Summary Background Continuous, non-invasive viral surveillance is essential to monitor emerging pathogens and guide public health responses. Most environmental surveillance studies use targeted qPCR approaches, and comparisons between wastewater and indoor air surveillance remain limited. We aimed to compare the utility of emergency department indoor air and urban wastewater for tracking circulating viruses and resolving genomic information. Methods We conducted a matched-pair study comparing 19 weekly indoor air samples from the central ventilation exhaust shaft of an emergency department and 19 24-hour composite municipal wastewater samples in Leuven, Belgium, from December 2024 to April 2025. Both sample sets were processed using probe-based hybrid-capture viral metagenomics targeting over 3000 viral species, using influenza A as a clinically relevant test case. Findings Wastewater captured higher overall viral diversity (233 versus 106 species) and more complete genomes compared to indoor air, showing a relatively stable composition, mainly of enteric and animal-associated viruses. Indoor air demonstrated lower overall diversity but was enriched for respiratory viruses, including influenza A, coronaviruses, metapneumovirus, and respiratory syncytial virus, and more frequently achieved high genome coverage for these pathogens. Although both sample types permitted influenza A subtype characterization, influenza A genomes from wastewater were often less well covered. When coverage thresholds were met, indoor air supported targeted antiviral resistance-site screening for influenza A and RSV-A. Interpretation Wastewater and indoor air generate distinct but complementary viromes. Wastewater acts as a diverse, population-level monitor for One-Health applications, whereas indoor air serves as a targeted, human-centric sentinel system facilitating further genomic characterization for respiratory viruses. Funding Mustafa Karatas is supported by a Research Foundation Flanders (FWO) fundamental research scholarship (number: 11P7I24N). C.G., L.C., E.H., S.G. and E.A. acknowledge support from the DURABLE project. The DURABLE project has been co-funded by the European Union, under the EU4Health Programme (EU4H), project no. 101102733. Research in context Evidence before this study We searched PubMed for studies published between Jan 2000 and March 2024 using the terms “wastewater surveillance”, “metagenomics”, “indoor air”, and “viral metagenomics”. Previous studies have shown that wastewater surveillance can detect population-level viral circulation, and more recent work has explored indoor air sampling as a method for monitoring respiratory virus transmission. However, environmental metagenomic studies have largely examined these two sample types separately. Furthermore, most studies relied on untargeted sequencing approaches, which often yield fragmented genomes in these environments. To date, no study has systematically compared indoor air and wastewater using a comprehensive hybrid-capture viral metagenomics approach for virus surveillance. Added value of this study We conducted a matched comparison of indoor air from a hospital emergency department and municipal wastewater collected during the same weeks in Leuven, Belgium. We analyzed both sample types using an identical hybrid-capture viral metagenomics workflow targeting more than 3000 viral species. This design enabled a direct evaluation of how the two environmental surveillance lenses differ in viral diversity, genomic recovery, and epidemiological relevance. Wastewater captured broader viral diversity and a stable background dominated by enteric and animal-associated viruses, whereas indoor air captured more respiratory viruses and more frequently yielded high genome completeness for these pathogens. When genome coverage thresholds were met, indoor air data enabled influenza subtype identification and screening for antiviral resistance markers. Implications of all the available evidence Our findings support a layered environmental surveillance strategy in which different environmental samples provide complementary information. Wastewater offers a stable, population-level view of viral circulation and captures broad viral diversity, including human and animal-associated viruses. Indoor air sampling in human-dominated settings provides a more direct signal of respiratory virus circulation and can yield genomes suitable for subtype and mutation-level characterization. Combining these approaches could strengthen metagenomic surveillance frameworks by improving the interpretation of environmental viral signals, supporting early detection of emerging pathogens, and helping distinguish human virus circulation from environmental or animal-derived detections.

BACKGROUND:Pneumococcal surveillance remains challenging due to limited invasive disease surveillance and logistically demanding nasopharyngeal carriage studies, particularly in low- and middle-income countries (LMICs). We evaluated indoor air sampling as a non-invasive approach for monitoring pneumococcal circulation and serotype distribution. METHODS:Monthly indoor air samples were collected over 20 months (January 2022-September 2023) in a Belgian childcare center. Samples were analyzed by qPCR for pneumococcal detection and serotype identification. RESULTS:Pneumococci were detected in all samples. After excluding serotypes with known specificity issues, 15 serotypes/serogroups were identified, with a mean of seven per sample. Commonly detected serotypes largely matched those reported in Belgian carriage studies and invasive disease surveillance. CONCLUSIONS:Indoor air sampling captured signals of community pneumococcal circulation and serotype dynamics. With further validation, it could provide a scalable, affordable surveillance tool to support vaccine policy, monitor vaccine impact, and strengthen respiratory pathogen surveillance, particularly in LMICs.

