📄 public and global health

Leveraging the U.S. blood supply to detect emerging viral threats

This paper proposes integrating metagenomic sequencing into the U.S. blood supply system as a proactive, cost-effective surveillance tool to detect emerging viral threats before they cause widespread clinical illness.

Original authors: Justen, L. J., Bhasin, H., Whittaker, C., Cunningham-Bryant, D., Lanteri, M., Busch, M. P., Esvelt, K. M., Sabeti, P. C.

Published 2026-06-21

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Justen, L. J., Bhasin, H., Whittaker, C., Cunningham-Bryant, D., Lanteri, M., Busch, M. P., Esvelt, K. M., Sabeti, P. C.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine the United States has a massive, nationwide network of blood donation centers. Every year, millions of people walk in to give blood or plasma. Right now, when these donations arrive, labs perform a very specific "security check." They look for about a dozen known bad guys—like HIV, Hepatitis B, and Hepatitis C—using a checklist. If a donation passes the checklist, it goes into the supply; if it fails, it's discarded.

This paper proposes adding a new, powerful tool to that security check: Metagenomic Sequencing (MGS).

Here is the simple breakdown of what the authors are suggesting, using everyday analogies:

1. The Problem: The "Known Bad Guy" List

Currently, the blood supply is like a bouncer at a club who only checks IDs against a list of 12 known troublemakers. If a new, unknown troublemaker shows up wearing a disguise (a novel virus), the bouncer doesn't have their name on the list, so they get let in.

History shows us this is dangerous. Viruses like HIV and Hepatitis C circulated in the blood supply for years before anyone realized they were there because the "bouncer" wasn't looking for them.

2. The Solution: The "Fishing Net" Approach

The authors suggest replacing the specific checklist with a giant, high-tech fishing net. Instead of looking for specific names, this net catches everything in the blood sample.

How it works: They take the leftover blood samples (the "residual" bits) that are already being tested for the known viruses. Instead of throwing them away after the test, they run them through a machine that reads the genetic code of every virus present.
The Benefit: If a brand-new, never-before-seen virus is in the blood, this net catches it immediately. It doesn't need to know the virus's name to know it's there.

3. Where the Samples Come From

The paper looks at two main sources, which are like two different types of blood donation:

Whole Blood (The Community Donors): People who give blood for transfusions (like the Red Cross). These samples are often mixed into small "mini-pools" (like mixing 10 cups of water together) to test for known viruses. The authors suggest sequencing these mini-pools.
Source Plasma (The Paid Donors): People who donate plasma specifically to make medicines. This industry is huge and collects samples much more frequently. They also mix samples into pools, sometimes even larger ones.

The authors argue that using these existing "leftover" samples is like finding a free, pre-packed lunch. You don't need to stop the donation process or ask donors for extra blood; you just use what's already sitting in the lab.

4. The Cost: A Small Price for Big Safety

The authors did the math on how much this would cost.

The Estimate: They calculate that the U.S. could run this nationwide surveillance system for about $5.5 million a year.
The Analogy: Think of this as the cost of a few new fire trucks for a large city. In exchange, the country gets an early warning system that could spot a new, dangerous virus before it spreads to even 0.01% of the population (about 34,000 people).
The Goal: The system is designed to catch a virus early, ideally before it becomes a full-blown epidemic.

5. What It Can and Cannot Catch

The paper is very clear about what this "net" can catch:

It's Great For: Viruses that hide in the blood without making the donor sick immediately. Think of viruses like HIV or Hepatitis, or mosquito-borne viruses like Zika. These can be in the blood for a long time while the person feels fine.
It's Not Great For: Viruses that live in the lungs or stomach, like the flu or the common cold. These usually don't show up in the blood unless the person is very sick (and very sick people don't donate blood). The paper notes that this system should complement other systems (like testing wastewater or sick patients), not replace them.

6. Privacy: Keeping Donors Anonymous

A major concern is privacy: "If you sequence the blood, won't you find out who the donor is?"
The authors explain that the system is designed to be safe:

The "Smoothie" Analogy: They test "mini-pools" (mixing many donors together). It's like making a smoothie with 10 different fruits; you can taste the fruit, but you can't tell which specific apple came from which specific tree.
De-identification: All donor names and personal info are stripped away before the samples ever reach the sequencing lab.
Existing Rules: This follows the same privacy rules already used during the pandemic when the CDC tested blood donors for SARS-CoV-2.

Summary

The paper argues that the U.S. blood supply is an underused treasure trove for spotting new diseases. By adding a "read everything" genetic scanner to the existing blood testing process, the country could spot dangerous new viruses months or even years earlier than we do now. It's a low-cost, high-tech upgrade to our national immune system, using samples that are already being collected and tested.

