A recent paper by Simon Grimm, published through the Institute for Progress (IFP), offers a detailed, data-driven proposal for transforming the United States’ capacity to detect emerging pathogens. Drawing from cost trends, technical feasibility, and policy precedents, it makes the case that within two to three years, the U.S. could implement a metagenomic sequencing network, augmented by frontier artificial intelligence (AI), to detect both known and novel threats far earlier than is possible today.
The Surveillance Gap and Emerging Risks
Current pathogen surveillance in the U.S. is optimized for detecting known threats through targeted assays such as quantitative polymerase chain reaction (qPCR). While effective for diagnostics, this approach leaves blind spots for novel or engineered pathogens. These blind spots are especially dangerous in light of advances in frontier AI systems, which are already demonstrating expert-level capabilities in virology. Without a rapid, scalable detection infrastructure, the nation could face months-long delays in recognizing a new outbreak, as happened historically with pathogens like H5N1.
Metagenomics: The Core Technology
The proposed solution centers on deploying large-scale metagenomic sequencing. Unlike targeted testing, this approach sequences all genetic material in a sample, enabling identification of known organisms and flagging of unknown sequences for further study. Once prohibitively expensive, sequencing costs have dropped to the point where national-scale deployment is now feasible.
For rapid-turnaround swab samples, Oxford Nanopore Technologies sequencing can deliver results in under 24 hours. For wastewater samples, which require greater read depth due to lower pathogen concentrations, high-output Illumina platforms can process data within 48 hours.
AI as a Force Multiplier
AI can be layered onto metagenomic data streams to produce actionable intelligence. Anomaly-detection models trained on months of historical sequencing data could sift through billions of daily reads from sources such as airport wastewater. Suspicious sequences—those not matching known pathogens—could then be analyzed by AI protein-folding models to identify functional similarities to dangerous viral proteins, such as influenza hemagglutinin. This workflow allows for the detection of threats even when their genetic makeup is entirely novel.
The approach mirrors the public-data model of the National Weather Service: by openly publishing metagenomic data within 24 hours, government can enable external researchers, companies, and nonprofits to innovate beyond what a single agency could develop in-house.
Investment and Implementation Plan
The funding requirement is approximately $100 million per year for two to three years—a cost comparable to a single F-35 fighter jet. Allocations within the CDC’s Biothreat Radar Detection System could include:
- Traveler Surveillance ($44M): Expanding nasal swab sampling and sequencing at 20 U.S. airports.
- Airplane Wastewater Surveillance ($10M): Scaling pooled wastewater testing to additional airports.
- Respiratory Sample Surveillance ($26M): Sequencing PCR-negative respiratory samples from large commercial laboratories.
The Defense Innovation Unit (DIU) could support development of AI algorithms capable of handling billions of reads per day, while the Department of Health and Human Services (HHS) sets data-sharing standards that preserve privacy while enabling open access.
Implications for Public Health and National Security
Metagenomic surveillance would enhance seasonal pathogen tracking, improve outbreak response times, and reduce the economic and social costs of infectious disease. From a national security standpoint, it functions as an early-warning system for biological threats—whether arising naturally or through deliberate engineering—analogous to radar for air defense or intrusion detection in cybersecurity.
From Proposal to Preparedness
By treating metagenomic surveillance as critical national infrastructure and following the blueprint outlined in the IFP paper, the United States can shift from a reactive to a proactive stance in pathogen detection. The combination of rapidly falling sequencing costs, mature AI analytics, and the precedent of open government data in other domains provides both the means and the model for success. The barrier is not technology—it is the commitment to build and sustain the system before the next crisis arrives.
Grimm, S. Scaling Pathogen Detection with Metagenomics. Institute for Progress Nucleic Acid Observatory.