ABSTRACT Whole-genome sequencing (WGS) accelerates drug-susceptibility testing (DST) in Mycobacterium tuberculosis (Mtb). Open-access software tools have become widely available, but the sources of real-world performance variability remain uncharacterized. We performed a systematic review and meta-analysis of the performance of open-access, independently validated WGS-based DST prediction tools. Bivariate random-effects meta-analysis was performed for six maintained tools (TBProfiler, Mykrobe, PhyResSE, MTBseq, GenTB, and SAM-TB). Bivariate meta-regression identified covariates associated with performance variation. Thirty-nine studies comprising 144,623 genomes were included. For the two most extensively validated tools, TBProfiler and Mykrobe, pooled rifampicin sensitivity was 95.4% (95% CI: 93.5–96.7) and 93.7% (92.0–95.1), with a specificity of 97.3% (95.7–98.3) and 97.0% (94.8–98.3), respectively. For isoniazid, the sensitivity was 92.0% (90.4–93.3) and 88.2% (85.5–90.4) and specificity 97.3% (96.0–98.2) and 97.5% (95.8–98.5). For ethambutol, the specificity was heterogeneous across tools (86.5%–95.4%); for pyrazinamide, the sensitivity varied widely (49.9%–80.6%). For fluoroquinolones, both sensitivity and specificity approached 90%, with heterogeneity. For newer agents, data scarcity precluded meaningful assessment. Meta-regression identified rifampicin resistance prevalence as the dominant predictor of decreased specificity across first-line drugs (β −1.5 to −3.6 on logit scale, false discovery rate [FDR] q < 0.05), while lineage composition effects were small and confounded. Current open-access WGS prediction tools achieve clinically useful accuracy as rule-out tests for rifampicin, isoniazid, and fluoroquinolone resistance. Predictive performance for second-line drugs is limited by data scarcity. Methodological limitations, including lineage bias, data leakage, and selective sampling, may undermine the tools’ generalizability across diverse global tuberculosis populations. IMPORTANCE Tuberculosis remains a leading infectious disease killer worldwide. Whole-genome sequencing (WGS) of Mycobacterium tuberculosis offers the potential to rapidly predict drug resistance as a one-stop test, but the accuracy of the software tools used to interpret sequencing results has been inconsistently reported. This meta-analysis leverages the heterogeneity across 39 studies and 144,623 genomes to identify factors that drive inconsistencies in reported performance, providing context-specific guidance for clinical adoption. We show that most tools perform adequately as rule-out tests for resistance to the most important first- and second-line drugs but fall short of specificity targets. Importantly, we identify that the local burden of drug resistance in a study population is the dominant factor driving inconsistencies between reported performance estimates. These findings provide guidance for laboratories considering adopting sequencing-based resistance testing and specify priorities for future tool development and validation.

Surveillance of urban wastewater in Leuven, Belgium, detected human rotavirus C (RVC) in April 2025. Our analyses revealed a G4P[2] strain closely related to RVC strains detected in feces of schoolchildren in China in March 2025.

Mycobacterium abscessus (Mab) is an emerging pulmonary pathogen with extensive drug resistance, resulting in prolonged multidrug therapy, severe side effects, and cure rates below 50%. The absence of a reliable immunocompetent mouse model and the poor translation of in vitro drug activity to mammalian in vivo efficacy hinder preclinical progress. To address this gap, we developed a novel Galleria mellonella infection model for Mab, combined with bioluminescence imaging (BLI), enabling longitudinal monitoring and quantification of mycobacterial burden. Two double-reporter strains were assessed: a firefly luciferase-expressing strain requiring exogenous luciferin, and an autonomously luminescent Lux strain. Both generated stable signals suitable for longitudinal in vitro and in vivo imaging, with improved in vivo detectability of the luciferase-expressing strain based on its characteristic red-shifted emission spectrum. BLI provided sensitive, early quantification of mycobacterial load and treatment responses, outperforming traditional larval endpoints. This model supports early detection of dose-dependent treatment effects and enables real-time monitoring, thereby highlighting the value of G. mellonella as a rapid, cost-effective complementary preclinical model for screening novel antimycobacterial compounds.

