COMPUTATIONAL OPTIMIZATION STRATEGIES FOR MITIGATION OF ANTIBIOTIC RESISTANCE IN ECOSYSTEMS

Pravardhana Prabhu, Swathi, Nithya, Deepika, Sai Lekhana D S

Antibiotic resistance has emerged as a global environmental and public health concern, with soil and aquatic ecosystems acting as critical reservoirs for antibiotic-resistant bacteria and antibiotic resistance genes. Continuous inputs of antibiotics from agricultural practices, wastewater discharge, and industrial effluents promote the persistence and dissemination of resistance across environmental compartments. Traditional mitigation approaches relying on experimental studies and regulatory frameworks often face limitations due to ecosystem complexity, resource constraints, and spatiotemporal heterogeneity. In this context, computational modeling and optimization strategies provide powerful tools for analyzing antimicrobial resistance dynamics and designing effective mitigation interventions.
This review presents a comprehensive synthesis of computational optimization strategies applied to mitigate antibiotic resistance in soil and aquatic ecosystems. The study examines a wide range of modeling approaches, including statistical models, machine learning techniques, simulation frameworks, and network-based analyses, highlighting their roles in understanding resistance evolution and transmission pathways. Furthermore, optimization methods such as evolutionary algorithms, multi-objective optimization, and hybrid AI-driven approaches are critically reviewed with respect to their applications in wastewater treatment optimization, pollution control, and intervention planning. Comparative assessments are provided to evaluate the strengths, limitations, and practical applicability of existing methods.
The review also identifies key challenges, including data scarcity, limited real-world implementation, and inadequate integration of soil-water interaction dynamics. Finally, future research directions are outlined, emphasizing the potential of digital twins, data-driven decision-support systems, and interdisciplinary frameworks for sustainable environmental antimicrobial resistance mitigation.

Keywords –Antibiotic resistance, Computational optimization, Soil ecosystems, Aquatic environments, Environmental modeling, AMR mitigation, Decision-support system.