Urban zoonotic spillover has historically been detected only after human cases appear. An integrative review published in Frontiers argues that multi-omic biomarkers can shift that timeline — moving surveillance from reactive confirmation to predictive detection before an outbreak takes hold in a city.

The review consolidates evidence across omic platforms — genomics, transcriptomics, proteomics and related high-throughput profiling — and evaluates which biomarker signatures carry genuine predictive value for spillover risk in urban settings. That setting matters. Dense human-animal interfaces, from rodents and bats to companion animals and peri-urban livestock, concentrate the contact structure that drives spillover.

What did the review actually examine?

As an integrative review, the study does not report new trial data. It synthesizes existing literature on candidate biomarkers and asks a practical question: which molecular signals, measurable in animal reservoirs or environmental samples, reliably precede a spillover event?

The authors frame the urban context as the core challenge. Spillover detection in cities demands biomarkers that:

• Perform in heterogeneous reservoir populations rather than single well-characterized host species
• Remain detectable in accessible sample types — feces, saliva, environmental nucleic acids, ectoparasites
• Distinguish genuine spillover risk from background microbial noise in microbiologically crowded urban ecosystems
• Feed into surveillance systems fast enough to trigger intervention before human transmission

What changes at the exam table and in the barn?

For veterinary practitioners, the review's relevance is indirect but concrete. Multi-omic surveillance pipelines depend on sample collection from the animal side of the human-animal interface — and veterinarians, veterinary technicians and wildlife health officers are the professionals positioned to collect them.

Companion-animal practices sit inside the urban interface the review targets. Dogs and cats share pathogens with rodent and bat reservoirs in the same city blocks, and baseline multi-omic data from companion animals could help surveillance systems calibrate what "normal" microbial traffic looks like in a given urban ecosystem.

Food-animal and public-health implications run together here. Peri-urban farms, live-animal markets and backyard flocks concentrate reservoir-host contact. Biomarker-based early warning in those nodes could flag amplification events — the step where a reservoir pathogen acquires transmission traits — before human index cases surface.

The public-health stake is the timeline. Post-spillover outbreak response costs more and starts later than risk-based detection. If biomarker panels can rank urban hotspots by spillover probability, public-health authorities could direct sequencing capacity and vector control toward the highest-risk interfaces first.

What are the open problems?

The review's integrative format means its findings are only as strong as the underlying primary literature, which remains uneven across omic layers. Genomic surveillance of urban reservoirs is comparatively mature; transcriptomic and proteomic predictors of spillover — signals of host immune pressure, pathogen gene expression shifts, or protein-level markers of imminent host switching — are earlier in development.

Translating a candidate biomarker into a field-deployable assay also imposes constraints the review addresses: cost per sample, turnaround time, and the analytical validation needed before a panel can inform resource-allocation decisions rather than merely generate hypotheses.

None of the approaches surveyed carries regulatory status as a diagnostic at this stage; these are research-grade surveillance tools, not licensed tests reviewed by FDA CVM, EMA or USDA. That distinction matters for any practice weighing whether to participate in surveillance programs.

Why "predictive" is the operative word

The review's central claim is architectural rather than a single finding: no single omic layer predicts spillover well, but integrated multi-omic signatures — combining host, pathogen and environmental data — plausibly can. Convergence across independent molecular signals offers stronger evidence than any one platform alone, and integration is where the field is now investing.

For the veterinary profession, the near-term ask is sample supply and cross-sector data sharing. Longitudinal, metadata-rich samples from urban animals are the raw material every biomarker pipeline in the review depends on, and veterinary surveillance networks are the most direct route to obtaining them at scale.

The review points toward a future in which urban spillover surveillance draws on continuously updated multi-omic baselines — and the veterinary profession's role in building those baselines will determine whether prediction becomes operational.