🤖 AI Summary
In infectious disease control, diagnostic tests—such as the tuberculin skin test (TST) for bovine tuberculosis—often exhibit suboptimal sensitivity and high false-negative rates due to real-world operational heterogeneity. To address this, we propose a machine learning–based risk-context modeling framework that systematically identifies key environmental determinants of test performance—including veterinary practice patterns and livestock movement scale—and enables the first interpretable, quantitative characterization of missed-diagnosis contexts. By integrating high-fidelity livestock surveillance data with verified outbreak records, our model enhances TST sensitivity for infected herds by 16.2 percentage points without compromising specificity. This improvement significantly strengthens early outbreak detection capability. The work establishes a novel paradigm for context-aware, dynamic optimization of diagnostic assays in complex, heterogeneous field settings.
📝 Abstract
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.