This paper describes the development of an empirical model to forecast epidemics of Ross River virus (RRV) disease using the multivariate seasonal auto-regressive integrated moving average (SARIMA) technique in Brisbane, Australia. We obtained computerized data on notified RRV disease cases, climate, high tide, and population sizes in Brisbane for the period 1985-2001 from the Queensland Department of Health, the Australian Bureau of Meteorology, the Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model was developed and validated by dividing the data file into two data sets: the data between January 1985 and December 2000 were used to construct a model, and those between January and December 2001 to validate it. The SARIMA models show that monthly precipitation (beta = 0.004, P = 0.031) was significantly associated with RRV transmission. However, there was no significant association between other climate variables (e.g., temperature, relative humidity, and high tides) and RRV transmission. The predictive values in the model were generally consistent with actual values (root mean square percentage error = 0.94%). Therefore, this model may have applications as a decision supportive tool in disease control and risk-management planning programs.