Negative binomial mixed model neural network for modeling of pulmonary tuberculosis risk factors in West Java provinces

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts

Fire Activity in Mediterranean Forests (The Algerian Case)

International audienceAlgeria has high wildfire activity, albeit restricted to the northern coastal fringe. However, no study has investigated why fire is restricted to that area, and what combination of factors explains the occurrence of wildfires. Here, we describe the current fire regime of Northern Algeria from 2000 to 2019 and we correlate fire activity to a range of environmental and anthropic drivers. We found a strong north-south gradient in fire occurrence: it is maximal in the high-fueled (productive) oak forests of Northern Algeria with high annual rainfall amount, whereas it is fuel-limited in the South due to semi-arid conditions. We determined that fire is nearly absent where the bioclimate is subarid or arid, due to the steppic vegetation with summer Normalized Difference Vegetation Index (NDVI) values below 0.35. Therefore, fire occupies a narrow niche in space (the humid and subhumid areas with high productivity) and in time as most fires occur in summer after the high rainfalls from fall to spring that promote fuel growth. Humans also play a role as fire hotspots are concentrated in croplands and in built-up areas with high human density and infrastructures mixed with shrublands and forests. We discuss how the ongoing climate changes and the desertification progressing towards the North of Algeria may finally restrict forests to a narrow fringe providing less and less ecological services to the Algerian people

Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria)

The southern rim of the Mediterranean Basin (MB) has a long fire history but fire hazard is poorly investigated in comparison to the northern rim. We built a fire database using MODIS data (2001–2015) for an area typical of the northern coastal Algeria (Bejaia region) in order to decipher the role of environmental and anthropic controls on the fire frequency and the area burnt. We found a high role of bioclimate, which controls the fuel dryness, ignitability, and biomass. Maximal fire frequency and burnt areas were recorded in northern sub-humid areas with high amounts of forests and shrublands, and fire was limited in the southern subarid area. Humans set most fires, and preferentially burn forests, shrublands, pastures, groves, and agricultural lands. The maximal fire frequency and burnt area occurs in wildland urban interfaces characterized by forest-shrublands mosaics with disseminated habitats. Fire activity is low to medium in rural-urban interfaces characterized by agropastoral areas with high habitat density and large habitat patches. Small to large crown fires occur in forests and shrublands, while small surface fires predominate in agropastoral areas and groves. Large fires (> 100 ha) are rare (10%) but contribute for ca.50% to the total area burnt. These fire features are typical of many rural countries of the southern rim of the MB, and contrast with those on the northern rim. Based on this, we propose to improve the prevention, the detection, and the management of forest fires in the long term and to protect forests that host high biodiversity in Algeria