From concept to capability: CBRNE-grade, equity-first benchmarks for preventing HKU5-CoV-2 spillover

To the Editor, On the basis of a timely and balanced analysis of the potential of HKU5-CoV-2 as an emerging zoonotic threat, Ludovici et al.[1] make a compelling case for applying a chemical, biological, radiological, nuclear, and explosive (CBRNE) perspective to pandemic prevention. Their article underscores the importance of a systems-based preparedness paradigm designed not only to recognize zoonotic risks, but also to detect, stress-test, and govern for them before sustained human-to-human transmission occurs. Building on this conceptual foundation, we propose a four-layer preparedness architecture—Sensing, Inference, Response, and Legitimacy—to translate the CBRNE-oriented approach into measurable and operational spillover-prevention capabilities. The coronavirus disease 2019 pandemic exposed several structural weaknesses in global spillover preparedness. First, many surveillance systems still lack actionable operational thresholds, meaning that detection signals are not linked to predefined escalation criteria. Second, genomic surveillance capacity remains unevenly distributed, resulting in delays in identifying emerging variants and zoonotic threats. Third, technology transfer initiatives are often episodic rather than institutionally sustained, thereby limiting long-term workforce development and infrastructure resilience. Finally, experimental evidence that lineage 2 HKU5-CoV can use human angiotensin-converting enzyme 2 (ACE2) as a cell entry receptor heightens the plausibility of future merbecovirus spillover and reinforces the urgency of strengthening early-warning systems.[2–6] A CBRNE-aligned preparedness strategy can address these challenges by translating preparedness goals into measurable operational standards. For example, internationally benchmarked indicators could include: A “time-to-signal” metric requiring detection of a novel zoonotic coronavirus within ≤7 days from sentinel sampling to public data deposition; Predefined surge-sequencing triggers, such as ≥3 epidemiologically linked atypical pneumonia clusters at an animal–human interface within 14 days; and Minimum genomic surveillance coverage targets of ≥5% sequencing of severe respiratory cases in high-risk regions during peacetime, scalable to ≥20% during alert phases. Such thresholds would transform preparedness from an aspirational concept into a measurable capability and enable transparent cross-regional accountability. The ecological and evolutionary dynamics of coronaviruses in wildlife reservoirs create a continuous background risk of zoonotic spillover.[4] However, whether an initial spillover event develops into a broader public health crisis depends largely on the capacity of surveillance and response systems to detect and contain early signals. In many settings, uneven surveillance coverage and limited diagnostic access delay detection, allowing localized zoonotic events to escalate into wider outbreaks. Importantly, these weaknesses are not solely technological. Empirical assessments indicate that surveillance gaps often arise from governance fragmentation, informatics limitations, inconsistent quality assurance, and insufficient workforce development rather than from the mere absence of sequencing platforms.[5,7] Consequently, the core preparedness challenge lies in building integrated surveillance ecosystems that can detect zoonotic threats early while ensuring equitable access to diagnostics, data infrastructure, and trained personnel. The preparedness relevance of HKU5-CoV-2 is strengthened by experimental evidence showing that lineage 2 HKU5-CoV can use human ACE2 as a functional entry receptor.[6] Although this finding does not imply inevitable emergence, it does justify platform-ready countermeasures that can be deployed rapidly once epidemiological signals are validated. In parallel, advances in coronavirus vaccinology show that breadth-oriented immunogen design can elicit broadly protective immune responses, thereby supporting the principle that response platforms should be developed before crisis conditions arise.[8,9] Even though many current vaccine efforts remain concentrated on sarbecoviruses, the broader preparedness lesson is transferable: breadth-first platform readiness, standardized correlates, and scalable manufacturing can compress response timelines when novel threats emerge. HKU5-CoV-2-specific risk factors, including receptor usage, bat reservoir ecology, and merbecovirus phylogenetics, should therefore guide targeted sentinel surveillance at wildlife–livestock–human interfaces. However, effective preparedness requires distinguishing pathogen-specific risk characterization from system-level preparedness design. This distinction is essential for the systems architecture proposed here: pathogen-specific insights determine which signals should be monitored, whereas the preparedness architecture defines how surveillance, analysis, and response capabilities are organized. In our proposed four-layer preparedness architecture, targeted sentinel surveillance is situated within the Sensing layer, alongside syndromic surveillance, environmental sampling, and metagenomic detection. The architecture does not omit pathogen-specific considerations; rather, it provides a generalizable operational framework capable of incorporating virus-specific indicators without constraining preparedness to a single-pathogen scenario. To operationalize the CBRNE preparedness perspective without compromising public trust, our four-layer preparedness architecture should be implemented as an integrated capability stack consisting of Sensing, Inference, Response, and Legitimacy. In this context, “stacked” refers to interdependent operational layers rather than a strictly sequential process. Each layer performs a distinct function while reinforcing the others: sensing systems generate early signals, inference systems analyze and interpret these signals, response systems mobilize diagnostic and countermeasure capacities, and legitimacy mechanisms sustain transparent governance and public trust. Implemented together, these layers create a resilient preparedness infrastructure capable of translating early zoonotic signals into coordinated public health action. Sensing: integrated syndromic surveillance, targeted sentinel sampling, and metagenomic detection where feasible; Inference: standardized analytics with transparent uncertainty reporting and predefined escalation criteria; Response: regionally distributed diagnostic, clinical, and manufacturing pathways to avoid single-point failure; Legitimacy: privacy-preserving data governance, benefit-sharing arrangements, and crisis-resilient public oversight. Central to this model is equity-first capacity design. Equity should be measurable across at least four domains: (1) the geographic distribution of sequencing and diagnostic infrastructure per 10 million population; (2) the proportion of national surveillance budgets dedicated to sustained workforce development; (3) the inclusion of low- and middle-income country representatives in governance and data-sharing consortia; and (4) data sovereignty safeguards that ensure reciprocal access to analytic outputs. Monitoring these indicators annually would allow equity to function as a performance metric rather than a rhetorical commitment. Emerging CBRNE scholarship increasingly emphasizes cumulative, interconnected, and long-horizon risk frameworks that extend beyond acute events.[10] Integrating such perspectives into infectious disease preparedness can strengthen systemic resilience. As Ludovici et al.[1] demonstrate, CBRNE thinking offers an important conceptual foundation for comparative zoonotic prevention. Building on that foundation now requires embedding operational thresholds, equity metrics, and governance safeguards into preparedness architecture so that CBRNE evolves from retrospective justification into proactive global capability. Funding None. Author Contributions M Vijayasimha and M Srikanth contributed to the conception and design of the manuscript and to the interpretation of the relevant scientific literature. M Vijayasimha drafted the initial version of the manuscript. M Srikanth critically reviewed and revised the manuscript for important intellectual content. Both authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Conflicts of Interest None.