Testing the intrinsic mechanisms driving the dynamics of Ross River Virus across Australia

The mechanisms driving dynamics of many epidemiologically important mosquito-borne pathogens are complex, involving combinations of vector and host factors (e.g., species composition and life-history traits), and factors associated with transmission and reporting. Understanding which intrinsic mechanisms contribute most to observed disease dynamics is important, yet often poorly understood. Ross River virus (RRV) is Australia's most important mosquito-borne disease, with variable transmission dynamics across geographic regions. We used deterministic ordinary differential equation models to test mechanisms driving RRV dynamics across major epidemic centers in Brisbane, Darwin, Mandurah, Mildura, Gippsland, Renmark, Murray Bridge, and Coorong. We considered models with up to two vector species (Aedes vigilax, Culex annulirostris, Aedes camptorhynchus, Culex globocoxitus), two reservoir hosts (macropods, possums), seasonal transmission effects, and transmission parameters. We fit models against long-term RRV surveillance data (1991-2017) and used Akaike Information Criterion to select important mechanisms. The combination of two vector species, two reservoir hosts, and seasonal transmission effects explained RRV dynamics best across sites. Estimated vector-human transmission rate (average beta = 8.04x10-4per vector per day) was similar despite different dynamics. Models estimate 43% underreporting of RRV infections. Findings enhance understanding of RRV transmission mechanisms, provide disease parameter estimates which can be used to guide future research into public health improvements and offer a basis to evaluate mitigation practices. Ross River virus (RRV) causes the highest number of vector-borne disease infections in Australia, yet the mechanisms driving its transmission across regions remains poorly understood. We analyzed long-term surveillance data from eight epidemic regions spanning tropical to temperate climates. We tested the importance of different mosquito vectors, wildlife hosts, and varies seasonal and transmission effects in explaining observed patterns of RRV notifications. Despite differing environments, models indicate combinations of two key mosquito vectors and two marsupial hosts, and interacting seasonally best explains RRV dynamics. Estimated mosquito-human transmission rates were similar across regions, whereas wildlife contributions varied. Models estimate 43% underreporting of RRV infections nationally. Findings provide new quantitative insights on transmission mechanisms and health impacts of RRV. Estimating mechanisms and key parameters allows for the future assessment of public health interventions like mosquito control. This modelling framework evaluating long-term data could be applied to other complex vector-borne diseases to unravel intrinsic drivers and guide mitigation strategies.