Predictive modelling of Ross River virus notifications in southeastern Australia

Arbovirus Transmission in Australia from 2002 to 2017

Arboviruses pose a significant global public health threat, with Ross River virus (RRV), Barmah Forest virus (BFV), and dengue virus (DENV) being among the most common and clinically significant in Australia. Some arboviruses, including those prevalent in Australia, have been reported to cause transfusion-transmitted infections. This study examined the spatiotemporal variation of these arboviruses and their potential impact on blood donation numbers across Australia. Using data from the Australian Department of Health on eight arboviruses from 2002 to 2017, we retrospectively assessed the distribution and clustering of incidence rates in space and time using Geographic Information System mapping and space-time scan statistics. Regression models were used to investigate how weather variables, their lag months, space, and time affect case and blood donation counts. The predictors' importance varied with the spatial scale of analysis. Key predictors were average rainfall, minimum temperature, daily temperature variation, and relative humidity. Blood donation number was significantly associated with the incidence rate of all viruses and its interaction with local transmission of DENV, overall. This study, the first to cover eight clinically relevant arboviruses at a fine geographical level in Australia, identifies regions at risk for transmission and provides valuable insights for public health intervention.

Current and future burden of Ross River virus infection attributable to increasing temperature in Australia: a population-based study

Background

Ross River virus (RRV), Australia's most notifiable vector-borne disease transmitted through mosquito bites, has seen increased transmission due to rising temperatures. Quantifying the burden of RRV infection attributable to increasing temperatures (both current and future) is pivotal to inform prevention strategies in the context of climate change.

Methods

As RRV-related deaths are rare in Australia, we utilised years lived with disability (YLDs) associated with RRV infection data from the Australian Institute of Health and Welfare (AIHW) Burden of Disease database between 2003 and 2018. We obtained relative risks per 1 °C temperature increase in RRV infection from a previous meta-analysis. Exposure distributions for each Köppen-Geiger climate zone were calculated separately and compared with the theoretical-minimum-risk exposure distribution to calculate RRV burden attributable to increasing temperatures during the baseline period (2003-2018), and projected future burdens for the 2030s and 2050s under two greenhouse gas emission scenarios (Representative Concentration Pathways, RCP 4.5 and RCP 8.5), two adaptation scenarios, and different population growth series.

Findings

During the baseline period (2003-2018), increasing mean temperatures contributed to 35.8 (±0.5) YLDs (19.1%) of the observed RRV burden in Australia. The mean temperature attributable RRV burden varied across climate zones and jurisdictions. Under both RCP scenarios, the projected RRV burden is estimated to increase in the future despite adaptation scenarios. By the 2050s, without adaptation, the RRV burden could reach 45.8 YLDs under RCP4.5 and 51.1 YLDs under RCP8.5. Implementing a 10% adaptation strategy could reduce RRV burden to 41.8 and 46.4 YLDs, respectively.

Interpretation

These findings provide scientific evidence for informing policy decisions and guiding resource allocation for mitigating the future RRV burden. The current findings underscore the need to develop location-specific adaptation strategies for climate-sensitive disease control and prevention.

Funding

Australian Research Council Discovery Program.

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 β = 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.

Prediction of Ross River Virus Incidence Using Mosquito Data in Three Cities of Queensland, Australia

Ross River virus (RRV) is the most common mosquito-borne disease in Australia, with Queensland recording high incidence rates (with an annual average incidence rate of 0.05% over the last 20 years). Accurate prediction of RRV incidence is critical for disease management and control. Many factors, including mosquito abundance, climate, weather, geographical factors, and socio-economic indices, can influence the RRV transmission cycle and thus have potential utility as predictors of RRV incidence. We collected mosquito data from the city councils of Brisbane, Redlands, and Mackay in Queensland, together with other meteorological and geographical data. Predictors were selected to build negative binomial generalised linear models for prediction. The models demonstrated excellent performance in Brisbane and Redlands but were less satisfactory in Mackay. Mosquito abundance was selected in the Brisbane model and can improve the predictive performance. Sufficient sample sizes of continuous mosquito data and RRV cases were essential for accurate and effective prediction, highlighting the importance of routine vector surveillance for disease management and control. Our results are consistent with variation in transmission cycles across different cities, and our study demonstrates the usefulness of mosquito surveillance data for predicting RRV incidence within small geographical areas.

