Localized Outbreaks of Epidemic Polyarthritis among Military Personnel Caused by Different Sublineages of Ross River Virus, Northeastern Australia, 2016-2017.
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SUBMITTER: Liu W
PROVIDER: S-EPMC6759257 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.MethodsRRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.ResultsWe found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.ConclusionOur retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.
| S-EPMC2542357 | 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 (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: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: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: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:BackgroundRoss River virus (RRV) is Australia's most important arbovirus given its annual burden of disease and the relatively large number of Australians at risk for infection. This mosquito-borne arbovirus is also a zoonosis, making its epidemiology and infection ecology complex and cryptic. Our grasp of enzootic, epizootic, and zoonotic RRV transmission dynamics is imprecise largely due to a poor understanding of the role of wild mammalian hosts in the RRV system.MethodsThe current study applied a piecewise structural equation model (PSEM) toward an interspecific comparison of sylvatic Australian mammals to characterize the ecological and life history profile of species with a history of RRV infection relative to those species with no such history among all wild mammalian species surveyed for RRV infection. The effects of species traits were assessed through multiple causal pathways within the PSEM framework.ResultsSylvatic mammalian species with a history of RRV infection tended to express dietary specialization and smaller population density. These species were also characterized by a longer gestation length.ConclusionsThis study provides the first interspecific comparison of wild mammals for RRV infection and identifies some potential targets for future wildlife surveys into the infection ecology of this important arbovirus. An applied RRV macroecology may prove invaluable to the epidemiological modeling of RRV epidemics across diverse sylvatic landscapes, as well as to the development of human and animal health surveillance systems.
| S-EPMC6334474 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) is transmitted person-to-person mainly by close contact or droplets from respiratory tract. However, the actual time of viral shedding is still uncertain as well as the different routes of transmission. We aimed to characterize RNA shedding from nasopharyngeal and rectal samples in prolonged cases of mild COVID-19 in young male soldiers. Seventy patients from three different military locations were monitored after recommending to follow more strict isolation measures to prevent the spread of the virus. Then, nasopharyngeal, rectal, and blood samples were taken. SARS-CoV-2 RNA was detected by RT-PCR and specific antibodies by chemiluminescent immunoassays. The median nucleic acid conversion time (NACT) was 60 days (IQR: 7-85 days). Rectal swabs were taken in 60 % of patients. Seven patients (10 %) were positive in nasopharyngeal and rectal swabs, and five (7.14 %) remained positive in rectal swabs, but negative in nasopharyngeal samples. Four patients (5.71 %) that had been discharged, were positive again after 15 days. No significant difference was found in nucleic acid conversion time between age groups nor clinical classification. Maintaining distancing among different positive patients is essential as a possible re-exposure to the virus could cause a longer nucleic acid conversion time in SARS-COV-2 infections.
| S-EPMC8895548 | biostudies-literature
Project description:ObjectiveTo identify the impact of the first world war on the lifespan of participating military personnel (including in veterans who survived the war).DesignComparison of two cohorts of military personnel, followed to death.SettingMilitary personnel leaving New Zealand to participate in the first world war.ParticipantsFrom a dataset of the New Zealand Expeditionary Forces, we randomly selected participants who embarked on troopships in 1914 and a comparison non-combat cohort who departed on troopships in late 1918 (350 in each group).Main outcome measuresLifespan based on dates of birth and death from a range of sources (such as individual military files and an official database of birth and death records).ResultsA quarter of the 1914 cohort died during the war, with deaths from injury predominating (94%) over deaths from disease (6%). This cohort had a significantly shorter lifespan than the late 1918 "non-combat" cohort, with median ages of death being 65.9 versus 74.2, respectively (a difference of 8.3 years shown also in Kaplan-Meier survival curves, log rank P<0.001). The difference for the lifespan of veterans in the postwar period was more modest, with median ages of death being 72.6 versus 74.3, respectively (a difference of 1.7 years, log rank P=0.043). There was no evidence for differences between the cohorts in terms of occupational class, based on occupation at enlistment.ConclusionsMilitary personnel going to the first world war in 1914 from New Zealand lost around eight years of life (relative to a comparable military cohort). In the postwar period they continued to have an increased risk of premature death.
| S-EPMC4266957 | biostudies-literature
Project description:Ross River virus (RRV), an alphavirus of the Togaviridae family, is the most medically significant mosquito-borne virus of Australia. Past RRV phylogenetic and evolutionary analyses have been based on partial genome analyses only. Three geographically distinct RRV lineages, the Eastern, the Western, and the supposedly extinct North-Eastern lineage, were classified previously. We sought to expand on past phylogenies through robust genome-scale phylogeny to better understand RRV genetic diversity and evolutionary dynamics. We analyzed 106 RRV complete coding sequences, which included 13 genomes available on NCBI and 94 novel sequences derived for this study, sampled throughout Western Australia (1977-2014) and during the substantial Pacific Islands RRV epidemic (1979-1980). Our final data set comprised isolates sampled over 59 years (1959-2018) from a range of locations. Four distinct genotypes were defined, with the newly described genotype 4 (G4) found to be the contemporary lineage circulating in Western Australia. The prior geographical classification of RRV lineages was not supported by our findings, with evidence of geographical and temporal cocirculation of distinct genetic groups. Bayesian Markov chain Monte Carlo (MCMC) analysis revealed that RRV lineages diverged from a common ancestor approximately 94 years ago, with distinct lineages emerging roughly every 10 years over the past 50 years in periodic bursts of genetic diversity. Our study has enabled a more robust analysis of RRV evolutionary history and resolved greater genetic diversity that had been previously defined by partial E2 gene analysis.IMPORTANCE Ross River virus (RRV) causes the most common mosquito-borne infection in Australia and causes a significant burden of suffering to infected individuals as well as being a large burden to the Australian economy. The genetic diversity of RRV and its evolutionary history have so far only been studied using partial E2 gene analysis with a limited number of isolates. Robust whole-genome analysis has not yet been conducted. This study generated 94 novel near-whole-genome sequences to investigate the evolutionary history of RRV to better understand its genetic diversity through comprehensive whole-genome phylogeny. A better understanding of RRV genetic diversity will enable better diagnostics, surveillance, and potential future vaccine design.
| S-EPMC6955267 | biostudies-literature