This is a lightly-edited version of a memo that I presented
at the Summer 2026 Biosecurity Summit outside of DC. While others at
SecureBio often see things similarly, I'm attempting to present my
view and not a SecureBio "house view".
The Goal
We need to be robust to adversaries who want to cause very large-scale harm
with biology. This includes actors (human or AI) who want to kill all humans,
cause short-term incapacitation or long-term civilizational collapse, or who
have strategies for sparing some while they harm others. There are multiple
reasons an actor might have these targets aside from being directly omnicidal,
such as reducing response capacity during an AI takeover.
That there is an attacker itself is a key constraint to any defensive
system: it must be designed for adversarial attacks. The attacker can assess
the state of the world's detection systems and plan accordingly. Taken to the
extreme, this presents a "minimax" landscape: a system is only as good as its
weakest link (the place where it is least sensitive). This is an important
framing, and it correctly prioritizes getting some sensitivity towards a wide
range of attacks over very high sensitivity towards just a few. On the other
hand, (a) to the extent that gaps depend on non-public choices, you can
maintain strategic ambiguity to prevent attackers from aiming for the gaps, and
(b) reducing the number of gaps reduces attacker options.
The strongest form of success is to deter an attacker by denying them the
ability to achieve their goal: an adversary who knows an attack wouldn't
accomplish their goal will generally not try that attack. This deterrent
effect is tightly coupled to the extent to which detection would indeed thwart
the achievement of the attacker's goals. This means that achieving
deterrence-by-denial means both building an effective system, from detection
through to action, and making it known that you've built it.
The other main kind of deterrence is deterrence-by-punishment. If you
develop strong attribution capabilities, an attacker risks identification and
retaliation. The extent to which this would deter an adversary, however,
depends a lot on what they have to lose. A state seeking strategic advantage
might be deterred by the prospect of retaliation, while someone keen on killing
everyone (including themselves) has little left to threaten.
Biosurveillance Applications
Where specifically does biosurveillance fit in? What are the threats, and
where can it make the difference between an attacker succeeding and
failing?
Initial Detection of Stealth Pandemics
A stealth
pandemic is one where a pathogen spreads through most of the population
unnoticed, with no or unremarkable symptoms, before causing very serious
effects. [1] If it were subtle enough, people wouldn't realize how serious the
situation was in time to respond effectively. While we call these "stealth
pathogens", whether a given pathogen would cause a stealth pandemic depends on
the interaction between the pathogen, the body, and humanity's many ways of
noticing that something unusual is happening.
Whether it is possible to create a pathogen that would be sufficiently
difficult to notice is an open question: I've heard different things from
different experts. When considering (a) the significant advances in biological
design tools and general biological understanding that we've been seeing with
AI progress, and (b) the speed and unpredictability of the process by which
unusual symptoms today lead to attention and action, I do think there's a
significant chance that within the next five years many actors would be in a
position to cause stealth pandemics.
Detection of suspicious sequencing reads may not be sufficient to estimate
whether they represent an ongoing stealth pandemic. A pathogen may be
constructed in a way that makes its potential for rapid spread and delayed harm
obvious, but that is far from guaranteed. Assessing this likely requires
additional scientific work: genome completion, estimating likely effects in the
human body, and considering whether the genome suggests an intentional
attack.
Triggering Initial Response
A pathogen doesn't have to be stealthy to be disastrous: it could simply be
very hard to contain (a
"wildfire
pandemic"). Beyond its direct effects, such a pathogen could be
intentionally released to reduce capacity at a critical time, such as during a
coup or an AI takeover attempt.
Whether an outbreak is wildfire, stealth, or has aspects of both, the time
from initial discovery to serious response is critical. Initial indications
are generally ambiguous, and it is often difficult to understand the extent or
trajectory of the threat. With
the 1976 swine
flu, we overreacted and vaccinated 45M people because we didn't have the
monitoring to know it wasn't spreading widely.
