Emerging viral pathogens present a growing challenge to global health, but critical information about them is often scattered across academic papers, health reports, and other disparate sources. This fragmentation makes it difficult for researchers, public health agencies, and policymakers to quickly access standardized, up-to-date data needed to track and mitigate risks. A centralized, systematically classified dataset could bridge this gap, enabling faster response times and more informed decision-making.

What Could This Dataset Include?

One way to structure this dataset would be to classify viruses by multiple key dimensions:

• Taxonomy (e.g., Riboviria, Monodnaviria)
• Molecular biology (e.g., Baltimore classification like dsDNA or ssRNA viruses)
• Epidemiology (e.g., transmission methods such as zoonotic, airborne, or vector-borne)
• Geography (regions of emergence or concern)

Additional fields could include host species, virulence factors, and links to genomic data. Over time, this could evolve into a dynamic platform with API access, allowing integration with research tools or outbreak modeling software.

Who Could Benefit and How?

Several groups could find value in such a resource:

• Researchers studying pathogen evolution or spillover events
• Public health agencies tracking emerging threats
• Biotech and pharmaceutical companies developing treatments
• Veterinary scientists monitoring viruses with zoonotic potential

Incentives for participation could include research collaboration opportunities for academic institutions, improved outbreak preparedness for health organizations, and enhanced biosecurity for governments. Publishers might partner to provide curated metadata while maintaining some proprietary interests.

How Could This Be Implemented?

A possible execution path might involve:

1. Starting with an MVP containing 50-100 high-profile pathogens in a simple CSV/JSON format
2. Partnering with virology labs to validate classifications and fill data gaps
3. Scaling to a web platform with search functionality and user submissions
4. Sustaining through a freemium model with basic free access and paid premium features like API usage

To test feasibility, one could manually classify a subset of pathogens to assess consistency challenges or create a waitlist to measure interest. Addressing potential biases (like overrepresentation of human pathogens) could involve collaborating with veterinary databases.

While existing resources like NCBI Virus or GIDEON provide valuable data, this approach could offer unique advantages by combining comprehensive classification with dynamic updates and interoperability features. It would aim to bridge the gap between genomic data and practical public health needs.