship between climate variability and the monthly incidence of notified RRv infections. The autoregressive integrated moving average (ARIMA) model was used to perform a time-series analysis. Since maximum and minimum temperatures were highly correlated with each other (rs = 0.75), two separate models were developed.
Results - For the eight major cities in Queensland, the climate-RRv correlation coefficients were in the range of 0.12 to 0.52 for maximum and minimum temperatures, -0.10 to 0.46 for rainfall, and 0.11 to 0.52 for relative humidity and high tide. For the
whole State, rainfall (partial regression coefficient: 0.017 [95% confidence interval: 0.009-0.025] in Model I and 0.018 [0.010-0.026) in Model II), and high tidal level (0.030 [0.006-0.054] in Model I and 0.029 [0.005-0.053] in Model II) appeared to have
played significant roles in the transmission of RRv in Queensland. Maximum temperature was also marginally significantly associated with the incidence of RRv infection.
Conclusion - Rainfall, temperature and tidal levels may be important environmental determinants in the transmission cycles of RRv disease.