Abstract Mycobacterium avium pulmonary disease is an emerging global health challenge for which drug development remains limited by preclinical models that rely on laboratory strains and invasive endpoint analyses. Here, we compared recent clinical M. avium isolates with the reference strain ATCC 700898 across macrophage, Galleria mellonella , and murine infection models and evaluated longitudinal micro-computed tomography (µCT) as a non-invasive tool to monitor disease progression and treatment response. While extracellular growth rates were comparable, clinical isolates demonstrated enhanced host-associated fitness and induced higher bacterial burdens and more severe pulmonary pathology in mice than the reference strain. These strain-dependent differences were detected by quantitative µCT imaging. Using the hypervirulent isolate MYC_0069, we further show that clarithromycin monotherapy and standard-of-care triple therapy significantly reduced bacterial burden and lung pathology. Together, these findings establish a clinically relevant chronic M. avium model that combines clinical isolates with longitudinal imaging to enable preclinical anti-mycobacterial drug evaluation in vivo .

Extensive population testing played a crucial role in mitigating the COVID-19 pandemic. However, scaling up testing capacity requires a considerable workforce and infrastructure. Furthermore, sampling and testing delays can hinder timely interventions. We therefore sought to improve pre-test triage through an ensemble model based on self-reported information. We trained an XGBoost classifier to predict individual risk of COVID-19 infection for higher education students in Leuven (Belgium) from real-world social and health data related to 38,180 test results. The model could recommend isolation, testing, or release of individuals at high, moderate, or low risk of infection, respectively, based on two parametrizable probability thresholds. We then studied the epidemiological impact of the ensemble triage tool in silico, by simulating its implementation in our context to control an epidemic over time. The predictive model achieved a ROC AUC of 77.5% , but its performance varied across rolling retraining windows. The epidemiological simulations highlight the potential of the ensemble-enhanced triage system to control a surge of infections in the student population of Leuven. Given a rapid implementation at the onset of an infection surge, it could reduce the effective reproduction number below 1.0 while reducing the testing requirements by 47% . The predictions of the ensemble model were strongly influenced by the number of contacts which individuals reported, the reason for testing, and the onset of symptoms. Our study suggests that pre-test triage guided by ensemble models could play an important role in allocating testing resources efficiently. Given timely implementation and isolation compliance within the population, it could also help rapidly control a surge of infections. Future research could validate this approach for other pathogens, in other settings, and with deep learning models.

Fracture-related infection (FRI) is a serious complication in orthopaedic trauma that can lead to delayed union, nonunion, and poor clinical outcomes. A better understanding of the host immune response may provide valuable insights into the pathophysiology of FRI and may help identify genomic elements that contribute to the infection. This observational study compared immune responses between patients with FRI and non-infected controls using bone/tissue biopsies and sonication fluid, and it explored the possibility of detecting bacterial and biofilm genes using transcriptome profiling with hybridization technology (nCounter® RNA hybridization technology). A total of 15 infected patients demonstrated significant upregulation of the innate immune pathway, including Toll-like receptor (TLR) signalling and the MyD88 cascade, suggesting an active immune response contributing to both infection control and bone resorption. Among the differentially expressed genes, PTGS2 (COX-2) showed the highest level of upregulation in the infection group. Bone biopsies showed enhanced chemokine (e.g. CXCL1, CXCL2, CCL4/L1/L2) signalling, with higher levels compared to tissue biopsies. Transcriptomic analysis identified bacterial transcripts in cases where conventional culture was negative, revealing potential cases of low-bacterial-load infections causing culture-negativity. Transcriptome profiling exposed distinct immune activation patterns in FRI and enabled the detection of pathogens missed by conventional culture. These findings call for larger, prospective studies to further explore the clinical utility of transcriptomics in understanding and managing FRI.

