Rainfall, mosquito density and the transmission of Ross River virus: A time-series forecasting model

This paper attempted to develop an epidemic forecasting model using local data on rainfall and mosquito density to predict outbreaks of Ross River virus (RRV) disease in Brisbane, Australia. We obtained monthly data on the counts of RRV cases, monthly total rainfall, human population size and mosquito density (i.e., average number of mosquitoes trapped in all mosquito monitoring stations per month) between 1 November 1998 and 31 December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Australia Bureau of Statistics and Brisbane City Council, respectively. Both polynomial distributed lag (PDL) time-series regression and seasonal auto-regressive integrated moving average (SARIMA) models were used to examine associations of RRV transmission with rainfall and mosquito density after adjustment for seasonality and auto-correlation. The results show that 85% and 95% of the variance in the RRV transmission was accounted for by rainfall and mosquito density, respectively. Both rainfall and mosquito density were strong predictors of the RRV transmission in simple models. However, multivariate PDL models show that only mosquito density at lags of 0 and 1 month was significantly associated with the transmission of RRV disease. The SARIMA models show similar results. The findings of this study may facilitate the development of early warning systems for the control and prevention of this disease and other similar vector-borne diseases using local rainfall and/or vector data.