Risk Modeling Research Articles

Macro-financial linkage, endogenous risk premium and monetary policy: evidence from a semi-structural model estimated for Morocco

PurposeThis paper seeks to investigate the amplitude of macro-financial linkage for an emerging country like Morocco.Design/methodology/approachFor this purpose, it presents a semi-structural new-Keynesian model. In the blocks of the former, a risk premium is charged on the lending rate in addition to the policy rate. To identify the macro-financial linkage, the risk premium is considered endogenous. It is represented as a function of the borrower’s probability of default, which is, in turn, a function of the GDP gap. To identify this two-wave relationship, we estimate an ARDL model between 2009Q1 and 2020Q1. Therefore, we integrate the estimation results into the new-Keynesian semi-structural model.FindingsThe results reveal a significant impact of the financial condition on the path of the business cycle. In fact, demand shocks and nonperforming loan shocks (NPLs) are exacerbated by the presence of macro-financial linkage. Under this condition, the amplitude and persistence of the shocks are amplified and extended.Originality/valueThis paper extends the literature on the interconnection between the real and financial economies by considering the endogeneity of the credit risk premium and modeling its dynamics.

Integrated Health Monitoring System using Fingerprint and Disease Risk Prediction

Recent developments in machine learning and biometric systems have had a big influence on healthcare applications. In order to provide a holistic approach to health monitoring, this research introduces a unique method that combines disease risk prediction with fingerprint-based blood group recognition. Based on user-provided health data, the system uses machine learning algorithms to determine the risk of common illnesses including diabetes, cardiovascular disease, and chronic kidney disease (CKD) and convolutional neural networks (CNNs) to identify blood type from fingerprint photos. Through the integration of fingerprint biometric data with health risk modelling, the suggested approach seeks to increase the precision and usability of health forecasts. The system's effectiveness is demonstrated by the evaluation's excellent classification accuracy for blood groups and trustworthy estimates of illness risk.This work demonstrates the possibility of integrating biometric data with predictive health models and offers a promising step toward customized healthcare solutions

Flood fragility and vulnerability functions for residential buildings in the Province of Leyte, Philippines

AbstractThe Philippines experiences frequent flooding, but, despite expansive tools for risk reduction, there remain gaps in understanding generalised relationships between flood events and damage to residential structures for regions outside the nation's capital. This gap has limited the ability to model flood risk and damage without robust functions to link hazards and housing vulnerability. This research draws on 394 household surveys to empirically derive a suite of flood fragility and vulnerability functions for residential structures in the Province of Leyte for light material, elevated light material and masonry structures. The results showed that masonry construction was more resilient to floods compared to light material counterparts. Elevated light material structures also exhibited lower damages at low inundations but tend to fail abruptly at flood depths greater than 3 m. By empirically deriving flood damage functions, the findings contribute to a more localised approach to quantifying housing vulnerability and risk that can be used for catastrophe and risk modelling, with applications for government agencies, the insurance industry and disaster risk researchers. This research lays the foundation for future flood risk mapping with growing significance under climate change.

Abstract 4136280: Inflammation and Cancer Mortality among CVD Individuals: NHANES-Continuous study

