Banking crises pose a constant threat to macroeconomic stability in emerging markets, where standard econometric Early Warning Systems (EWS) often fail to model nonlinear macro-financial relationships. This paper examines whether machine learning algorithms, rather than standard logistic regression, can improve forecasts of banking crisis risk in Nigeria. We compare the performance of Random Forests, Support Vector Machines (SVMs), and Extreme Gradient Boosting (XGBoost) to logistic regression on the African Financial Crises dataset (1954-2014) with annual data. Resampling is restricted to the training set to compensate for the rarity of crisis instances. In a strict out-of-time validation setting, the model’s accuracy is assessed by accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). Our findings show that tree-based ensemble models outperformed logistic regression on the test set: XGBoost generalises better (AUC = 1.0; F1 = 0.95 for non-crisis, 0.80 for crisis) instances, although Random Forest yields the highest cross-validated F1-score on the training set. Exchange rate volatility, inflation, systemic crisis variables, and defaults on external sovereign debt are identified as key predictors through feature importance analysis. Crisis years exhibit the strongest predictive signals, suggesting that annual data have limited early-warning capacity. Due to the small sample size and lack of crisis observations during the test period, results should be interpreted cautiously. All things considered, the findings provide strong early evidence that, although not yet ready as fully functional policy tools, machine learning models can support conventional tools for tracking banking crises in Nigeria.

Foot-and-mouth disease (FMD) is a highly transmissible viral infection of livestock that threatens food security and causes substantial economic losses in endemic regions. Despite its economic impact, the role of environmental viral load and wildlife reservoirs in sustaining FMD transmission remains poorly quantified. The aim of this study is to assess the extent to which community viral load sustains FMD persistence and to identify key transmission drivers in a coupled livestock–wildlife–environment system. A Susceptible–Exposed–Infected (SEI) model with a free-living virus compartment was analyzed via the basic reproduction number ($R_0$) and solved numerically using a Nonstandard Finite Difference Method. Sensitivity analysis identified wild host population size, transmission rates, host recruitment, environmental viral decay, and viral load thresholds as major determinants of $R_0$. Results indicate that higher transmission rates accelerate susceptible depletion and increase exposed and infected classes, with wildlife dominating environmental viral contributions. Community viral load is central to sustaining outbreaks and informs targeted control strategies.

Foodborne diseases remain a major public health concern because of their high incidence and the substantial number of deaths they cause worldwide. Consequently, there is a need to develop multiscale mathematical models that improve our understanding of the biological mechanisms driving disease transmission and identify key factors influencing disease persistence. Traditional mathematical models typically investigate within-host and between-host dynamics separately, overlooking the interactions between processes occurring at these two scales. This study develops and analyses a nested multiscale mathematical model for Escherichia coli O157:H7 that integrates within-host bacterial replication with between-host environmental transmission. The two scales are explicitly coupled through pathogen shedding, allowing the influence of within-host bacterial dynamics on environmental contamination and disease transmission to be quantified. A nonstandard finite difference (NSFD) scheme is constructed to preserve the essential dynamical properties of the continuous model, and the resulting discrete model is analysed mathematically. Numerical simulations demonstrate that once E. coli O157:H7 becomes established within infected cattle, continued bacterial replication and pathogen shedding sustain environmental contamination, thereby promoting the persistence of disease transmission within the cattle population. These findings highlight the important role of within-host bacterial dynamics in driving environmental transmission and provide a quantitative framework for evaluating intervention strategies targeting both within-host bacterial replication and between-host disease transmission.

Banking crises are a persistent threat to macroeconomic stability in emerging markets, where conventional econometric monitoring frameworks often fail to capture non-linear macro-financial relationships. This paper examines whether machine learning algorithms can improve the detection of banking crisis risk in Nigeria compared to standard logistic regression. We compare the performance of Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) against logistic regression using annual data from the African Financial Crises dataset (1954–2014). Resampling is only implemented on the training set to overcome the infrequency of crisis events. Performance on models is assessed based on accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) in a rigorous out-of-time validation setting. Our findings indicate that tree-based ensemble models outperform logistic regression on the test set: XGBoost achieves the best generalization performance (AUC = 1.0; F1 = 0.95 in non-crisis, 0.80 in crisis), whereas Random Forest has the highest cross-validated F1-score on the training set. The most important variables are exchange rate volatility, inflation, and indicators of systemic crisis. The most significant crisis indicators are, however, seen in crisis years, which means that the annual data do not provide much lead-time to detect the crisis. These results should be taken with caution because of the small sample size and the limited number of crisis observations during the test period. Altogether, machine learning models have potential as additional tools to monitor banking crises in Nigeria, though at the moment they are not fully operational as policy instruments.