Diagnostic tests which can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been therefore paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. Here, we follow other recent proposals to further refine this concept, by using machine learning to assess the situational risk under which a diagnostic test is applied to augment its interpretation . We develop this to predict the occurrence of breakdowns of cattle herds due to bovine tuberculosis, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected by the skin test, improves by over 16 percentage points. While many risk factors are associated with increased risk of becoming infected, of note are several factors which suggest that, in some herds there is a higher risk of infection going undetected, including effects that are correlated to the veterinary practice conducting the test, and number of livestock moved off the herd.

翻译：能够检测临床前或亚临床感染的诊断测试是我们控制传染病武器库中最强大的工具之一。因此，大量努力被投入到改进人类、植物和动物疾病的诊断检测中，包括将诊断测试的使用目标对准更可能被感染的个体的策略。在此，我们遵循其他近期提案，进一步细化这一概念，通过使用机器学习评估诊断测试应用时的情境风险来增强其解释。我们利用异常详细的检测记录，开发该方法以预测因牛结核病导致的牛群爆发事件。研究表明，在不牺牲检测特异性的前提下，检测灵敏度可得到提升，使得皮试检测到的感染牛群比例提高了超过16个百分点。尽管许多风险因素与感染风险增加相关，但值得注意的是，有若干因素表明某些牛群中存在更高的未检测到感染的风险，包括与执行检测的兽医实践相关的效应以及从牛群中转出的牲畜数量。