Open AccessEnvironmental and entomological factors determining Ross River virus activity in the River Murray Valley of South Australia
Author(s) -
Williams Craig R.,
Fricker Stephen R.,
Kokkinn Michael J.
Publication year - 2009
Publication title -
australian and new zealand journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.946
H-Index - 76
eISSN - 1753-6405
pISSN - 1326-0200
DOI - 10.1111/j.1753-6405.2009.00390.x
Subject(s) - ross river virus , abundance (ecology) , relative species abundance , regression analysis , geography , scale (ratio) , virus , environmental science , ecology , biology , statistics , cartography , virology , mathematics , alphavirus
Objectives:1) To determine whether environmental and mosquito abundance variables could be used to explain fluctuations in the activity of Ross River (RR) virus, in the River Murray Valley of South Australia (SA). 2) To develop models at the local government spatial scale to understand local variability in RR activity factors.Method:Notification data of RR virus positive serology, mosquito surveillance, meteorological and river height data were analysed for the period 1999 to 2006. Stepwise multiple regression was used to determine significant environmental factors and to create descriptive models.Results:The three models developed for different regions of the Valley explained significant amounts of variation in notification rates (R 2 0.77 – 0.98). Regional variation in the models was observed, with differences in significant mosquito species evident. Rainfall was a significant predictor of RR virus activity in two of the models, while the height of the River Murray was significant in the third. An overall model for the entire SA section of the Valley contained only time‐lagged mosquito abundance variables (R 2 0.52).Conclusion:Although rainfall, river height and mosquito abundance are significant factors in determining RR virus activity, there are regional differences in this relationship.Implications:The regional variability of RR virus activity drivers has been defined, and has implications for the forecasting of future activity in this part of SA. The models provided here can provide the foundation for an effective RR virus early warning system, but only if criteria for action, lines of responsibility and the resources required have been determined.