Ross River Virus Disease Research Articles

Auto-Abs neutralizing type I IFNs in patients with severe Powassan, Usutu, or Ross River virus disease.

Arboviral diseases are a growing global health concern. Pre-existing autoantibodies (auto-Abs) neutralizing type I interferons (IFNs) can underlie encephalitis due to West Nile virus (WNV) (∼40% of patients) and tick-borne encephalitis (TBE, due to TBE virus [TBEV]) (∼10%). We report here that these auto-Abs can also underlie severe forms of rarer arboviral infections. Auto-Abs neutralizing high concentrations of IFN-α2, IFN-β, and/or IFN-ω are present in the single case of severe Powassan virus (POWV) encephalitis studied, two of three cases of severe Usutu virus (USUV) infection studied, and the most severe of 24 cases of Ross River virus (RRV) disease studied. These auto-Abs are not found in any of the 137 individuals with silent or mild infections with these three viruses. Thus, auto-Abs neutralizing type I IFNs underlie an increasing list of severe arboviral diseases due to Flaviviridae (WNV, TBEV, POWV, USUV) or Togaviridae (RRV) viruses transmitted to humans by mosquitos (WNV, USUV, RRV) or ticks (TBEV, POWV).

Multi-Network-Based Ensemble Deep Learning Model to Forecast Ross River Virus Outbreak in Australia

Ross River virus (RRV) disease is one of the most epidemiological mosquito-borne diseases in Australia. Its major consequences on public health require building a precise and accurate model for predicting any forthcoming outbreaks. Several models have been developed by machine learning (ML) researchers, and many studies have been published as a result. Later, deep learning models have been introduced and shown tremendous success in forecasting, mainly the long short-term memory (LSTM), which performs significantly better than the traditional machine learning approaches. There are four common problems that previously developed models need to solve. They are exploding gradient, vanishing gradient, uncertainty and parameter bias. LSTM has already solved the first two problems, i.e. exploding and vanishing gradient problems, and the remaining two are overcome by [Formula: see text]-LSTM. However, developing a prediction model for the RRV disease is a challenging task because it presents a wide range of symptoms, and there needs to be more accurate information available on the disease. To address these challenges, we propose a data-driven ensemble deep learning model using multi-networks of LSTM neural network for RRV disease forecasting in Australia. Data is collected between 1993 and 2020 from the Health Department of the Government of Australia. Data from 1993 to 2016 is taken to train the model, while the data of 2016–2020 is used as a test dataset. Previous research has demonstrated the efficacy of both ARIMA and exponential smoothing techniques in the field of time-series forecasting. As a result, our study sought to evaluate the performance of our proposed model in comparison to these established parametric methods, including ARIMA and ARMA, as well as the more recent deep learning approaches such as encoder–decoder and attention mechanism models. The results show that [Formula: see text]-LSTM achieves higher accuracy and has a less mean-square error. We have also discussed the comparison of the models in detail. Such forecasting gives an insight into being well prepared and handling the situation of the outbreak.

Role of MXRA8 in Ross River Virus Disease Pathogenesis.

Arthritogenic alphaviruses such as Ross River virus (RRV) and Chikungunya virus (CHIKV) are responsible for large-scale epidemics that cause debilitating acute and chronic musculoskeletal diseases. MXRA8 was recently discovered as an entry receptor for multiple alphaviruses including CHIKV, RRV, Mayaro virus (MAYV), and O'nyong-nyong virus (ONNV). However, the role of MXRA8 in the development of alphavirus-induced musculoskeletal inflammation has not yet been fully studied. Here, we attempt to fully characterize the contribution of MXRA8 to RRV disease in an established mouse model. MXRA8 knockout (MXRA8-/-) mice generated on a C57BL/6J background, showed abrogated disease signs and reduced viral replication, which correlated with lower viral load, diminished proinflammatory cytokines, and limited cell infiltrates in inflamed tissues. Immunomodulation genes were upregulated to higher levels in RRV-infected wild-type (WT) mice than in MXRA8-/- mice. Intriguingly, Cdkn1a and Ifi44 genes in blood and CD127/IL7RA, CD45, BatF3, IFNGR, Ly6G/Ly6C, CD40, CD127, F4/80, and MHC-II genes in quadriceps were found to be upregulated in RRV-infected MXRA8-/- mice compared to WT mice. Our results showed an essential role of MXRA8 in the immune response of mice infected with RRV and, more importantly, demonstrated novel changes in immunomodulation genes, which shed light on the immunopathogenesis of alphavirus-induced disease. IMPORTANCE Previous studies have shown the importance of the cell surface protein MXRA8 as an entry receptor for several different prominent alphaviruses such as CHIKV, RRV, MAYV, and ONNV. In particular, the role of MXRA8 in the tissue tropism, viral pathogenesis, and immune response of a CHIKV mouse model have already been briefly characterized. However, the role of MXRA8 warrants further characterization in RRV disease background, since there are noticeable differences in the disease profile between CHIKV and RRV. For example, patients infected with CHIKV are usually affected by sudden onset of severe arthritis and fever, whereas RRV-infected patients generally only have minor joint pain and mild fever. Here, we characterized the role of MXRA8 in RRV disease and assessed several key mechanisms of MXRA8 that may contribute to the disease progression.