With 2014
ebola in West Africa, we underreacted and let it spread freely for three
months because the extent wasn't recognized. Similarly,
with 2026
ebola, we saw another three month delay, this time in part because field
PCR
tests couldn't
see it. The case
of 2009 H1N1,
however, showed how a well functioning (though flu-specific) biosurveillance
system could enable timely response. In today's COVID-weary climate where
public health is deeply worried about losing credibility through false alarms,
biosurveillance can help avoid a default of delaying response while waiting for
more information.
Enabling Ongoing Suppression
Once an initial response is in motion, you need monitoring to effectively
deploy mitigations and know whether they're working. How much value there is
depends on how symptoms relate to infectiousness. If symptoms are absent or
highly delayed, effective response is essentially impossible without solid
monitoring. At the other extreme, if symptoms are highly visible and begin
immediately, monitoring is moderately valuable: you know you have a problem,
but with "fog of war" you don't fully know its extent or distribution.
Large-scale monitoring allows you to compare locations and track trajectories
to optimize resource deployment.
I give relatively little attention to this biosurveillance application in
this memo, mainly because I think it's a place where what exists today is
closest to what needs to exist, so this is a lower priority for additional
work.
What does success look like?
There's no single threshold, where you win by building a system with a
specific level of capability. Biosurveillance systems reduce the cost-benefit
tradeoff of initiating a pandemic; increasingly capable systems decrease the
likelihood that an attacker deems this worth their effort and reduce the harm
if they decide to attack. Still, for each application, there are some 'sweet
spots' where the cost-benefit ratio is maximized.
Initial Detection of Stealth Pandemics
If you imagine the most capable system that can be deployed for a given
level of investment, it will be capable of averting some fraction of expected
possible stealth harm. Here's how I see it:
A system that is too slow to beat status-quo detection might still help
with triggering initial response, but doesn't provide initial
detection benefit.
A system that is a bit faster but doesn't give enough time to act before
most people are already infected provides some benefit, especially in cases
where the harm can be mitigated if it's known soon enough, but most expected
harm will still occur.
A system capable enough to flag an attack in time to allow us to protect
enough workers, such that (a) civilization does not collapse and (b) medical
countermeasures can be developed and deployed, provides significant benefit.
Attacks could still be massively disruptive.
An extremely capable system, including a very broad sampling regime,
could flag outbreaks when they were still small enough to contain. At this
point an attack is still costly, but the disruption is limited to the areas
where the attack was seeded.
None of these are hard boundaries. There are factors that 'smear' these
thresholds across many capability levels: some of this is luck (ex: who
contributes to what samples), while some is uncertainty about the world that
both we and an attacker would share (ex: how much shedding a given pathogen
would actually produce in a large population). Here's an illustrative
chart:
I've intentionally left the x-axis vague. It's not "cumulative incidence at
detection", because (a) that's horizontally smeared as described above and (b)
it would imply that increased capacity is downstream of sensitivity only and
not other important factors like what kind of pathogens you can detect at all
or how quickly you can trigger response. Instead, the x-axis represents the
level of resources invested.
A system that is sufficiently capable to flag pathogens early enough to
protect enough workers to avert civilizational collapse is well worth the
investment; additional sensitivity beyond this is valuable, but likely
substantially less cost-effective.
This means that success looks like a pathogen-agnostic system that flags
attacks in time to protect enough workers to prevent civilizational collapse
and buy time for pathogen-specific mitigations. How early this needs to be
depends on how quickly response can happen. If effective response takes a
month from detection, you need to flag before ~0.05% of people have been
infected. On the other hand, if it takes two weeks, you only need to flag
before ~2% of people have been infected, and if you can get it down to seven
days, then even flagging at 10% cumulative infections would be enough.
See appendix for more detailed
reasoning.