Prediction of Ross River virus incidence in Queensland, Australia: building and comparing models

Transmission of Ross River virus (RRV) is influenced by climatic, environmental, and socio-economic factors. Accurate and robust predictions based on these factors are necessary for disease prevention and control. However, the complicated transmission cycle and the characteristics of RRV notification data present challenges. Studies to compare model performance are lacking. In this study, we used RRV notification data and exposure data from 2001 to 2020 in Queensland, Australia, and compared ten models (including generalised linear models, zero-inflated models, and generalised additive models) to predict RRV incidence in different regions of Queensland. We aimed to compare model performance and to evaluate the effect of statistical over-dispersion and zero-inflation of RRV surveillance data, and non-linearity of predictors on model fit. A variable selection strategy for screening important predictors was developed and was found to be efficient and able to generate consistent and reasonable numbers of predictors across regions and in all training sets. Negative binomial models generally exhibited better model fit than Poisson models, suggesting that over-dispersion in the data is the primary factor driving model fit compared to non-linearity of predictors and excess zeros. All models predicted the peak periods well but were unable to fit and predict the magnitude of peaks, especially when there were high numbers of cases. Adding new variables including historical RRV cases and mosquito abundance may improve model performance. The standard negative binomial generalised linear model is stable, simple, and effective in prediction, and is thus considered the best choice among all models.

Associations between temperature and Ross river virus infection: A systematic review and meta-analysis of epidemiological evidence

Ross River virus (RRV) infection is one of the emerging and prevalent arboviral diseases in Australia and the Pacific Islands. Although many studies have been conducted to establish the relationship between temperature and RRV infection, there has been no comprehensive review of the association so far. In this study, we performed a systematic review and meta-analysis to assess the effect of temperature on RRV transmission. We searched PubMed, Scopus, Embase, and Web of Science with additional lateral searches from references. The quality and strength of evidence from the included studies were evaluated following the Navigation Guide framework. We have qualitatively synthesized the evidence and conducted a meta-analysis to pool the relative risks (RRs) of RRV infection per 1 °C increase in temperature. Subgroup analyses were performed by climate zones, temperature metrics, and lag periods. A total of 17 studies met the inclusion criteria, of which six were included in the meta-analysis The meta-analysis revealed that the overall RR for the association between temperature and the risk of RRV infection was 1.09 (95% confidence interval (CI): 1.02, 1.17). Subgroup analyses by climate zones showed an increase in RRV infection per 1 °C increase in temperature in humid subtropical and cold semi-arid climate zones. The overall quality of evidence was "moderate" and we rated the strength of evidence to be "limited", warranting additional evidence to reduce uncertainty. The results showed that the risk of RRV infection is positively associated with temperature. However, the risk varies across different climate zones, temperature metrics and lag periods. These findings indicate that future studies on the association between temperature and RRV infection should consider local and regional climate, socio-demographic, and environmental factors to explore vulnerability at local and regional levels.

Quantitative PCR assay for the detection of Aedes vigilax in mosquito trap collections containing large numbers of morphologically similar species and phylogenetic analysis of specimens collected in Victoria, Australia

Background

Aedes vigilax is one of the most significant arbovirus vector and pest species in Australia’s coastal regions. Occurring in multiple countries, this mosquito species occurs as a species complex which has been separated into three clades with two detected in Australia. Until recently, Ae. vigilax has largely been absent from Victoria, only occasionally caught over the years, with no reported detections from 2010 to 2016. Complicating the detection of Ae. vigilax is the shared sympatric distribution to the morphologically similar Ae. camptorhynchus, which can exceed 10,000 mosquitoes in a single trap night in Victoria. Currently, there are no molecular assays available for the detection of Ae. vigilax. We aim to develop a quantitative PCR (qPCR) for the detection of Ae. vigilax, with the specificity and sensitivity of this assay assessed as well as a method to process whole mosquito traps.