Background: Both cancer and cardiovascular disease (CVD) are the leading causes of death worldwide. While previous research showed a relationship between CVD and cancer incidence, limited evidence is available regarding the relationship between inflammation and cancer mortality in CVD patients. Methods: A prospective cohort study using data from the continuous National Health and Nutrition Examination Survey (NHANES) (1999-2016) merged with Medicare and National Death Index (NDI) mortality data until December 31, 2018. We included CVD individuals with no history of cancer at baseline to investigate the relationship between inflammatory markers, including Neutrophil-to-Lymphocyte ratio (NLR), as the main exposure and cancer mortality as the main outcome. Results: We included 4,089 adult individuals representing 127,809,316 individuals (47% females, 72% non-Hispanic Whites, and 5% Hispanics). The risk factor analysis (Table) using the competing risk modeling showed that while inflammatory markers did not show significant associations with cancer mortality in the overall analysis, upon stratification by race/ethnicity, NH-Blacks showed higher cancer mortality with higher WBC count (aHR 5.61, 95% CI 1.66-18.91, p=0.007) and NLR (aHR 2.97, 95% CI 1.19-7.42, p=0.02), but lower cancer mortality with higher neutrophil (aHR 0.08, 95% CI 0.01-0.60, p=0.02) and lymphocyte (aHR 0.34, 95% CI 0.15-0.80, p=0.02) counts. Notably, our NH-Blacks showed lower neutrophils at baseline than others which could be explained by a phenomenon called “Benign Ethnic Neutropenia -BEN-” that still brings more complexity when interpreting NLR. Conclusions: Our study highlights the close relationship between cardiovascular health, inflammation, and cancer mortality. The findings also suggest that race/ethnicity may play a significant role in the relationship between inflammation and cancer mortality among individuals with CVD. Yet, further research is still needed to underpin the biological pathways influencing this relationship.

Abstract 4123540: Cardiovascular Health and Cancer Incidence and Mortality: NHANES-III and -Continuous study

Background: Cancer and cardiovascular disease (CVD) are the leading causes of death worldwide. Limited evidence is available regarding the relationship between CVD, cardiovascular risk factors (CVRF), and cancer mortality; and how CVRF further modify that relationship. Methods: The first part included 438 and 2100 CVD patients aged 65+ from NHANES-III and Continuous (1999-2016) datasets, respectively. Competing risk models with subdistribution hazards ratio (aHR) were used to identify risk factors for cancer incidence. The second part included 44,591 adult individuals from NHANES-Continuous merged with Medicare and National Death Index mortality data until the end of 2018. We included individuals with no history of cancer at baseline. We used competing risk modeling to test the relationship between CVD at baseline and cancer mortality. We also conducted a comprehensive risk factor analysis. Results: In the first part (Figure), females in NHANES-III had lower cancer risk (aHR 0.39, P=0.001) compared to males. Poor physical activity was associated with increased cancer risk in both datasets (aHR 2.59 in NHANES-III, aHR 1.59 in Continuous). In NHANES-Continuous, age (aHR 1.07, P<0.001) and current smoking (aHR 2.55, P=0.001) showed a significant association with developing cancer. No other factors investigated showed significant associations. In the second analysis (Table), the included sample size represented 1,738,423,317 individuals (52% females, 67% non-Hispanic Whites, and 9% Hispanics). Competing risk modeling showed a significantly higher risk of cancer mortality in individuals with CVD (aHR 1.37, 95% CI 1.07-1.76, P=0.01) after adjusting for age, sex, and race/ethnicity. Notably, cancer mortality increased with aging (aHR 1.08, 95%CI 1.05-1.11, P<0.0001), current smoking status (aHR 6.78, 95%CI 3.43-13.42, P<0.0001), and obesity (aHR 2.32, 95%CI 1.13-4.79, P=0.02). Finally, a significant interaction (P=0.034) was found where those with CVD and obesity showed higher cancer mortality than those with normal BMI (aHR, 95%CI 1.03-2.91, P=0.04). Conclusions: Our study highlights the close relationship between cardiovascular health and cancer, including among CVD patients. Our findings suggest that physical activity and obesity may play a significant role in cancer incidence and mortality among the general population and CVD patients. These findings emphasize the need for more proactive approach in the management of shared risk factors for CVD and cancer.

Brief communication: From modelling to reality – flood modelling gaps highlighted by a recent severe storm surge event along the German Baltic Sea coast

Abstract. In October 2023, the Baltic Sea coasts of Germany and Denmark experienced a severe storm surge, predominantly impacting the German state of Schleswig-Holstein and parts of southern Denmark. The surge led to extensive flooding in cities like Flensburg and Schleswig, causing the breaching of at least six (regional) dikes and causing over EUR 200 million in damages in Schleswig-Holstein. By chance, the peak water levels of this storm surge aligned well with those of recent hydrodynamic flood modelling studies of the region. This rare coincidence offers crucial insights for our understanding of flooding impacts, flood management, and modelling. By comparing those studies to the real-world example using extensive media reports, we aim to extract key insights and identify gaps to be tackled in order to improve flood risk modelling in the Baltic Sea region and beyond.