Note that the "fast enough to beat status-quo detection" regime would be a
far higher bar for wildfire than stealth. This means that, on time scales
rapid enough to factor into planning, I don't expect it to be economically
feasible to build a system where biosurveillance would be your first indication
of a wildfire pathogen.
Triggering Initial Response
We don't know very much about what would actually get decision-makers to
take sufficiently prompt action. In a stealth scenario, this is extremely
challenging, since response must begin before the main symptoms manifest, but
even in a wildfire scenario, there's a huge difference between a response that
follows immediately from when someone identifies the first cluster vs one where
it kicks off in earnest only after deaths start to become highly visible.
Success looks like a very short time, ideally under a week, from when a
catastrophic pathogen is flagged until the danger has been recognized, PPE has
been distributed to essential workers, biohardening has been deployed or
activated, lockdowns have been instituted, and development of rapid diagnostics
and other medical countermeasures has begun. These are very costly actions, in
economic terms but also via anteing political capital and institutional trust.
Biosurveillance can contribute by giving decision-makers the information to
determine whether those costs are worth paying. This looks like clarifying the
extent of spread to date, estimating trajectory, and performing initial wet lab
work such as genome completion.
Beyond the technical work, response requires trust. Before someone will act
on an alert from a system they need to believe that it indicates something
real, and that trust needs to be built over time. Non-catastrophic detections
are key, showing you can track trends
that match what
other evidence shows,
surface matters
of public health concern, and turn up real engineered
'benign
positives'.
Most of the work in reducing time to response, however, is outside
biosurveillance. This could include helping government agencies develop better
plans for how to handle various indications, running exercises that get the
actual principals to experience the feeling of making these specific calls with
realistically incomplete information, or streamlining inter-agency
communication so available clinical and epidemiological data gets to the right
people quickly.
Enabling Ongoing Suppression
Wastewater PCR was used
by Australia, New
Zealand,
and Singapore
(down to
the building
level), among others, as part of their suppression strategies to guide
public health response during COVID-19. At the point when transmission has
been diminished to some threshold, or if an attack is identified before the
pathogen has spread widely, a sensitive biosurveillance system can tell you
when and where you need to focus more expensive and intrusive detection methods
and interventions.
This means that a system for successfully maintaining suppression looks like
a larger-scale and more fine-grained implementation of the biosurveillance
component for triggering initial response. Knowing that something is spreading
in ten major cities around the US might be enough to spur rapid action, but you
need a much more detailed picture if you're trying to maintain ongoing
suppression.
What gets us there?
Initial detection of stealth pandemics
is SecureBio Detection's focus,
and where I have the most developed view. I'll walk through what I think is
needed for this scenario, and then discuss how this changes for other
scenarios. Please don't interpret this structure as a claim about the relative
likelihood of stealth scenarios!
Sampling Strategies
Municipal wastewater is a very helpful sampling modality: in most cities,
sewage is processed at a small number of locations, letting you track pathogens
across hundreds of thousands of people from a single easily-collected sample.
Since many pathogens don't shed heavily into wastewater, however, and it's a
very noisy sample type, comprehensive initial detection at a reasonable cost
likely requires tracking additional sample types. SecureBio runs
a nasal swab program; beyond swabs
I see air, blood, aircraft wastewater, and leftover material from clinical
tests (clinical lab discards) as the strongest candidates for supplemental
sampling. SecureBio has looked into these strategies in some depth
(initial
overview, blood, aircraft
wastewater, clinical
discards), but there's still a lot that could be learned here.
Lab Technology
You need technology that is pathogen agnostic: if you monitor only specific
pathogens, the adversary can choose ones you don't monitor. With current and
near-future tech, "pathogen agnostic" means sequencing. For viruses, it is
practical to concentrate particles by size, and then perform untargeted and
enriched metagenomic sequencing metagenomic sequencing. This lets you do the
rest of the detection in the computer, for maximum flexibility and generality,
and this is what SecureBio does today.
