Towards the elimination of dog-mediated rabies: development and application of an evidence-based management tool
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RESEARCH ARTICLE
Open Access
Towards the elimination of dog-mediated rabies: development and application of an evidence-based management tool Kristyna Rysava1,2* , Tamara Mancero3, Eduardo Caldas4, Mary Freire de Carvalho3, André P. B. Castro5, Veronica Gutiérrez6, Daniel T. Haydon2, Paul C. D. Johnson2, Rebecca Mancy2, Lúcia R. Montebello5, Silene M. Rocha5, Jesús F. Gonzalez Roldan6, Marco Antonio Natal Vigilato3, Victor Del Rio Vilas3,7 and Katie Hampson2
Abstract Background: International organizations advocate for the elimination of dog-mediated rabies, but there is only limited guidance on interpreting surveillance data for managing elimination programmes. With the regional programme in Latin America approaching elimination of dog-mediated rabies, we aimed to develop a tool to evaluate the programme’s performance and generate locally-tailored rabies control programme management guidance to overcome remaining obstacles. Methods: We developed and validated a robust algorithm to classify progress towards rabies elimination within sub-national administrative units, which we applied to surveillance data from Brazil and Mexico. The method combines criteria that are easy to understand, including logistic regression analysis of case detection time series, assessment of rabies virus variants, and of incursion risk. Subjecting the algorithm to robustness testing, we further employed simulated data sub-sampled at differing levels of case detection to assess the algorithm’s performance and sensitivity to surveillance quality. Results: Our tool demonstrated clear epidemiological transitions in Mexico and Brazil: most states progressed rapidly towards elimination, but a few regressed due to incursions and control lapses. In 2015, dog-mediated rabies continued to circulate in the poorest states, with foci remaining in only 1 of 32 states in Mexico, and 2 of 27 in Brazil, posing incursion risks to the wider region. The classification tool was robust in determining epidemiological status irrespective of most levels of surveillance quality. In endemic settings, surveillance would need to detect less than 2.5% of all circulating cases to result in misclassification, whereas in settings where incursions become the main source of cases the threshold detection level for correct classification should not be less than 5%. (Continued on next page)
* Correspondence: [email protected] 1 University of Warwick, School of Life Sciences, Gibbet Hill Road, Coventry, UK 2 University of Glasgow, Institute of Biodiversity, Animal Health and Comparative Medicine, Graham Kerr building, MVLS, Glasgow G12 8QQ, UK Full list of author information is available at the end of the article
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate i
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