This study sought to describe RRV spatial and temporal incidence patterns in SEQ, and to identify epidemiological trends that help elucidate the drivers of virus spillover. We found that incidence rates were highest for females, for age groups between 30–64 years, and for residents of rural suburbs (SSCs), especially those north of Brisbane. Suburbs around the edges of major urban areas were persistent annual hot spots for RRV disease. This suggests that suburbs in rural and peri-urban areas possess characteristics that promote circulation of RRV, possibly related to specific habitat or land use types present. The specifc contributors to human infection in different environments are uncertain, and this requires further investigation. In particular, identification of RRV vectors and hosts in areas where natural and urbanised environments meet.
The demographic trends we observed were comparable with those of previous Australian studies, with the highest rates in females, and in age groups between 30 and 64 years (3, 8, 9, 11, 41). Although male to female prevalence ratios have varied slightly in previous studies, no overall gender-related risk has been apparent (3, 8). Clinical studies report that children show fewer symptoms than adults, presumably due to age-related differences in immune responses, while symptoms tend to persist longer in adults (8, 11, 41, 42). However, seroprevalence studies have shown RRV antibody seroconversion to increase with age (43–45) suggesting that the true incidence of infection may be higher in younger age groups than indicated by notified cases. In all likelihood, exposure to RRV is probably equivalent across all ages and genders, with higher reports in adults due to differing disease manifestations with age. Disease rates will also vary with geographical region, as the tropical northern regions of Australia have higher rates than the temperate south (1). Because few comprehensive seroprevalence studies have been conducted in SEQ, the true age-related burden is unknown.
There was a strikingly consistent seasonal trend in outbreaks across SEQ, peaking annually between the months of February and May. This coincides with periods of relatively high temperature and rainfall from late austral summer to early autumn, when SEQ’s average daily temperatures are 20–24◦C (46). Although climate alone does not predict outbreak occurrence, it influences vector and wildlife host species’ abundance (47–50). Favourable temperature conditions, rainfall, high tides and low-level flooding have all been associated with elevated RRV risk in previous studies (26–28, 51–53). Temperature also impacts viral replication, with the ideal temperature for RRV transmission at 26.4 degrees celsius (transmission range of 17 to 31.5◦C) (54). However, variations in weather patterns and vector-host ecology mean that climate-based predictions are only valid locally or, at best, regionally, rather than nationally (55, 56). Hence, although suitable weather conditions are a requisite precursor to outbreaks, outbreak occurrence ultimately depends on availability of, and interactions between, sufficient competent vectors and susceptible hosts (57, 58).
The variable annual spatial trend observed suggests that conditions supporting transmission occur sporadically in particular SSCs, and may change from one year to the next. This might be due to local climatic and environmental variations which influence vector and host abundance in both freshwater and saltwater habitats (23, 27). Freshwater vectors Cx. annulirostris, Ae. notoscriptus, Ae. procax, and Ae. vittiger as well as saltwater vectors Ae. vigilax, Cx. sitiens and Verallina funerea were associated with large outbreaks in Brisbane and the Sunshine Coast Region during the 1990s (14, 59). The most recent outbreaks of 2014–2015 were linked to increased abundance of the freshwater vectors Cx. annulirostris and Ae. procax in Brisbane following high rainfall (17). Many of the implicated vectors share similar or overlapping habitat types and have broad host-feeding behaviours (25). Hence, the relative contribution of different vectors is to human outbreaks is challenging to disentangle. It is possible that multiple vectors in different habitats contribute to varying degrees, at different times (60, 61).
Similarly, a number of different hosts that maintain RRV circulation across SEQ could contribute to epidemics. Although few studies have investigated the role of specific wildlife hosts in human RRV outbreaks, opportunistic serosurveys of wildlife together with a handful of experimental infection studies have generated some hypotheses (21). Potential hosts theorised to contribute to RRV transmission include birds, small mammals and marsupials (including rodents, possums, flying foxes) in urban areas; and larger mammals and marsupial macropods (such as horses and cattle, kangaroos and wallabies) in peri-urban and rural areas (15, 20, 21, 57). However, current evidence identifying important RRV hosts is limited, and broader investigations of the transmission potential of wildlife are much needed (57). In the absence of these, it can be assumed from RRV’s wide geographic and habitat range that there is flexibility in both vectors and hosts. In SEQ, the seasonal composition of vectors and hosts in peri-urban habitats, especially those in proximity to hot spot suburbs, should be a particular focus for future RRV transmission studies.
We identified both high incidence rates and the most persistent hot spots overall in Noosa Shire and Sunshine Coast Region, in which there are low-medium human population densities and diverse land use types present. The specific drivers of high rates of RRV in these LGAs are uncertain, but could relate to the proximity of peri-urban human populations to rural vector and wildlife habitats. Interactions between humans, vectors and wildlife in or near particular land use types in peri-urban areas could create a ‘perfect storm’ of factors supporting RRV transmission. Human-modified and fragmented landscapes are known to influence the risk of vector borne diseases, either positively or negatively, through altering ecological relationships between wildlife, vectors and humans. (62–64). Land use changes such as deforestation and agricultural development have been linked to increased risk of West Nile virus and malaria infection (65, 66), and have been linked to arboviral disease risk in Australia (67, 68). While our findings do not confirm a link between land use and RRV risk, they do suggest this could be worthy of investigation. Given RRV’s expansion into urban and outer metropolitan areas of Australia in recent years, it is conceivable that urban expansion and alteration of wildlife habitats may have implications for RRV risk.
The absence of hot spots, and concentration of cold spots, in the Scenic Rim LGA suggests that it lacks sufficient human, wildlife host and vector populations to maintain persistent outbreaks. This is despite the Scenic Rim having a large proportion of natural conservation and irrigated agricultural areas, which could theoretically support mosquito and wildlife habitats. This might be explained by the low human population in this LGA, which results in sporadically high but inconsistent incidence rates, and unstable spatial patterns of disease (both hot and cold spots in the same suburbs in different years). This pattern could potentially change if human populations in the Scenic Rim were to increase. Again, analyses of different land use types, their association with specific vector and host habitats, and RRV risk would assist understanding of how and where these factors inter-relate.
This study is the first to describe long-term epidemiological trends of RRV across SEQ. We report RRV disease patterns at both broad (LGA) and fine (SSC) scales, and identify characteristics associated with higher RRV risk that can inform future investigations. However, the study was limited by its reliance on routinely collected public health data, and the associated challenges with passive disease reporting. In Queensland, notification processes for RRV do not include individual case interview, nor information on the timing and location of RRV exposure, which is often unknown. We used the case’s reported onset date of symptoms and residential address as a proxy for this. While we expect that many residents will be bitten and infected in their home suburb, this will not be true for all, and there is no way to correct for this. Nevertheless, given RRV’s high incidence and wide geographical range across SEQ, using the case residence seems a reasonable proxy for location of infection. Socioecological factors not accounted for in our study may also have influenced the demographic trends we observed. For instance, healthcare-seeking practices likely differ between genders and age-groups, and exposure to mosquitoes through occupational or leisure activities could also differ between demographic groups. The impact of socioeconomic factors on infection risk has also varies by geographic region (22, 23, 28). However, within our study region, previous studies indicate that socioeconomic variation is unlikely to have had a significant impact on our results (28, 32).