Pandemics, Volume 1, Issue 2 (September 2026) – 2 articles

The recurrent emergence of viral outbreaks, including SARS-CoV-2, influenza, Ebola, and respiratory syncytial virus (RSV), continues to expose critical limitations in conventional antiviral therapies, particularly in terms of targeting specificity, bioavailability, and resistance development. Nanotechnology has emerged as a transformative approach to overcome these challenges. This review provides a comprehensive and critical analysis of nanoparticle-based antiviral systems, including lipid-based, polymeric, inorganic, and hybrid nanocarriers, with a focus on their roles in enhancing drug delivery, targeting precision, and therapeutic efficacy. These platforms exert antiviral effects through multiple coordinated mechanisms, including inhibition of viral entry, suppression of replication, gene silencing, and modulation of host immune responses. The clinical success of lipid nanoparticle-based mRNA vaccines highlights the translational potential of nanotechnology, while emerging nanotherapeutic strategies demonstrate increasing versatility across diverse viral pathogens. However, key challenges—including safety, scalability, formulation stability, and regulatory constraints—continue to limit widespread clinical implementation. Overall, nanoparticle-mediated antiviral systems represent a multifunctional and adaptable platform capable of addressing the limitations of conventional therapies and enabling more effective, resilient, and precision-driven strategies for future pandemic preparedness. Full article

In dense urban settings, diagnostic systems reduce transmission only when sampling, result return, and isolation are operationally feasible during the period of peak infectiousness. We define a waiting-window transmission externality that arises when infectious individuals remain mobile between diagnostic sampling and actionable isolation. The term is formalized as E = N × P × TR × D, where N is daily testing volume, P is test positivity, TR is residual transmission during the waiting period, and D is sample-to-action turnaround time. The equation is used as a first-order operational risk-accounting framework rather than as a complete epidemic model. Using Monte Carlo uncertainty propagation, we compare centralized 48 h testing, surge conditions with coupled delay and crowding, near-patient rapid testing, and home sampling with isolation at sampling. Centralized 48 h workflows produce approximately 80 excess waiting-window infections per 1000 tests/day at p = 10% and approximately 401 at p = 50%, increasing to approximately 126 and 628 under surge coupling. Near-patient testing and home sampling reduce these values to approximately 5–26 across the same positivity range. We also distinguish two operationally different but epidemiologically related approaches: home sampling with immediate precautionary isolation reduces TR while laboratory turnaround may remain nonzero, whereas home-based molecular testing reduces D by returning results at the point of collection. Sensitivity checks for surge coupling and household transmission floors show that the qualitative ordering of workflows is preserved, although the magnitude of benefit depends on adherence and local operating conditions. These findings support redesigning diagnostic workflows around sample-to-action time, isolation feasibility, decentralized logistics, and equity rather than assay performance alone. Full article