Since 2012, Cameroon has introduced rapid molecular diagnostic tests for tuberculosis (TB). Despite this progress, WHO estimates indicate a TB diagnostic gap of 43% at the national level in 2019. This raises questions about the strategic allocation of available rapid molecular diagnostic tools to areas with lower TB notification. In a cross-sectional study, we combined Cameroon notification data on TB (2019), rifampicin-resistant (RR)-TB (2015-2019), as well as local TB risk factors, availability, intensity of use and accessibility of the Xpert MTB-RIF test with openly available geospatial datasets from OpenStreetMap and WorldPop. A mathematical model estimated TB and RR-TB incidence rates at the regional level. We compared these estimates with the number of reported TB cases to identify diagnostic gaps. Centre, East and Far North regions had the highest estimated TB incidence rates (400, 300 and 200 cases per 100,000 inhabitants, respectively), while South and Adamawa had the highest estimated RR-TB incidence rates (14.9 and 8.9 cases per 100,000 inhabitants, respectively). We report a national diagnostic gap of 53% and 50% for TB and RR-TB, respectively. These findings highlight the need to improve the allocation of diagnostic tools that follows the local disease burden in resource-limited settings to improve health equity.

Since late 2020, the emergence of variants of concern (VOCs) of SARS-CoV-2 has been of concern to public health, researchers and policymakers. Mutations in the SARS-CoV-2 genome-for which clear evidence is available indicating a significant impact on transmissibility, severity and/or immunity-illustrate the importance of genomic surveillance and monitoring the evolution and geographic spread of novel lineages. Lineage B.1.619 was first detected in Switzerland in January 2021, in international travellers returning from Cameroon. This lineage was subsequently also detected in Rwanda, Belgium, Cameroon, France, and many other countries and is characterised by spike protein amino acid mutations N440K and E484K in the receptor binding domain, which are associated with immune escape and higher infectiousness. In this study, we perform a phylogeographic analysis to track the geographic origin and subsequent dispersal of SARS-CoV-2 lineage B.1.619. We employ a recently developed travel history-aware phylogeographic model, enabling us to incorporate genomic sequences with associated travel information. We estimate that B.1.619 most likely originated in Cameroon, in November 2020. We estimate the influence of the number of air-traffic passengers on the dispersal of B.1.619 but find no significant effect, illustrative of the complex dispersal patterns of SARS-CoV-2 lineages. Finally, we examine the metadata associated with infected Belgian patients and report a wide range of symptoms and medical interventions.

Three hospitals implemented molecular point-of-care tests (POCTs) to screen patients for SARS-CoV-2 infection upon admission during the 2021/2022 influenza season, which in Belgium lasted from January to April 2022. The samples were simultaneously tested for influenza A/B. Influenza positivity at admission was examined in relation to patient characteristics and symptomatology. Influenza POCTs were performed on all patients requiring urgent hospitalization, regardless of the admission reason. A total of 9327 patients were included in the study, of which 411 (4.4%) tested positive for influenza A/B. Asymptomatic infection and mild illness accounted for respectively 11.2% (95% CI: 8.5%-14.6%), and 43.3% (95% CI: 38.6%-48.1%) of the cases. A total of 66% (95% CI: 60%-72%) of all patients in these symptom categories (asymptomatic and mild illness) showed a high viral load (cycle threshold [Ct] < 24). Only in 30 (7.3%, 95% CI: 5.2%-10.2%) of all cases and in two (4.4%, 95% CI: 1.2%-14.5%) of the asymptomatic cases, the symptomatology worsened during hospital stay. Coinfections with both influenza and SARS-CoV-2 occurred in 35 patients (8.5% of all influenza positive patients). There was no difference in symptomatology between patients with co-infections and those with an influenza mono-infection. Patients could not be reliably categorized into carriers with low versus high viral loads based on symptomatology, age, and vaccination status. More than half of the influenza-positive individuals were either asymptomatic or had mild symptoms upon admission, while often carrying high viral loads. Our results show that without screening of patients at hospital admission, a considerable number of patients with a high viral load may be incorrectly classified as being not infectious.