Pathways Activated by Infected and Bystander Chondrocytes in Response to Ross River Virus Infection.

Old world alphaviruses, such as Ross River virus (RRV), cause debilitating arthralgia during acute and chronic stages of the disease. RRV-induced cartilage degradation has been implicated as a cause of joint pain felt by RRV patients. Chondrocytes are a major cell type of cartilage and are involved in the production and maintenance of the cartilage matrix. It is thought that these cells may play a vital role in RRV disease pathogenesis. In this study, we used RNA-sequencing (RNA-Seq) to examine the transcriptomes of RRV-infected and bystander chondrocytes in the same environment. RRV containing green fluorescent protein (GFP) allowed for the separation of RRV-infected (GFP+) and bystander uninfected cells (GFP-). We found that whereas GFP+ and GFP- populations commonly presented similar gene expression profiles during infection, there were also unique signatures. For example, RIMS2 and FOXJ1 were unique to GFP+ cells, whilst Aim2 and CCL8 were only found in bystander chondrocytes. This indicates that careful selection of potential therapeutic targets is important to minimise adverse effects to the neighbouring uninfected cell populations. Our study serves as a resource to provide more information about the pathways and responses elicited by RRV in cells which are both infected and stimulated because of neighbouring infected cells.

N-Linked Glycans Shape Skin Immune Responses during Arthritis and Myositis after Intradermal Infection with Ross River Virus.

Arthritogenic alphaviruses are mosquito-borne arboviruses that include several re-emerging human pathogens, including the chikungunya (CHIKV), Ross River (RRV), Mayaro (MAYV), and o'nyong-nyong (ONNV) virus. Arboviruses are transmitted via a mosquito bite to the skin. Herein, we describe intradermal RRV infection in a mouse model that replicates the arthritis and myositis seen in humans with Ross River virus disease (RRVD). We show that skin infection with RRV results in the recruitment of inflammatory monocytes and neutrophils, which together with dendritic cells migrate to draining lymph nodes (LN) of the skin. Neutrophils and monocytes are productively infected and traffic virus from the skin to LN. We show that viral envelope N-linked glycosylation is a key determinant of skin immune responses and disease severity. RRV grown in mammalian cells elicited robust early antiviral responses in the skin, while RRV grown in mosquito cells stimulated poorer early antiviral responses. We used glycan mass spectrometry to characterize the glycan profile of mosquito and mammalian cell-derived RRV, showing deglycosylation of the RRV E2 glycoprotein is associated with curtailed skin immune responses and reduced disease following intradermal infection. Altogether, our findings demonstrate skin infection with an arthritogenic alphavirus leads to musculoskeletal disease and envelope glycoprotein glycosylation shapes disease outcome. IMPORTANCE Arthritogenic alphaviruses are transmitted via mosquito bites through the skin, potentially causing debilitating diseases. Our understanding of how viral infection starts in the skin and how virus systemically disseminates to cause disease remains limited. Intradermal arbovirus infection described herein results in musculoskeletal pathology, which is dependent on viral envelope N-linked glycosylation. As such, intradermal infection route provides new insights into how arboviruses cause disease and could be extended to future investigations of skin immune responses following infection with other re-emerging arboviruses.

Interleukin-17 contributes to Ross River virus-induced arthritis and myositis.