Methods

Trapping was performed during the 2017–2020 mosquito season in Victoria in two coastal areas across these 3 consecutive years. A qPCR assay was designed to allow rapid identification of Ae. vigilax as well as a whole mosquito trap homogenizing and processing methodology. Phylogenetic analysis was performed to determine which clade Ae. vigilax from Victoria was closest to.

Results

Aedes vigilax was successfully detected each year across two coastal areas of Victoria, confirming the presence of this species. The qPCR assay was proven to be sensitive and specific to Ae. vigilax, with trap sizes up to 1000 mosquitoes showing no inhibition in detection sensitivity. Phylogenetic analysis revealed that Ae. vigilax from Victoria is associated with clade III, showing high sequence similarity to those previously collected in New South Wales, Queensland and Western Australia.

Conclusions

Aedes vigilax is a significant vector species that shares an overlapping distribution to the morphologically similar Ae. camptorhynchus, making detection difficult. Here, we have outlined the implementation of a specific and sensitive molecular screening assay coupled with a method to process samples for detection of Ae. vigilax in collections with large numbers of non-target species.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04923-y.

Optimising predictive modelling of Ross River virus using meteorological variables

BACKGROUND

Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.

METHODOLOGY/PRINCIPAL FINDINGS

We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.

CONCLUSIONS/SIGNIFICANCE

We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

Using ecological variables to predict Ross River virus disease incidence in South Australia

BACKGROUND

Ross River virus (RRV) disease is Australia's most widespread vector-borne disease causing significant public health concern. The aim of this study was to identify the ecological covariates of RRV risk and to develop epidemic forecasting models in a disease hotspot region of South Australia.

METHODS

Seasonal autoregressive integrated moving average models were used to predict the incidence of RRV disease in the Riverland region of South Australia, an area known to have a high incidence of the disease. The model was developed using data from January 2000 to December 2012 then validated using disease notification data on reported cases for the following year.

RESULTS

Monthly numbers of the mosquito Culex annulirostris (β=0.033, p<0.001) and total rainfall (β=0.263, p=0.002) were significant predictors of RRV transmission in the study region. The forecasted RRV incidence in the predictive model was generally consistent with the actual number of cases in the study area.

CONCLUSIONS

A predictive model has been shown to be useful in forecasting the occurrence of RRV disease, with increased vector populations and rainfall being important factors associated with transmission. This approach may be useful in a public health context by providing early warning of vector-borne diseases in other settings.

Epidemiological models for predicting Ross River virus in Australia: A systematic review

Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used time-series models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.

Ross River Virus Infection of Horses: Appraisal of Ecological and Clinical Consequences

Ross River virus (RRV) is a mosquito-borne arbovirus of the genus Alphavirus that causes disease in humans and horses in Australia. A temporal association of RRV infection in horses with clinical signs including pyrexia, malaise, and polyarthralgia has been reported, along with reduced athletic performance, often for extended periods. Despite these reports, disease due to RRV remains somewhat controversial as experimental infection of horses has resulted in obvious viraemia yet minimal signs of clinical disease. The relatively high viraemia demonstrated by horses infected with RRV has led to speculation that they could act as an important reservoir host of the virus, although this remains unclear. This review sought to appraise the existing literature relating to RRV infection of horses and to summarize the ecological and clinical consequences of RRV of relevance to the equine industry and to public health more broadly.

Flooding and Arboviral Disease: Predicting Ross River Virus Disease Outbreaks Across Inland Regions of South-Eastern Australia

