Editorial: Recent Advancements in the Research Models of Infectious Diseases

Sam Ebenezer Rajadas^1*Christy Rosaline Nirmal²Azger Dusthackeer³Shakila Harshavardhan⁴Krupakar Parthasarathy¹

• ¹Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, India
• ²Achira Labs Pvt Ltd, Bengaluru, India
• ³ICMR - National Institute for Research in Tuberculosis, Chennai, India
• ⁴Madurai Kamaraj University School of Biotechnology, Madurai, India

understanding of infectious processes and to highlight the interplay between experimental systems, computational modeling, and translational applications with clinical relevance.The articles in this collection highlight a diverse range of infectious disease models. This includes mathematical and computational frameworks that are increasingly being leveraged to Equally transformative are developments in computational and hybrid modeling. Artificial intelligence and machine-learning approaches, particularly, those constrained by biological or physical priors are accelerating our ability to simulate infection dynamics, predict drug responses, and identify emergent properties within complex datasets (Theodosiou and Read, 2023). Reaction diffusion and spatially explicit models are enriching our understanding of how infections propagate across tissues or populations, while phylodynamic frameworks integrating genomic and temporal data are reconstructing the invisible transmission networks that underlie epidemics (Waddel et al., 2025;Zhang and Wang, 2025). As these approaches evolve, the synergy between data-driven and mechanistic modeling will become highly significant to both fundamental and translational infection research.Despite these advancements, challenges persist in integrating the immunological and multi-omics clinical datasets, which are prone to variability, missingness, and overfitting. The opacity of some artificial intelligent models and their algorithms can hinder clinical trust and mechanistic interpretation, thus warrants extensive validation along with ethical and biosafety considerations.