Background Early diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. Active case-finding (ACF), a cornerstone of TB control programs, aims to achieve this by targeting symptom screening and laboratory testing for individuals at high risk of infection. However, its efficiency is dependent on the ability to accurately identify such high-risk individuals and communities. The socioeconomic determinants of TB include difficulties in accessing health care and high within-household contact rates. These two determinants are common in the poorest neighborhoods of many sub-Saharan cities, where household crowding and lack of health-care access often coincide with malnutrition and HIV infection, further contributing to the TB burden. Objective In this study, we propose a new approach to enhance the efficacy of ACF with focused interventions that target subpopulations at high risk. In particular, we focus on densely inhabited urban areas, where the proximity of individuals represents a proxy for poorer neighborhoods with enhanced contact rates. Methods To this end, we used satellite imagery of the city of Kigali, Rwanda, and computer-vision algorithms to identify areas with a high density of small residential buildings. We subsequently screened 10,423 people living in these areas for TB exposure and symptoms and referred patients with a higher risk score for polymerase chain reaction testing. Results We found autocorrelation in questionnaire scores for adjacent areas up to 782 meters. We removed the effects of this autocorrelation by aggregating the results based on H3 hexagons with a long diagonal of 1062 meters. Out of 324 people with high questionnaire scores, 202 underwent polymerase chain reaction testing, and 9 people had positive test results. We observed a weak but statistically significant correlation (r=0.28; P=.04) between the mean questionnaire score and the mean urban density of each hexagonal area. Conclusions Nine previously undiagnosed individuals had positive test results through this screening program. This limited number may be due to low TB incidence in Kigali, Rwanda, during the study period. However, our results suggest that analyzing satellite imagery may allow the identification of urban areas where inhabitants are at higher risk of TB. These findings could be used to efficiently guide targeted ACF interventions.

During the COVID-19 pandemic, contact tracing was widely used to limit virus propagation and implement targeted disease control measures. It can however be difficult to assess whether infected cases are actually linked to the traced index case. In this study, we developed and applied an analytical pipeline using genomic data to assess the precision of contact tracing, defined as the proportion of transmission events suggested by contact tracing that are not contradicted by genomic analysis. We exemplify our approach by examining the transmission of SARS-CoV-2 among students at Belgium's largest university, in Leuven, during the Omicron BA.1 and BA.2 epidemic waves. We analysed 459 case-contact pairs identified through contact tracing. We then aimed to determine whether the pairs, where patients were infected with the same variant, clustered together within a time-scaled phylogeny. Our findings indicate that 34.6 % of transmission events suggested by contact tracing were not invalidated by our combined phylogenetic and single nucleotide polymorphism analysis. Only considering non invalidated transmission events, we estimated serial intervals with a smaller standard deviation than with all case-contact pairs. Our genomic-based pipeline allows us to assess the ability of our contact tracing program to correctly identify transmission chains. While contact tracing is crucial for early outbreak detection and control, monitoring its precision is vital for using this data in public health decisions.

BACKGROUND:Sampling the air in indoor congregate settings, where respiratory pathogens are ubiquitous, may constitute a valuable yet underutilised data source for community-wide surveillance of respiratory infections. However, there is a lack of research comparing air sampling and individual sampling of attendees. Therefore, it remains unclear how air sampling results should be interpreted for the purpose of surveillance. METHODS:In this prospective observational study, we compared the presence and concentration of several respiratory pathogens in the air with the number of attendees with infections and the pathogen load in their nasal mucus. Weekly for 22 consecutive weeks, we sampled the air in a single childcare setting in Belgium. Concurrently, we collected the paper tissues used to wipe the noses of 23 regular attendees: children aged zero to three and childcare workers. All samples were tested for 29 respiratory pathogens using PCR. FINDINGS:Air sampling sensitively detected most respiratory pathogens found in nasal mucus. Some pathogens (SARS-CoV-2, Pneumocystis jirovecii) were found repeatedly in the air, but rarely in nasal mucus, whilst the opposite was true for others (Human coronavirus NL63). All three pathogens with a clear outbreak pattern (Human coronavirus HKU-1, human parainfluenza virus 3 and 4) were found in the air one week before or concurrent with the first detection in paper tissue samples. The presence and concentration of pathogens in the air was best predicted by the pathogen load of the most infectious case. However, air pathogen concentrations also correlated with the number of attendees with infections. Detection and concentration in the air were associated with CO2 concentration, a marker of ventilation and occupancy. INTERPRETATION:Our results suggest that air sampling could provide sensitive, responsive epidemiological indicators for the surveillance of respiratory pathogens. Using air CO2 concentrations to normalise such signals emerges as a promising approach. FUNDING:KU Leuven; DURABLE project, under the EU4Health Programme of the European Commission; Thermo Fisher Scientific.

