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Epidemiological models for predicting ross river virus in australia: A systematic review

Qian, Wei; Viennet, Elvina; Glass, Katie; Harley, David

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...[Show more]

dc.contributor.authorQian, Wei
dc.contributor.authorViennet, Elvina
dc.contributor.authorGlass, Katie
dc.contributor.authorHarley, David
dc.date.accessioned2022-03-02T04:47:35Z
dc.date.available2022-03-02T04:47:35Z
dc.identifier.issn1935-2727
dc.identifier.urihttp://hdl.handle.net/1885/261634
dc.description.abstractRoss 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 timeseries 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.
dc.description.sponsorshipThis work is supported by the University of Queensland Research Training Scholarship and Frank Clair Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherPublic Library of Science
dc.rights© 2020 The Authors
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcePLoS Neglected Tropical Diseases
dc.titleEpidemiological models for predicting ross river virus in australia: A systematic review
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume14
dc.date.issued2020
local.identifier.absfor111706 - Epidemiology
local.identifier.ariespublicationa383154xPUB14825
local.publisher.urlhttps://journals.plos.org/
local.type.statusPublished Version
local.contributor.affiliationQian, Wei, Mater Research Institute-University of Queensland (MRI-UQ)
local.contributor.affiliationViennet, Elvina, Australian Red Cross Lifeblood
local.contributor.affiliationGlass, Katie, College of Health and Medicine, ANU
local.contributor.affiliationHarley, David, University of Queensland
local.bibliographicCitation.issue9
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage17
local.identifier.doi10.1371/journal.pntd.0008621
local.identifier.absseo920203 - Diagnostic Methods
dc.date.updated2020-12-20T07:20:56Z
dcterms.accessRightsOpen Access
dc.provenanceThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.licenseCreative Commons Attribution licence
CollectionsANU Research Publications

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