Spatial and Temporal Patterns of Ross River Virus in Queensland, 2001-2020.
Ontology highlight
ABSTRACT:
SUBMITTER: Qian W
PROVIDER: S-EPMC8396220 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
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:BackgroundRoss River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Public health concern about RRV is increasing due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. Australia experienced its largest recorded outbreak of 9544 cases in 2015, with the majority reported from south east Queensland (SEQ). This study examined potential links between disease patterns and transmission pathways of RRV.MethodsThe spatial and temporal distribution of notified RRV cases, and associated epidemiological features in SEQ, were analysed for the period 2001-2016. This included fine-scale analysis of disease patterns across the suburbs of the capital city of Brisbane, and those of 8 adjacent Local Government Areas, and host spot analyses to identify locations with significantly high incidence.ResultsThe mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak in cases between February and May. The highest RRV incidence was in adults aged from 30 to 64 years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect.ConclusionsAlthough RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.
| S-EPMC7530966 | biostudies-literature
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:Australia experienced its largest recorded outbreak of Ross River virus (RRV) during the 2014-15 reporting year, comprising >10,000 reported cases. We investigated epidemiologic, entomologic, and virologic factors that potentially contributed to the scale of the outbreak in Queensland, the state with the highest number of notifications (6,371). Spatial analysis of human cases showed that notifications were geographically widespread. In Brisbane, human case notifications and virus detections in mosquitoes occurred across inland and coastal locations. Viral sequence data demonstrated 2 RRV lineages (northeastern genotypes I and II) were circulating, and a new strain containing 3 unique amino acid changes in the envelope 2 protein was identified. Longitudinal mosquito collections demonstrated unusually high relative abundance of Culex annulirostris and Aedes procax mosquitoes, attributable to extensive freshwater larval habitats caused by early and persistent rainfall during the reporting year. Increased prevalence of these mosquitoes probably contributed to the scale of this outbreak.
| S-EPMC6874252 | biostudies-literature
Project description:Health warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991-2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007-2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r 2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.
| S-EPMC9152751 | biostudies-literature
Project description:BackgroundBarmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis.Methods/principal findingsWe calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (?(2)?=?7587, df?=?7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state.Conclusions/significanceThis is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.
| S-EPMC3192738 | biostudies-literature
Project description:Purpose:Rhinoviruses (RVs) occur more frequently than other viruses and more often in people displaying symptoms than in those without. We sought to estimate the spectrum of RV diversity, RV species seasonality and to analyse RV involvement in respiratory virus co-detections. Methodology:A convenience collection of 1179 airway sample extracts from patients with suspected respiratory infections, collected during 2001, was subjected to comprehensive molecular testing. Results:RVs were the most common virus detected. We were able to genotype ~90?% of RV detections, identifying 70 distinct RVs, spanning all three species. RV-Bs were under-represented. We found RV species co-circulated at times, although one species usually dominated. Each species displayed a bimodal distribution. Conclusion:Notably, RVs and influenza A viruses (IFAV) seldom co-occurred, supporting their roles as primary pathogens of the airway among acutely ill infants. Whether RV circulation has a moderating or controlling effect on the IFAV season or is controlled by it cannot be determined from these data. Despite the frequent perception that RVs commonly co-occur with another virus, our findings indicated this was not always the case. Nearly 80?% of RV detections occurred alone. Understanding more about population-level interference between viruses may allow us to harness aspects of it to generate a non-specific antiviral intervention that mimics a putative protective effect. For routine respiratory virus screening to best serve the patient, RV testing should be a principal component of any acute respiratory illness testing algorithm throughout the year.
| S-EPMC7525053 | 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:BackgroundRoss River virus (RRV) is Australia's most common and widespread mosquito-transmitted arbovirus and is of significant public health concern. With increasing anthropogenic impacts on wildlife and mosquito populations, it is important that we understand how RRV circulates in its endemic hotspots to determine where public health efforts should be directed. Current surveillance methods are effective in locating the virus but do not provide data on the circulation of the virus and its strains within the environment. This study examined the ability to identify single nucleotide polymorphisms (SNPs) within the variable E2/E3 region by generating full-length haplotypes from a range of mosquito trap-derived samples.MethodsA novel tiled primer amplification workflow for amplifying RRV was developed with analysis using Oxford Nanopore Technology's MinION and a custom ARTIC/InterARTIC bioinformatic protocol. By creating a range of amplicons across the whole genome, fine-scale SNP analysis was enabled by specifically targeting the variable region that was amplified as a single fragment and established haplotypes that informed spatial-temporal variation of RRV in the study site in Victoria.ResultsA bioinformatic and laboratory pipeline was successfully designed and implemented on mosquito whole trap homogenates. Resulting data showed that genotyping could be conducted in real time and that whole trap consensus of the viruses (with major SNPs) could be determined in a timely manner. Minor variants were successfully detected from the variable E2/E3 region of RRV, which allowed haplotype determination within complex mosquito homogenate samples.ConclusionsThe novel bioinformatic and wet laboratory methods developed here will enable fast detection and characterisation of RRV isolates. The concepts presented in this body of work are transferable to other viruses that exist as quasispecies in samples. The ability to detect minor SNPs, and thus haplotype strains, is critically important for understanding the epidemiology of viruses their natural environment.
| S-EPMC10243270 | biostudies-literature