SURG-21. DEVELOPMENT AND VALIDATION OF A CLINICAL RISK SCORE FOR POSTOPERATIVE OUTCOME IN NEWLY DIAGNOSED GLIOBLASTOMA: A REPORT OF THE RANORESECT GROUP

Abstract BACKGROUND Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aim to make use of our RANO classification for extent of resection (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. METHODS The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. RESULTS Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy (TMZ/RT→TMZ). Residual tumor, MGMT promotor methylation status, age, and KPS were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p<0.01). The presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma (n = 258, p<0.01). Compared to previously postulated models, goodness-of-fit measurements were superior for our score. CONCLUSIONS The novel RANO risk score serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The score may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

Achieving successful gas plant construction: A risk modelling framework with quantitative risk-based approach to schedule management

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: a report of the RANO resect group.

Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aimed (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool. The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort. Our training cohort compromised 1003 patients with newly diagnosed isocitrate dehydrogenase-wildtype glioblastoma. Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative KPS were prognostic for survival and incorporated into regression tree analysis. By individually weighting the prognostic factors, an additive score (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for inferior survival. The prognostic value of our risk model was retained in treatment-based subgroups and confirmed in an external validation cohort of 258 patients with glioblastoma. Compared to previously postulated models, goodness-of-fit measurements were superior for our model. The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The model may serve to guide patient management and reduce imbalances between study arms in prospective trials.

Perioperative risk scores: prediction, pitfalls, and progress.

Perioperative risk scores aim to risk-stratify patients to guide their evaluation and management. Several scores are established in clinical practice, but often do not generalize well to new data and require ongoing updates to improve their reliability. Recent advances in machine learning have the potential to handle multidimensional data and associated interactions, however their clinical utility has yet to be consistently demonstrated. In this review, we introduce key model performance metrics, highlight pitfalls in model development, and examine current perioperative risk scores, their limitations, and future directions in risk modelling. Newer perioperative risk scores developed in larger cohorts appear to outperform older tools. Recent updates have further improved their performance. Machine learning techniques show promise in leveraging multidimensional data, but integrating these complex tools into clinical practice requires further validation, and a focus on implementation principles to ensure these tools are trusted and usable. All perioperative risk scores have some limitations, highlighting the need for robust model development and validation. Advancements in machine learning present promising opportunities to enhance this field, particularly through the integration of diverse data sources that may improve predictive performance. Future work should focus on improving model interpretability and incorporating continuous learning mechanisms to increase their clinical utility.

Modeling of Soil Erosion Risk Utilizing Morphometric Analysis and Geospatial Approach in Lake Nakuru, Kenya

Modeling of Soil Erosion Risk Utilizing Morphometric Analysis and Geospatial Approach in Lake Nakuru, Kenya

Patient specific numerical hemodynamics for postoperative risk assessment: series case study of EC-IC cerebral bypass

Abstract The study is devoted to the hemodynamics during cerebral vascular bypass surgery for the treatment of cerebral aneurysms in two patients. The location, morphological characteristics and treatment approaches of the patients were similar, but different outcomes were observed as a result of the performed microsurgical procedures . Computational approach was used to analyze the hemodynamic differences of aneurysms, treated via extra-intra cranial (EC-IC) cerebral bypass shunt. The paper presents a new criterion based on the energy parameters of healthy compartment of cerebral circulation. The applied approach demonstrates a new effective method of preoperative risk modelling for medical decision-making.

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

Artificial Intelligence in public health: A case study

Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.

