Comparing the Real-World Performance of Exponential-family Random Graph Models and Latent Order Logistic Models for Social Network Analysis

BCG vaccination reduces bovine tuberculosis transmission, improving prospects for elimination

Bacillus Calmette-Guérin (BCG) is a routinely used vaccine for protecting children against Mycobacterium tuberculosis that comprises attenuated Mycobacterium bovis. BCG can also be used to protect livestock against M. bovis; however, its effectiveness has not been quantified for this use. We performed a natural transmission experiment to directly estimate the rate of transmission to and from vaccinated and unvaccinated calves over a 1-year exposure period. The results show a higher indirect efficacy of BCG to reduce transmission from vaccinated animals that subsequently become infected [74%; 95% credible interval (CrI): 46 to 98%] compared with direct protection against infection (58%; 95% CrI: 34 to 73%) and an estimated total efficacy of 89% (95% CrI: 74 to 96%). A mechanistic transmission model of bovine tuberculosis (bTB) spread within the Ethiopian dairy sector was developed and showed how the prospects for elimination may be enabled by routine BCG vaccination of cattle.

On the role of benchmarking data sets and simulations in method comparison studies

Method comparisons are essential to provide recommendations and guidance for applied researchers, who often have to choose from a plethora of available approaches. While many comparisons exist in the literature, these are often not neutral but favor a novel method. Apart from the choice of design and a proper reporting of the findings, there are different approaches concerning the underlying data for such method comparison studies. Most manuscripts on statistical methodology rely on simulation studies and provide a single real-world data set as an example to motivate and illustrate the methodology investigated. In the context of supervised learning, in contrast, methods are often evaluated using so-called benchmarking data sets, that is, real-world data that serve as gold standard in the community. Simulation studies, on the other hand, are much less common in this context. The aim of this paper is to investigate differences and similarities between these approaches, to discuss their advantages and disadvantages, and ultimately to develop new approaches to the evaluation of methods picking the best of both worlds. To this aim, we borrow ideas from different contexts such as mixed methods research and Clinical Scenario Evaluation.