Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.

In recent years, precise economic forecasting has primarily relied on econometric models, which often assume linearity and stationarity in time series data. However, the nonlinear and dynamic nature of economic data calls for more innovative approaches. Machine learning (ML) techniques offer significant advantages over traditional methods by capturing complex, nonlinear patterns without predefined specifications. This study investigates the effectiveness of Long Short-Term Memory (LSTM) networks for forecasting Gross Domestic Product (GDP) in a univariate setting using quarterly Romanian GDP data spanning from 1995 to 2023. The dataset encompasses significant economic events, including the 2008 financial crisis and the COVID-19 pandemic, highlighting its relevance for broader economic forecasting applications. While the univariate approach simplifies model development, it also limits the incorporation of additional economic indicators, potentially affecting generalizability. Furthermore, computational challenges, such as time-intensive hyperparameter tuning, emerged during model optimization. We implemented LSTM networks with input data based on four and six lags to predict GDP and compared their performance with Seasonal Autoregressive Integrated Moving Average (SARIMA), a classical econometric method. Our results reveal that LSTM networks consistently outperformed SARIMA in predictive accuracy, demonstrating their robustness in capturing economic trends. These findings underscore the potential of ML in enhancing economic forecasting methodologies.

This paper identifies the salient factors that characterize the inequality income distribution for Romania. Data analysis is rigorously carried out using sophisticated techniques borrowed from classical statistics (Theil). Decomposition of the inequalities measured by the Theil index is also performed. This study relies on an exhaustive (11.1 million records for 2014) data-set for total personal gross income of Romanian citizens.

Prague Economic Papers - impact factor journal of the University of Economics, Prague