Effectively developing robust predictive models for forecasting infectious disease dynamics over space and time relies on successful surveillance strategies to accurately assess host infection status. We are constantly refining these models to improve our understanding of transmission and persistence dynamics in host populations but are continuously challenged with difficulties in accurately diagnosing host infection status. These challenges are especially persistent for pathogens of the Mycobacterium tuberculosis Complex (MTBC), which cause tuberculosis (TB) disease in a wide array of mammalian hosts. These challenges are further exacerbated when working with MTBC pathogens in free-ranging wildlife hosts. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently described and novel MTBC pathogen is Mycobacterium mungi, which infects the highly social, group-living banded mongoose (Mungos mungo). M. mungi poses a large threat to human and animal health as banded mongoose hosts thrive in urbanized areas and live in close proximity to humans, but despite this threat, accurately diagnosing M. mungi infection status remains a primary challenge. Here, I develop a host response-based assay for differentiating banded mongoose with clinical M. mungi disease from individuals that are putatively healthy using transcriptional biomarkers in whole blood. To our knowledge, this is the first evaluation of host response-based transcriptional signatures to detect TB infection in unstimulated whole blood collected from a free-ranging wildlife species. I found that the expression of two genes, GBP5 and DUSP3, are significantly upregulated (GBP5, p < .05; DUSP3, p < .005) in banded mongoose with clinical M. mungi disease when compared to that of putatively healthy individuals. These results are consistent with studies of active M. tuberculosis disease in humans and support the use of host response-based assays using blood transcriptional biomarkers for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving surveillance strategies for diagnosing M. mungi infection status in banded mongoose and will be essential in refining predictive models for forecasting transmission and persistence dynamics over space and time. / Creating models to predict how diseases circulate and persist within a population is dependent on our ability to accurately diagnose if a host is infected. Diagnosing infection is difficult for some diseases, including tuberculosis (TB) pathogens, which infect humans and many other mammalian species. While vast improvements have been made in diagnosing TB infection in humans, diagnosing TB in free-ranging wildlife species is a constant challenge. These challenges are further exacerbated across the different pathogen species of TB. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently discovered TB pathogen is Mycobacterium mungi, which infects free-ranging banded mongoose (Mungos mungo). This pathogen poses a large threat to human and animals health since banded mongoose thrive in urbanized areas and live in close proximity to humans. Despite this threat, accurately diagnosing M. mungi infection in banded mongoose remains a challenge. Here, I develop a diagnostic molecular tool that uses banded mongoose blood to measure the expression of specific genes and differentiate diseased individuals from seemingly healthy individuals. To our knowledge, this is the first study that has used this specific approach for diagnosing TB in a free-ranging wildlife species. I found that the expression of two genes are significantly increased in banded mongoose with clinical M. mungi disease when compared to that of seemingly healthy individuals. These results are consistent with studies human TB disease in humans and support the use of this approach for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving diagnostics for M. mungi infection in banded mongoose and will be essential in refining models for predicting how this disease circulates and persists over space and time.