Arthritogenic alphaviruses are mosquito-borne viruses that are a major cause of infectious arthropathies worldwide, and recent outbreaks of chikungunya virus and Ross River virus (RRV) infections highlight the need for robust intervention strategies. Alphaviral arthritis can persist for months after the initial acute disease, and is mediated by cellular immune responses. A common strategy to limit inflammation and pathology is to dampen the overwhelming inflammatory responses by modulating proinflammatory cytokine pathways. Here, we investigate the contribution of interleukin-17 (IL-17), a cytokine involved in arthropathies such as rheumatoid arthritis, in the development RRV-induced arthritis and myositis. IL-17 was quantified in serum from RRV-infected patients, and mice were infected with RRV and joints and muscle tissues collected to analyse cellular infiltrates, tissue mRNA, cytokine expression, and joint and muscle histopathology. IL-17 expression was increased in musculoskeletal tissues and serum of RRV-infected mice and humans, respectively. IL-17–producing T cells and neutrophils contributed to the cellular infiltrate in the joint and muscle tissue during acute RRV disease in mice. Blockade of IL-17A/F using a monoclonal antibody (mAb) reduced disease severity in RRV-infected mice and led to decreased proinflammatory proteins, cellular infiltration in synovial tissues and cartilage damage, without affecting viral titers in inflamed tissues. IL-17A/F blockade triggered a shift in transcriptional profile of both leukocyte infiltrates and musculoskeletal stromal cells by downregulating proinflammatory genes. This study highlights a previously uncharacterized role for an effector cytokine in alphaviral pathology and points towards potential therapeutic benefit in targeting IL-17 to treat patients presenting with RRV-induced arthropathy.

TIR-Domain-Containing Adapter-Inducing Interferon-β (TRIF)-Dependent Antiviral Responses Protect Mice against Ross River Virus Disease.

ABSTRACTRoss River virus (RRV) is the major mosquito-borne virus in the South Pacific region. RRV infections are characterized by arthritic symptoms, which can last from several weeks to months. Type I interferon (IFN), the primary antiviral innate immune response, is able to modulate adaptive immune responses. The relationship between the protective role of type I IFN and the induction of signaling proteins that drive RRV disease pathogenesis remains poorly understood. In the present study, the role of TIR-domain-containing adapter-inducing interferon-β (TRIF), an essential signaling adaptor protein downstream of Toll-like receptor (TLR) 3, a key single-stranded RNA (ssRNA)-sensing receptor, was investigated. We found that TRIF−/− mice were highly susceptible to RRV infection, with severe disease, high viremia, and a low type I IFN response early during disease development, which suggests the TLR3-TRIF axis may engage early in response to RRV infection. The number and the activation level of CD4+ T cells, CD8+ T cells, and NK cells were reduced in TRIF−/− mice compared to those in infected wild-type (WT) mice. In addition, the number of germinal center B cells was lower in TRIF−/− mice than WT mice following RRV infection, with lower titers of IgG antibodies detected in infected TRIF−/− mice compared to WT. Interestingly, the requirement for TRIF to promote immunoglobulin class switch recombination was at the level of the local immune microenvironment rather than B cells themselves. The slower resolution of RRV disease in TRIF−/− mice was associated with persistence of the RRV genome in muscle tissue and a continuing IFN response.

Standing Water and Missing Data: The Murky Relationship between Flooding and Mosquito-Borne Diseases.

Standing Water and Missing Data: The Murky Relationship between Flooding and Mosquito-Borne Diseases.

Spatial and Temporal Patterns of Ross River Virus in Queensland, 2001-2020.

Ross River virus (RRV), the most common human arbovirus infection in Australia, causes significant morbidity and substantial medical costs. About half of Australian cases occur in Queensland. We describe the spatial and temporal patterns of RRV disease in Queensland over the past two decades. RRV notifications, human population data, and weather data from 2001 to 2020 were analysed by the Statistical Area Level 2 (SA2) area. Spatial interpolation or linear extrapolation were used for missing weather values and the estimated population in 2020, respectively. Notifications and incidence rates were analysed through space and time. During the study period, there were 43,699 notifications in Queensland. The highest annual number of notifications was recorded in 2015 (6182), followed by 2020 (3160). The average annual incidence rate was 5 per 10,000 people and the peak period for RRV notifications was March to May. Generally, SA2 areas in northern Queensland had higher numbers of notifications and higher incidence rates than SA2 areas in southern Queensland. The SA2 areas with high incidence rates were in east coastal areas and western Queensland. The timely prediction may aid disease prevention and routine vector control programs, and RRV management plans are important for these areas.

