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Assessing the dynamics of *Mycobacterium bovis* infection in three French badger populationsuse asterix (*) to get italics
Clement CALENGE, Ariane PAYNE, Edouard REVEILLAUD, Celine RICHOMME, Sebastien GIRARD, Stephanie DESVAUXPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2024
<p>The Sylvatub system is a national surveillance program established in 2011 in France to monitor infections caused by <em>Mycobacterium bovis</em>, the main etiologic agent of bovine tuberculosis, in wild species. This participatory program, involving both national and local stakeholders, allowed us to monitor the progression of the infection in three badger populations in clusters covering between 3222 km2 and 7698 km2 from 2013 to 2019. In each cluster, badgers were trapped and tested for <em>M. bovis</em>. Our first aim was to describe the dynamics of the infection in these clusters. We developed a Bayesian model of prevalence accounting for the spatial structure of the cases, the imperfect and variable sensitivity of the diagnostic tests, and the correlation of the infection status of badgers in the same commune caused by local factors (e.g., social structure and proximity to infected farms). This model revealed that the prevalence increased with time in one cluster (Dordogne/Charentes), decreased in the second cluster (Burgundy), and remained stable in the third cluster (Bearn). In all the clusters, the infection was strongly spatially structured, whereas the mean correlation between the infection status of the animals trapped in the same commune was negligible. Our second aim was to develop indicators for monitoring <em>M. bovis</em> infection by stakeholders of the program. We used the model to estimate, in each cluster, (i) the mean prevalence level at mid-period, and (ii) the proportion of the badger population that became infected in one year. We then derived two indicators of these two key quantities from a much simpler regression model, and we showed how these two indicators could be easily used to monitor the infection in the three clusters. We showed with simulations that these two simpler indicators were good approximations of these key quantities.</p>
https://dx.doi.org/10.5281/zenodo.8010664You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
https://dx.doi.org/10.5281/zenodo.10478002You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
https://dx.doi.org/10.5281/zenodo.10478002You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
participatory science; bovine tuberculosis; prevalence; indicators; spatial modelling; intraclass correlation
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Animal diseases, Ecohealth, Ecology of hosts, infectious agents, or vectors, Epidemiology, Geography of infectious diseases, Pathogenic/Symbiotic Bacteria, Zoonoses
Andrew Byrne (AndrewW.Byrne@agriculture.gov.ie), Pelayo Acevedo (pelayo.acevedo@uclm.es), Richard J Delahay (Dez.Delahay@apha.gov.uk), Dennis Heisey (dheisey@usgs.gov), Christoph Staubach (staubach@wus.bfav.de), Aaron Ellison (aellison@fas.harvard.edu), Vladimir Grosbois [vladimir.grosbois@cirad.fr] suggested: Not available at the moment but happy to contribute another time. No need for them to be recommenders of PCIInfections. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2023-06-05 10:50:49
Jean-Francois Guegan