Leptospirosis/Weil's Disease: Pathogenesis, Epidemiology, Bio-Screening, Next-Generation Interventions Powered by AI, ML, Modeling, 3d Fingerprinting, Cover Etiology, Treatment Horizons, Fungal, Mosquito and Airborne Infection

Leptospirosis or commonly referred to as Weil’s disease is an emerging neglected zoonotic bacterial infection that poses great challenges globally due to its complicated epidemiology, varied presentations, and increased prevalence linked with climate change, urbanization, and environmental pollution. Despite advancements in the diagnostic procedures and treatment options with antibiotics, delayed diagnosis and inadequate surveillance systems still hinder disease control efforts. This review provides current information about the disease with regard to the causative agents, pathogenesis, epidemiology, bio-screening techniques, and therapies of leptospirosis together with new developments in artificial intelligence (AI), machine learning (ML), modeling, geospatial analysis, three-dimensional (3D) fingerprinting, digital pathology, and precision medicine. Some of the emerging technologies such as explainable AI, deep learning (DL), Geographic Information System (GIS), Bayesian, and Long Short-Term Memory (LSTM) model, biomarker-based diagnostics, and AI -based drug discovery have demonstrated significant potential in the context of disease surveillance, prediction of outbreaks, prognosis, and personalization of clinical interventions. This review further addresses aspects like vaccine development, digital health, and the difficulties of transitioning these technologies into practice. The integration of intelligent computing techniques and molecular diagnostics in conjunction with One Health surveillance may offer a promising approach to achieve early detection, precise treatment, and preparedness in leptospirosis.