BACKGROUND Hospital-based communicable disease surveillance may be costly during large outbreaks and often misses mild or asymptomatic infections. It can be enhanced by environmental surveillance, which monitors circulating pathogens, even from asymptomatic carriers. AIM We investigated if tracking viruses in indoor air could be used for their surveillance in a community setting. We also tested the value of untargeted metagenomics to identify viruses in air samples. METHODS Weekly indoor air samples were collected with active air samplers from January until December 2022 from a daycare centre in Leuven, Belgium. Samples were analysed using respiratory and enteric quantitative (q)PCR panels, as well as with untargeted metagenomics, enabling both targeted and agnostic viral detections. RESULTS Human-associated viruses were detected in 40 of 42 samples across the study period, with MW polyomavirus being most prevalent (33 samples). Respiratory agents such as rhinoviruses and RSV-B and enteric viruses including rotavirus A, astrovirus, and adenovirus appeared at epidemiologically expected times. Skin-associated viruses were also observed, notably Merkel cell polyomavirus and STL polyomavirus. Metagenomics enabled reconstructing multiple complete genomes, distinguishing viral subtypes and detecting copresence of closely related variants. Additionally, several animal, insect, fungal, and plant viruses were found, reflecting both indoor and outdoor environmental exposure. CONCLUSION Indoor air monitoring, combined with untargeted metagenomics, demonstrates a potential to support virus surveillance. This approach can allow monitoring circulation of viruses in community settings, including those causing asymptomatic or mild infections. By enabling to reconstruct complete viral genomes, it allows detailed variant tracking, facilitating adapted public health responses.

Despite its historical decline, TB remains a significant cause of infectious disease-related global deaths. The lack of reliable diagnostic tests for vulnerable groups, such as children and immunocompromised patients, remains a challenge for TB control. For decades, it has been recognised that exhaled breath has great potential as a non-invasive and universally accessible clinical alternative to sputum and invasive sampling methods. Although translation into clinical practice has not yet occurred, there has been significant progress with promising results in various applications, including diagnosis, estimation of infectiousness, and monitoring of treatment response. More recently, the COVID-19 pandemic reignited global interest in this field and technological advances have further accelerated its development. In the coming decade, breath sampling will enhance our understanding of respiratory infectious diseases and host-immune responses, which may lead to clinical applications. Here we discuss the diagnostic landscape of TB and the current state of the art of breath sampling.

ABSTRACT The performance of a novel selective agar was evaluated against the performance of conventional mycobacterial cultures, i.e., a combination of the mycobacterial growth indicator tube (MGIT) with Löwenstein-Jensen (LJ), for the detection of nontuberculous mycobacteria (NTM) in sputum samples from people with cystic fibrosis (pwCF). Two hundred eighty-three sputum samples (231 fresh sputum and 52 spiked sputum) from 143 pwCF were collected. They were inoculated without prior decontamination on NTM Elite agar (30°C ± 2°C for 28 days) and inoculated on both MGIT and LJ (35°C–37°C for 6–8 weeks) after N-acetyl-L-cysteine-2% sodium hydroxide decontamination. NTM were identified by Matrix-Assisted Laser Desorption Ionization/Time of Flight Mass Spectrometry and/or PCR, and whole-genome sequencing. A total of 67 NTM were recovered overall by the combination of all culture media. NTM Elite agar allowed the recovery of 65 NTM (97%), compared to 22 for the conventional MGIT and LJ media combination (32.8%), including 22 NTM for MGIT (32.8%) and 3 NTM with the LJ medium (4.5%). For Mycobacterium abscessus complex, the sensitivity of NTM Elite agar was 95% compared with a sensitivity of 30% for the conventional MGIT and LJ media combination. Overall, 17.3% of cultures on NTM Elite agar were contaminated with other micro-organisms vs 46.3% on MGIT and 77% on LJ. This study shows that the novel selective agar (NTM Elite agar) significantly outperforms the conventional MGIT and LJ media combination in terms of sensitivity, selectivity, and ease of culture, without the requirement of an L3 laboratory. IMPORTANCE Nontuberculous mycobacteria (NTM) are significant pulmonary pathogens in patients with pre-existing structural lung conditions such as cystic fibrosis, bronchiectasis, or chronic obstructive pulmonary disease. Mycobacterium avium complex and Mycobacterium abscessus complex (MABSC) are the most frequently isolated organisms. Compared to the recommended culture method for NTM, which combines solid and liquid culture media, NTM Elite agar enables a faster/easier diagnosis and speeds up identification and susceptibility testing as the final reading is at 28 days instead of 6–8 weeks for the conventional mycobacterial cultures. In addition, for the NTM Elite agar, no decontamination stage before inoculation is necessary, unlike the conventional mycobacterial cultures. NTM Elite agar is derived from a formulation of medium adapted to rapidly growing mycobacteria (RGM). The medium enables the growth of RGM while suppressing other flora. It is supported with published clinical data showing the benefits of this medium.