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Field validation of phylodynamic analytical methods for inference on epidemiological processes in wildlife
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  • Carlo Pacioni,
  • Timothy Vaughan,
  • Tanja Strive,
  • Susan Campbell,
  • David Ramsey,
  • Alexei Drummond
Carlo Pacioni
Arthur Rylah Institute for Environmental Research

Corresponding Author:[email protected]

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Timothy Vaughan
ETH Zürich
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Tanja Strive
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Susan Campbell
Western Australia Department of Primary Industry and Regional Development
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David Ramsey
Arthur Rylah Institute
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Alexei Drummond
The University of Auckland, University of Auckland
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Amongst newly developed approaches to analyse molecular data, phylodynamic models are receiving much attention because of their potential to reveal changes to viral populations over short periods. This knowledge can be very important for understanding disease impacts. However, their accuracy needs to be fully understood, especially in relation to wildlife disease epidemiology, where sampling and prior knowledge may be limited. The release of the rabbit haemorrhagic disease virus (RHDV) as biological control in naïve rabbit populations in Australia in 1996 provides a unique dataset with which to validate phylodynamic models. By comparing the results obtained for RHDV1 with our current understanding of the RHDV epidemiology in Australia, we evaluated the performances of these recently developed models. In line with our expectations, coalescent analyses detected a sharp increase in the virus trajectory in the first few months after the virus release, followed by a more gradual increase. The phylodynamic analyses with a birth-death tree prior generated effective reproductive number estimates (the average number of secondary infections per each infectious case, Re) larger than one for most of the epochs considered. However, the possible range of the initial Re included estimates lower than one despite the known rapid spread of RHDV1 in Australia. Furthermore, the analyses that took into account the geographical structuring failed to converge. We argue that the difficulties that we encountered most likely stem from the fact that the samples available from 1996 to 2014 were too sparse with respect to geographic and within outbreak coverage to adequately infer some of the model parameters. In general, while these Bayesian analyses proved to be greatly informative in some regards, we caution that their interpretation may not be straight forward and recommend further research in evaluating the robustness of these models to assumption violations and sensitivity to sampling regimes.
15 Jul 2020Submitted to Transboundary and Emerging Diseases
17 Jul 2020Submission Checks Completed
17 Jul 2020Assigned to Editor
18 Jul 2020Reviewer(s) Assigned
04 Aug 2020Review(s) Completed, Editorial Evaluation Pending
24 Aug 2020Editorial Decision: Revise Major
21 Nov 20201st Revision Received
21 Nov 2020Assigned to Editor
21 Nov 2020Submission Checks Completed
24 Nov 2020Reviewer(s) Assigned
01 Dec 2020Review(s) Completed, Editorial Evaluation Pending
23 Dec 2020Editorial Decision: Revise Major
24 Feb 20212nd Revision Received
24 Feb 2021Submission Checks Completed
24 Feb 2021Assigned to Editor
26 Feb 2021Reviewer(s) Assigned
02 Mar 2021Review(s) Completed, Editorial Evaluation Pending
02 Mar 2021Editorial Decision: Accept
May 2022Published in Transboundary and Emerging Diseases volume 69 issue 3 on pages 1020-1029. 10.1111/tbed.14058