Flood frequency is expected to increase across the globe with climate change. Understanding the relationship between flooding and arboviral disease can reduce disease risk and associated costs. South-eastern Australia is dominated by the flood-prone Murray-Darling River system where the incidence of Australia's most common arboviral disease, Ross River virus (RRV), is high. This study aimed to determine the relationship between riverine flooding and RRV disease outbreaks in inland south-eastern Australia, specifically New South Wales (NSW). Each study month from 1991 to 2013, for each of 37 local government areas (LGAs) was assigned 'outbreak/non-outbreak' status based on long-term trimmed-average age-standardized RRV notification rates and 'flood/non-flood' status based on riverine overflow. LGAs were grouped into eight climate zones with the relationship between flood and RRV outbreak modeled using generalized estimating equations. Modeling adjusted for rainfall in the previous 1-3 mo. Spring-summer flooding increased the odds of summer RRV outbreaks in three climate zones before and after adjusting for rainfall 1, 2, and 3 mo prior to the outbreak. Flooding at any time of the year was not predictive of RRV outbreaks in the remaining five climate zones. Predicting RRV disease outbreaks with flood events can assist with more targeted mosquito spraying programs, thereby reducing disease transmission and mosquito resistance.

The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia

Ross River virus (RRV) is Australia's most epidemiologically important mosquito-borne disease. During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications can account for up to 30% of national RRV notifications. However, little is known about factors which can forecast RRV transmission in Victoria. We aimed to understand factors associated with RRV transmission in epidemiologically important regions of Victoria and establish an early warning forecast system. We developed negative binomial regression models to forecast human RRV notifications across 11 Local Government Areas (LGAs) using climatic, environmental, and oceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 to June 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing data set. Evapotranspiration and precipitation were found to be common factors for forecasting RRV notifications across sites. Several site-specific factors were also important in forecasting RRV notifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreak in 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreak in 2016/17, however only six LGAs could predict the outbreak using the same model. We document similarities and differences in factors useful for forecasting RRV notifications across Victoria and demonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexity of RRV transmission where additional epidemiological information is needed to accurately predict RRV activity. Our findings have been applied to produce a Ross River virus Outbreak Surveillance System (ROSS) to aid in public health decision making in Victoria.

Improving the prediction of arbovirus outbreaks: A comparison of climate-driven models for West Nile virus in an endemic region of the United States

Models that forecast the timing and location of human arboviral disease have the potential to make mosquito control and disease prevention more effective. A common approach is to use statistical time-series models that predict disease cases as lagged functions of environmental variables. However, the simplifying assumptions required for standard modeling approaches may not capture important aspects of complex, non-linear transmission cycles. Here, we compared a set of alternative models of human West Nile virus (WNV) in 2004-2017 in South Dakota, USA. We used county-level logistic regressions to model historical human case data as functions of distributed lag summaries of air temperature and several moisture indices. We tested two variations of the standard model in which 1) the distributed lag functions were allowed to change over the transmission season, so that dependence on past meteorological conditions was time varying rather than static, and 2) an additional predictor was included that quantified the mosquito infection growth rate estimated from mosquito surveillance data. The best-fitting model included temperature and vapor pressure deficit as meteorological predictors, and also incorporated time-varying lags and the mosquito infection growth rate. The time-varying lags helped to predict the seasonal pattern of WNV cases, whereas the mosquito infection growth rate improved the prediction of year-to-year variability in WNV risk. These relatively simple and practical enhancements may be particularly helpful for developing data-driven time series models for use in arbovirus forecasting applications.

Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment

Two saltmarsh mosquitoes dominate the transmission of Ross River virus (RRV, Togoviridae: Alphavirus), one of Australia's most prominent mosquito-borne diseases. Ecologically, saltmarshes vary in their structure, including habitat types, hydrological regimes, and diversity of aquatic fauna, all of which drive mosquito oviposition behavior. Understanding the distribution of vector mosquitoes within saltmarshes can inform early warning systems, surveillance, and management of vector populations. The aim of this study was to identify the distribution of Ae. camptorhynchus, a known vector for RRV, across a saltmarsh and investigate the influence that other invertebrate assemblage might have on Ae. camptorhynchus egg dispersal. We demonstrate that vegetation is a strong indicator for Ae. camptorhynchus egg distribution, and this was not correlated with elevation or other invertebrates located at this saltmarsh. Also, habitats within this marsh are less frequently inundated, resulting in dryer conditions. We conclude that this information can be applied in vector surveillance and monitoring of temperate saltmarsh environments and also provides a baseline for future investigations into understanding mosquito vector habitat requirements.