The use of scenario tree models in support of animal health surveillance: A scoping review

Scenario tree modelling is a well-known method used to evaluate the confidence of freedom from infection or to assess the sensitivity of a surveillance system in detecting an infection at a certain design prevalence. It facilitates the use of data from various sources and the inclusion of risk factors into calculations, while still obtaining quantitative estimates of surveillance sensitivity and probability of freedom. We conducted a scoping review to identify scenario tree models (STMs) applied to assess freedom from infection in veterinary medicine, characterize their use, parameterisation, reporting and potential limitations. We included published scientific articles and grey literature that were a) neither reviews nor expert opinions, b) aimed to assess freedom from infection, provided methods to assess it, or aimed to estimate the sensitivity of a surveillance program for early detection of an infection at a design prevalence, c) targeted infection in animals and d) used scenario tree modelling. The search covered documents published between January 2006 and August 2021. Several search methods were used to retrieve scientific articles and grey literature relevant to the subject. The search strategy included searching in scientific databases and/or grey literature repositories, contacting experts across the world that previously worked with STMs and retrieving citations from relevant reviews. Four hundred twenty-four distinct documents were retrieved with our search string. After screening, data was extracted from 99 documents representing 67 projects. Forty different animal diseases were modelled with STMs, the most represented being infections with tuberculous Mycobacterium sp., Avian Influenza A virus and Brucella sp. STMs were mostly used for diseases of cattle, swine and wild mammals. Results showed that STMs were used in a large variety of studies, are very versatile and were used in disparate frameworks. However, we also found that studies are not reported in a standardized way and often lack important information. This makes results hard to interpret, compare and reproduce. Additionally, we identified common assumptions and misconceptions, the most important ones regarding sensitivity and specificity, which could have an impact on the results of the studies using STMs. We recommend the elaboration of internationally agreed guidelines about how to report results from STMs in a uniform manner. Such guidelines should include information on the study setting, procedures and analyses, but also on how the results could be interpreted concerning freedom from infection. [Abstract copyright: Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.]