An "Uncharacterized" Australian Virus Is the Earliest Known Example of Ross River Virus with Changes in the nsP3 Protein Associated with the Explosive Outbreak of Ross River Virus Infection in the Pacific Region from 1979 to 1980.
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ABSTRACT:
SUBMITTER: Aaskov JG
PROVIDER: S-EPMC8601134 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Thermal biology predicts that vector-borne disease transmission peaks at intermediate temperatures and declines at high and low temperatures. However, thermal optima and limits remain unknown for most vector-borne pathogens. We built a mechanistic model for the thermal response of Ross River virus, an important mosquito-borne pathogen in Australia, Pacific Islands, and potentially at risk of emerging worldwide. Transmission peaks at moderate temperatures (26.4°C) and declines to zero at thermal limits (17.0 and 31.5°C). The model accurately predicts that transmission is year-round endemic in the tropics but seasonal in temperate areas, resulting in the nationwide seasonal peak in human cases. Climate warming will likely increase transmission in temperate areas (where most Australians live) but decrease transmission in tropical areas where mean temperatures are already near the thermal optimum. These results illustrate the importance of nonlinear models for inferring the role of temperature in disease dynamics and predicting responses to climate change.
| S-EPMC6112853 | biostudies-literature
Project description:The aim of this study was to compare murine immune responses, particularly CD8+ T cell activation, in acute infection of either Ross River virus (RRV) or lymphocytic choriomeningitis virus (LCMV). LCMV induces a strong CD8+ T cell response that is required for viral clearance. RRV induces CD8+ T cells that only affect viral clearance in certain tissue and do not contribute to prevention of viral RNA persistence in lymphoid and joint-associated tissue. We sequenced cells from the draining lymph node at 5 days post infection to assess activation states of CD8+ T cells at this timepoint.
2024-08-29 | GSE275970 | GEO
Project description: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.
| S-EPMC7978384 | biostudies-literature
Project description:Ross River virus (RRV) infection is a debilitating disease that has a significant impact on population health, economic productivity, and tourism in Australia. This study examined epidemiologic patterns of RRV disease in Queensland, Australia, during January 2001-December 2011 at a statistical local area level. Spatio-temporal analyses were used to identify the patterns of the disease distribution over time stratified by age, sex, and space. The results show that the mean annual incidence was 54 per 100,000 persons, with a male:female ratio of 1:1.1. Two space-time clusters were identified: the areas adjacent to Townsville, on the eastern coast of Queensland, and the southeast areas. Thus, although public health intervention should be considered across all areas in which RRV occurs, it should specifically focus on high-risk regions, particularly during summer and autumn to reduce the social and economic impacts of RRV infection.
| S-EPMC4080548 | biostudies-other
Project description:Ross River virus, a mosquitoborne alphavirus, causes epidemic polyarthritis in Australia and the Pacific region. We analyzed serum cytokine, chemokine, and growth factor levels in travelers returning to Germany from Australia. Serum samples showed elevated concentrations in the acute phase of the illness and, more pronounced, in the long-lasting convalescent phase.
| S-EPMC5367403 | biostudies-literature
Project description: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.
| S-EPMC7537878 | biostudies-literature
Project description: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.
| S-EPMC8396220 | biostudies-literature
Project description: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 the 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 Ross River virus (RRV) amino acid determinants for type I interferon (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 nonstructural protein 1 (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.
| S-EPMC7108833 | biostudies-literature
Project description: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.
| S-EPMC10868856 | biostudies-literature
Project description:BACKGROUND: Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. METHODS: We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. RESULTS: The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. CONCLUSIONS: This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.
| S-EPMC3004813 | biostudies-other