Dataset Information

Genome Sequences of Three Ross River Virus Isolates Obtained from the Australian Defence Force.

ABSTRACT: The complete genome sequences of three Ross River virus (RRV) isolates from infected Australian Defence Force (ADF) personnel and from mosquitoes collected in ADF training areas were determined. Phylogenetic analysis in comparison with all available complete RRV nucleotide sequences from GenBank split these three RRV isolates into two distinct sublineages.

SUBMITTER: Liu W

PROVIDER: S-EPMC6449554 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Genome Sequences of Three Ross River Virus Isolates Obtained from the Australian Defence Force.

Liu Wenjun W Kizu Joanne J Le Grand Luke L Mitchell Ian I Gauci Penelope J PJ Gubala Aneta J AJ

Microbiology resource announcements 20190404 14

The complete genome sequences of three Ross River virus (RRV) isolates from infected Australian Defence Force (ADF) personnel and from mosquitoes collected in ADF training areas were determined. Phylogenetic analysis in comparison with all available complete RRV nucleotide sequences from GenBank split these three RRV isolates into two distinct sublineages. ...[more]

PMID: 30948463

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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.

Dataset Information

Genome Sequences of Three Ross River Virus Isolates Obtained from the Australian Defence Force.

Publications

Genome Sequences of Three Ross River Virus Isolates Obtained from the Australian Defence Force.

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