Implementation of polygenic risk scores in secondary prevention of (breast) cancer

Abstract Utilizing polygenic risk scores to inform secondary prevention of breast cancer via population screening programs is one of the pioneering applications of genomics in public health applications. Over the last decade, polygenic risk scores for breast cancer have been developed and validated in various European populations (AUC ± 0.63, OR per Z unit ± 1.6, depending on the population). In some countries, the polygenic risk score has been integrated with decision modeling tools, such as CanRisk in the Netherlands, and low-level implementation into health and care systems has started. Implementation strategies vary, but generally consider initial implementation into high-risk populations, such as breast cancer families, in specialized clinical setting, such as academic medical centres, or specialized cancer clinics, referring relatives of patients to population screening based on comprehensive risk modelling, including polygenic risk scores. This route may be followed by other disease fields as well. The presentations outlines the current evidence and considerations on breast cancer genetic risk modelling, clinical pilot studies and the outline of step-wise implementation of such models to benefit secondary prevention programs. We start from the perspective of a single hospital, to the needed steps to upscale to national populations, both in diversity of these populations, as the throughput of genetic testing and counselling, until the harmonization and recalibration of various European healthcare models. We draw from examples of the Genotyping on all patients (GOALL), building cancer health platforms (CanHeal) and Genome of Europe (GoE) consortia.

CMR phenotypes and clinical outcomes in NYHA class I versus II-IV hypertrophic cardiomyopathy: identifying high-risk asymptomatic patients

Abstract Background Cardiac myosin inhibition therapy can deliver symptomatic benefit for NYHA class≥II patients with obstructive hypertrophic cardiomyopathy (HCM) and is being actively explored for non-obstructive phenotypes. However, expanding interest in clinical outcome reduction supports growing need to identify high risk cohorts across both symptomatic and asymptomatic cohorts. We aimed to examine phenotypic characteristics of NYHA-I versus NYHA≥II patients with HCM and their association with future outcomes. Methods 727 patients with HCM from the CIROC Registry who completed simultaneous CMR and patient health assessments, inclusive of NYHA evaluation and quality of life surveys, were studied. Baseline clinical and CMR characteristics were compared between NYHA-I and NYHA≥II cohorts, followed by risk modelling for the prediction of major adverse cardiovascular events (MACE), defined as: all-cause mortality, heart failure hospitalization, ventricular arrhythmia, new onset atrial fibrillation, or stroke. Cox models were constructed to identify independent predictors of MACE. Results Of the 727 patients, 546 (75%) reported NYHA-I and 181 (25%) NYHA≥II symptoms. Baseline characteristics are shown in Table 1. Compared to NYHA≥II, NYHA-I patients were younger, more likely male, and had a lower prevalence of diabetes and hypertension. No meaningful differences in CMR chamber volumes, function, left ventricular (LV) mass, or fibrosis burden were observed. Over a median follow up of 3.7 years, 109 patients (15%) experienced MACE: 12% in NYHA-I and 23% in NYHA≥II sub-groups (p<0.001). Baseline LV mass z-scores (determined from sex-matched, BSA-indexed SCMR healthy reference values) were independently associated with MACE by multivariable Cox modelling. An optimal LV mass z-score threshold of 3.9 for prediction of MACE was identified and combined with NYHA status to stratify patients into 4 categories. Kaplan-Meier curves showed patients with LV Mass z-scores >3.9 experienced worse event-free survival in both NYHA sub-groups (Figure 1a). Cox modelling in the overall cohort (Figure 1b) showed NYHA-I patients with LV Mass z-scores >3.9 experienced a 2.2-fold increased risk (p=0.007) of MACE, this exceeding the risk of NYHA ≥II patients below this threshold. Separate NYHA I and ≥II sub-group multivariable models showed LV Mass z-score >3.9 to remain a strong and independent predictor of MACE, providing respective hazards of 2.6 and 4.7 (p=0.001 and <0.001). Conclusions NYHA-I patients with HCM experience significantly lower rates of MACE versus NYHA≥II. However, LV mass z-scoring permits powerful sub-stratification of these sub-groups, identifying NYHA-I patients with higher LV mass who will experience worse outcomes versus NYHA≥II patients with lower LV mass. This simple yet powerful stratification offers unique potential to identify asymptomatic patients with HCM who may benefit from targeted therapeutics to reduce future MACE.