Technical Summary: Leveraging the U.S. Blood Supply for Emerging Viral Threat Detection

Problem Statement
Current infectious disease surveillance systems are predominantly reactive, relying on the detection of clinical illness or familiar pathogens. This approach creates a significant lag in identifying novel threats, particularly those with long incubation periods or asymptomatic transmission, as evidenced by the delayed recognition of HIV, Hepatitis B, and Hepatitis C. While the U.S. blood supply represents a massive, demographically diverse, and geographically distributed resource (approximately 12 million whole-blood and 40 million plasma donations annually), it is currently underutilized for proactive pathogen surveillance. Existing blood screening relies on targeted nucleic acid and antibody assays for a limited panel of pre-specified agents, leaving the system blind to emerging or unknown pathogens that may circulate asymptomatically.

Methodology
The paper proposes integrating metagenomic sequencing (MGS) into existing blood collection and testing workflows to create a pathogen-agnostic early warning system. The methodology focuses on utilizing residual samples generated during routine Transfusion-Transmitted Infection (TTI) testing, specifically:

Whole Blood Sector: Leftover aliquots from individual donations and "mini-pools" (MP) of 4–24 donations used for routine NAT.
Plasma Sector: Residual mini-pools (50–1,000 samples) and, to a lesser extent for early warning due to inventory holds, large manufacturing pools (1,000–60,000 samples).

The proposed workflow involves sending these de-identified residual samples to a designated laboratory for MGS. Bioinformatic analysis would identify known and novel pathogens, triggering targeted confirmatory assays via existing traceback infrastructure if a concerning signal is detected. The authors emphasize that this system is designed to complement, not replace, current targeted TTI testing.

To evaluate feasibility, the authors developed a cost-modeling framework adapted from prior outbreak dynamics models. This model calculates the annual investment required to detect a novel pathogen before it exceeds a specific cumulative incidence target (e.g., 0.01% of the population). The model incorporates:

Outbreak dynamics (e.g., a hypothetical HIV-like pathogen with a 43-week doubling time).
Pathogen characteristics (e.g., mean relative abundance of pathogen reads in infected individuals, set at $\mu=1.26\times10^{-5}$ based on acute HIV data).
Sequencing parameters (e.g., detection threshold of 100 reads).
Operational costs (sample acquisition, sequencing, labor, processing, and data management).

Key Contributions and Results

Operational Feasibility: The paper demonstrates that the infrastructure for large-scale, de-identified sample access already exists through partnerships between blood collection organizations (e.g., American Red Cross, Vitalant) and public health agencies, as established during the COVID-19 RESPONSE study and other surveillance initiatives.
Cost-Effectiveness Analysis: Modeling suggests that an annual investment of approximately $5.5 million (range $3.5–$ $3.5-$ 26.2 million) could support a national MGS-based monitoring program. This system would be capable of detecting a novel HIV-like pathogen before it infects 0.01% of the U.S. population (approximately 34,000 infections).
- This target requires sampling 10,000 individuals weekly and sequencing roughly 17 billion reads per week.
- The cost breakdown indicates sample acquisition (47.4%) and sequencing (40.2%) are the primary drivers.
- The model shows that improving sample preparation to increase pathogen relative abundance (e.g., via host depletion) could lower costs, though diminishing returns apply as the rarity of infected donors becomes the limiting factor.
Pathogen Suitability: The analysis identifies that MGS-based blood surveillance is most effective for:
- Persistent/Asymptomatic Viremia: Viruses like HIV, HBV, HCV, and CMV that establish long-term infections.
- Arboviruses: Dengue, Zika, and West Nile Virus, which often have high rates of asymptomatic viremia.
- Limitations: The system is less effective for respiratory (e.g., Influenza) and gastrointestinal viruses, as viremia in asymptomatic individuals is rare or non-existent for these pathogens.
Privacy and Regulatory Framework: The authors argue that using de-identified residual samples for public health surveillance falls under existing regulatory precedents (e.g., "non-research public health surveillance" determinations made during the pandemic). Safeguards include HIPAA-compliant de-identification, pooling of samples to mask individual contributions, and bioinformatic pipelines that remove human sequences prior to analysis.

Significance
The paper posits that leveraging the U.S. blood supply for MGS-based biosurveillance represents a "deployment-ready" opportunity to shift infectious disease detection from reactive to anticipatory. By capitalizing on established donor monitoring, testing, and privacy infrastructure, this approach offers a scalable mechanism to detect novel blood- and vector-borne threats months or years before they would be identified through symptom-based clinical surveillance. The authors conclude that while the system cannot detect all pathogen types (specifically those lacking viremia), it provides a high-leverage, cost-effective layer to national biosecurity that is particularly suited to identifying stealthy, emerging viral threats similar to historical failures in HIV and HCV detection. The framework is presented as broadly applicable to other nations with comparable blood collection infrastructure.