Ross River Virus Infection: A Cross-Disciplinary Review with a Veterinary Perspective.

Ross River virus (RRV) has recently been suggested to be a potential emerging infectious disease worldwide. RRV infection remains the most common human arboviral disease in Australia, with a yearly estimated economic cost of $4.3 billion. Infection in humans and horses can cause chronic, long-term debilitating arthritogenic illnesses. However, current knowledge of immunopathogenesis remains to be elucidated and is mainly inferred from a murine model that only partially resembles clinical signs and pathology in human and horses. The epidemiology of RRV transmission is complex and multifactorial and is further complicated by climate change, making predictive models difficult to design. Establishing an equine model for RRV may allow better characterization of RRV disease pathogenesis and immunology in humans and horses, and could potentially be used for other infectious diseases. While there are no approved therapeutics or registered vaccines to treat or prevent RRV infection, clinical trials of various potential drugs and vaccines are currently underway. In the future, the RRV disease dynamic is likely to shift into temperate areas of Australia with longer active months of infection. Here, we (1) review the current knowledge of RRV infection, epidemiology, diagnostics, and therapeutics in both humans and horses; (2) identify and discuss major research gaps that warrant further research.

Optimising predictive modelling of Ross River virus using meteorological variables.

BackgroundStatistical 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 findingsWe 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/SignificanceWe 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.

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. 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. 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. 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: Asystematic 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.

Arboviruses related with chronic musculoskeletal symptoms.

Arboviruses (ARthropods BOrne VIRUSES) are disease-causing viruses transmitted through the bite of hematophagous arthropods, such as mosquitoes and ticks. Among these, the alphavirus, genus of the Togaviridae family, is considered the most arthritogenic species, responsible for diseases such as chikungunya fever (CHIK), O'nyong-nyong virus fever, Ross River virus disease, Barmah Forest virus disease, Sindbis virus disease, and Mayaro fever. These arboviral diseases, especially CHIK, have impacted public health in recent decades, leading to devastating epidemics, particularly in developing countries, due to their high potential for chronicity, functional impairment, and great impact on the quality of life. In a similar way, chronic musculoskeletal symptoms have been described in all alphavirus infections. However, CHIK is the best studied. The purpose of this article is to review physiopathology, clinical manifestations, diagnosis, and treatment of alphaviruses, focusing on CHIK and chronic evolution of musculoskeletal symptoms.

Identification of Natural Molecular Determinants of Ross River Virus Type I Interferon Modulation

Ross River virus (RRV) belongs to the genus Alphavirus and is prevalent in Australia. RRV infection can cause arthritic symptoms in patients, and may include rash, fever, arthralgia and myalgia. Type I interferons (IFN) are the primary antiviral cytokines and trigger activation of the host innate immune system to suppress the replication of invading viruses. Alphaviruses are able to subvert the type I IFN system, but the mechanisms used are ill defined. In this study, seven RRV field strains were analyzed for induction of and sensitivity to type I IFN. The sensitivities of these strains to human IFN-β varied significantly and were highest for RRV 2548 strain. Compared to prototype laboratory strain RRV-T48, RRV 2548 also induced higher type I IFN levels both in vitro and in vivo and caused milder disease. To identify the determinants involved in type I IFN modulation, the region encoding the nonstructural proteins (nsPs) of RRV 2548 was sequenced, and 42 amino acid differences from RRV-T48 were identified. Using fragment swapping and site-directed mutagenesis, we discovered that substitutions E402A and R522Q in nsP1 as well as Q619R in nsP2 were responsible for increased sensitivity of RRV 2548 to type I IFN. In contrast, substitutions A31T, N219T, S580L and Q619R in nsP2 led to induction of higher levels of type I IFN. With exception of E402A all these variations are common for naturally occurring RRV strains. However, they are different from all known determinants of type I IFN modulation reported previously in nsPs of alphaviruses.Importance By identifying natural RRV amino acid determinants for type I IFN modulation, this study gives further insight into the mechanism of type I IFN modulation by alphaviruses. Here, the crucial role of type I IFN in the early stages of RRV disease pathogenesis is further demonstrated. This study also provides a comparison of the roles of different parts of the RRV nonstructural region in type I IFN modulation, highlighting the importance of nsP1 and nsP2 in this process. Three substitutions in nsP1 and nsP2 were found to be independently associated with enhanced type I IFN sensitivity, and four independent substitutions in nsP2 were important in elevated type I IFN induction. Such evidence has clear implications for RRV immunobiology, persistence and pathology. The identification of viral proteins that modulate type I IFN may also have importance for the pathogenesis of other alphaviruses.