Health Impact Assessment in Personalized Prevention: three applications on pharmacogenomic testing

Abstract Background In recent years, literature on the efficacy of genetic and genomic tests has expanded, yet the impact of personalized prevention programs that include such tests remains understudied. Our study aims to test the use of the Health Impact Assessment (HIA) framework, traditionally applied in non-health policy evaluation, to assess the impacts of personalized prevention policies. Methods We selected a policy mandating dihydropyrimidine dehydrogenase (DPYD) genetic test use before prescribing fluoropyrimidine for colorectal cancer (CRC) patients. Three HIAs are being conducted in Italy, Portugal, and Finland to assess the applicability across different contexts. The assessment involves literature review, comparative risk modeling, and stakeholder interviews. Three national steering committees, comprising representatives from key interest groups and experts, will evaluate evidence to gauge policy impacts and offer recommendations. Results The potential impacts identified include CRC patients’ outcomes, economic, organizational, patient experience, clinician workload, and equity. Preliminary results show a consensus in literature and national guidelines on the ability of the DPYD test to reduce adverse events and enhance the quality of life of CRC patients. However, the assessment reveals context-specific effects regarding equity, influenced by the prevalence of ethnic groups and variations in access to health services across regions. Differences also emerged on organizational aspects like the availability of resources such as laboratories and personnel. A lack of literature was observed on patient acceptability and clinician attitudes towards the test. Conclusions The use of HIA in personalized prevention might assist in identifying policy impacts often overlooked through the involvement of key interest groups in the assessment process. Such effort can further emphasize the significance of evaluating these programs while considering the geographical context. Key messages • Our study innovatively applies the Health Impact Assessment framework to evaluate personalized prevention programs in pharmacogenomic, addressing a critical research gap. • Preliminary findings underscore the importance of considering geographical factors in public health policy evaluation, highlighting nuanced context-specific effects.

Strategic Navigation of Firm-Specific Risks: A Comprehensive Analysis of Corporate Finance Challenges and Solutions

Abstract: This paper delves into the multifaceted nature of firm-specific risks (FSRs) within corporate finance, highlighting operational, financial, and strategic risks as pivotal challenges confronting companies across industries. Through detailed examinations of scenarios ranging from regulatory hurdles in the pharmaceutical industry to cyber threats in retail and currency fluctuations impacting multinational corporations, this study elucidates the intricate web of internal and external factors contributing to FSR. Furthermore, it explores the critical role of corporate governance, emphasizing board structure, transparency in financial reporting, and robust internal controls in effectively managing these risks. By integrating advanced data analytics and risk modeling with comprehensive risk management frameworks, the paper showcases strategies for identifying, assessing, and mitigating FSRs, thereby enhancing corporate resilience and strategic agility. This analysis not only contributes to the academic discourse on risk management but also offers practical insights for practitioners aiming to fortify their organizations against the unpredictability of the business landscape.

Experimental infection of ringtail possums (Pseudocheirus peregrinus) with Mycobacterium ulcerans, the agent of Buruli ulcer

Buruli ulcer (BU) is a necrotizing disease of skin and soft tissue caused by the bacterium Mycobacterium ulcerans (MU). In Australia, where the disease is emerging in new geographic areas and human case numbers are increasing, native possum species act as reservoir hosts. To better understand the life history of MU in one of its natural hosts, we conducted intra-dermal challenge of six wild caught, MU-naïve common ringtail possums (Pseudocheirus peregrinus). All six animals developed BU disease consistent with that observed in naturally infected ringtail possums. Time to ulceration varied between 49 and 77 days (mean = 61.8 days). Molecular evidence of systemic infection was detected in five animals and was supported by consistent histopathological findings in four animals. Pathological findings included random, multifocal, granulomatous hepatitis in four possums, one of which also had a mild, multifocal, interstitial granulomatous pneumonia. Acid-fast bacilli were only evident in inflammatory foci beyond the primary inoculation site in one possum. The ringtail possum model of MU infection is an important tool for the investigation of bacterial transmission dynamics, pathogenesis and immune response in a natural host. Data from this model may improve disease risk modelling and help identify intervention points to stop zoonotic transmission and disease spread.