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Testing the intrinsic mechanisms driving the dynamics of Ross River Virus across Australia

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dc.contributor.authorKoolhof, I.S.
dc.contributor.authorBeeton, N.
dc.contributor.authorBettiol, S.
dc.contributor.authorCharleston, M.
dc.contributor.authorFirestone, S.M.
dc.contributor.authorGibney, K.
dc.contributor.authorNeville, P.
dc.contributor.authorJardine, A.
dc.contributor.authorMarkey, P.
dc.contributor.authorKurucz, N.
dc.contributor.authorWarchot, A.
dc.contributor.authorKrause, V.
dc.contributor.authorOnn, M.
dc.contributor.authorRowe, S.
dc.contributor.authorFranklin, L.
dc.contributor.authorFricker, S.
dc.contributor.authorWilliams, C.
dc.contributor.authorCarver, S.
dc.contributor.editorGorbalenya, A.E.
dc.date.issued2024
dc.descriptionData source: Supplementary information, https://doi.org/10.1371/journal.ppat.1011944
dc.description.abstractThe 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.
dc.identifier.citationPLoS Pathogens, 2024; 20(2, e1011944):1-24
dc.identifier.doi10.1371/journal.ppat.1011944
dc.identifier.issn1553-7366
dc.identifier.issn1553-7374
dc.identifier.urihttps://hdl.handle.net/11541.2/38027
dc.language.isoen
dc.publisherPublic Library of Science
dc.rightsCopyright 2024 Koolhof et al. This 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. (http://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1371/journal.ppat.1011944
dc.subjectAedes
dc.subjectCulex
dc.subjectmosquito vectors
dc.subjectRoss River virus
dc.titleTesting the intrinsic mechanisms driving the dynamics of Ross River Virus across Australia
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12283499510001831 13283499500001831 Open Access Published Version
ror.mmsid9916835329401831

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