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.

Prophylactic Antiheparanase Activity by PG545 Is Antiviral In Vitro and Protects against Ross River Virus Disease in Mice

Recently we reported on the efficacy of pentosan polysulfate (PPS), a heparan sulfate mimetic, to reduce the recruitment of inflammatory infiltrates and protect the cartilage matrix from degradation in Ross River virus (RRV)-infected PPS-treated mice. Here, we describe both prophylactic and therapeutic treatment with PG545, a low-molecular-weight heparan sulfate mimetic, for arthritogenic alphaviral infection. We first assessed antiviral activity in vitro through a 50% plaque reduction assay. Increasing concentrations of PG545 inhibited plaque formation prior to viral adsorption in viral strains RRV T48, Barmah Forest virus 2193, East/Central/South African chikungunya virus (CHIKV), and Asian CHIKV, suggesting a strong antiviral mode of action. The viral particle-compound dissociation constant was then evaluated through isothermal titration calorimetry. Furthermore, prophylactic RRV-infected PG545-treated mice had reduced viral titers in target organs corresponding to lower clinical scores of limb weakness and immune infiltrate recruitment. At peak disease, PG545-treated RRV-infected mice had lower concentrations of the matrix-degrading enzyme heparanase in conjunction with a protective effect on tissue morphology, as seen in the histopathology of skeletal muscle. Enzyme-linked immunosorbent assay quantification of cartilage oligomeric matrix protein and cross-linked C-telopeptides of type II collagen as well as knee histopathology showed increased matrix protein degradation and cartilage erosion in RRV-infected phosphate-buffered saline-treated mice compared to their PG545-treated RRV-infected counterparts. Taken together, these findings suggest that PG545 has a direct antiviral effect on arthritogenic alphaviral infection and curtails RRV-induced inflammatory disease when administered as a prophylaxis.

Ross River virus disease clinical presentation, pathogenesis and current therapeutic strategies

Ross River virus (RRV) is an arthitogenic alphavirus capable of causing outbreaks of debilitating musculoskeletal inflammatory disease in humans. RRV is the most common mosquito-borne disease in Australia, with outbreaks of RRV generally occurring during seasonal wet and warm conditions. Patients with Ross River virus disease (RRVD) typically present with fever, polyarthralgia, myalgia and a maculopapular erythematous rash. Treatment of the disease is usually palliative with no licensed vaccines or antiviral therapies currently available. In an effort to better inform therapeutic design, much progress has been made to understand the pathogenesis of RRVD. Progress has been largely driven by clinical evaluations supported by research using established murine models of RRVD, able to accurately replicate human disease. In this review we describe RRVD pathogenesis and the role of the host immune response, with particular focus on insights from studying animal models. We also discuss prospects for effective vaccines, preclinical development of therapeutic strategies and raise important questions for future RRV research.

A model for the dynamics of Ross River Virus in the Australian environment

Ross River Disease is a mosquito-borne viral condition that affects pockets of the Australian human population, and can be debilitating in some instances. The evidence is that the virus reservoirs in marsupials, such as kangaroos, and this may account for the unpredictable outbreaks of the disease in humans. Accordingly, we present here a new model for the dynamics of Ross River Virus (RRV) in populations of mosquitoes and kangaroos. We calculate steady-state populations for the sub-groups in each species and demonstrate that naturally occurring oscillations in the populations (limit cycles) do not occur. When seasonal forcing of vector populations and kangaroo birth rates is taken into account, however, the model may predict multi-annual outbreaks and chaos, perhaps explaining the unpredictability of some RRV disease epidemics, particularly across southern Australia. Detailed results in this case are presented.

Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.

Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.