For bacteria, I'm pessimistic about adapting this approach directly, at
least with wastewater, because the genomes are much larger and there is a rich
background of sewer bacteria that can't be physically separated from
potentially threatening bacteria before sequencing in the same way that viruses
can. How to handle this is still an open question; see below.
For mirror life, the initial stages of spread might be very hard to
recognize, and an important step would be learning that mirror life was
spreading at all. It's likely that a highly sensitive and relatively cheap
assay could be developed, but no one has started on this yet.
Computational Technology
Metagenomic sequencing moves much of the problem of initial detection from
the lab into the computer. This means massively more sequencing reads than
humans could evaluate (8+ orders of magnitude), so we need a detection system
that can identify which reads indicate something concerning is happening. Some
reads can easily be recognized as concerning (ex: nucleic acid subsequences
unique to the smallpox genome should not be in wastewater) while others require
very sophisticated processing (ex: understanding the complex background well
enough to flag de novo genomes with no sequence similarity to anything
currently existing).
The general
approaches are looking for sequences with one or more of the following
features:
Dangerous. Sequences matching known pathogens that you would not expect to
see in the sample, perhaps signaling that they've been introduced.
New. Sequences that you haven't seen before, perhaps because they were
created de-novo.
Growing. Sequences that are becoming more common, perhaps signaling that
they're spreading through the human population.
The computational approach is very difficult given the scale of the data,
uncertainty over what an attack might look like, and the need for rapid
analysis. On the other hand, these are the kinds of highly computational
problems where I expect AI can be very productively applied, whereas many other
aspects of this system require relatively slow real-world effort.
What exists today?
Pathogen-agnostic biosurveillance is still in its early stages. I know of
four systems doing untargeted metagenomic sequencing for biosurveillance
today:
CASPER (SecureBio
+ Marc Johnson's
lab at the University of Missouri and other academic partners). This is
wastewater, primarily municipal, from 49 facilities representing 24 US cities.
It's virus-focused, and generated with very deep short-read sequencing. Lab
work happens independently at MU and SecureBio, with bioinformatics at
SecureBio.
Zephyr (SecureBio
+ Helena
Solo-Gabriele's lab at the University of Miami). This is pooled nasal
swabs from Boston and Miami, collected primarily in public places, bringing in
swabs from about 1,000 people weekly. It's virus-focused, and unlike CASPER
uses long-read sequencing.
ANTI-DOTE
(DoW + PHC + SecureBio). This is
wastewater from five US military facilities, which SecureBio processes under
contract from PHC. These samples go through the same lab and bioinformatic
processes as CASPER samples.
mSCAPE (UKGOV). This is
bronchoalveolar lavage from UK hospitals, currently including relatively few
samples, which limits sensitivity. Unlike the previous three, mSCAPE uses
combined viral and bacterial sequencing. These samples are sequenced to a
relatively low depth with long-read sequencing, and the data supports clinical
practice, public health, and biodefense.
There are also several hybrid-capture sequencing projects that might detect
an attack if the agent was similar enough to existing pathogens.
In addition to detection via pathogen-agnostic sequencing, there are also
paths where an outbreak becomes visible via showing symptoms in a sufficiently
large fraction of infected people. This could lead to suspicious clusters, and
then to sequencing and noticing that a genome looked edited. This is not a
well-developed path today, but (as discussed below) I'd like to see investment
here.
What's missing?
Here's an overview of what I think most needs doing. It represents the
current state of my thinking, but it's not as thoroughly considered as I wish
it were: please don't overweight it in your own decision-making! While
SecureBio Detection is exploring some of these, I think the ideal structure is
a healthy ecosystem of organizations taking on different parts of the problem
in parallel. SecureBio is often able to share samples, sequence prepared
nucleic acids, or share data to
help others make progress.
In roughly descending order of how valuable I estimate non-SecureBio work
would be, representing a combination of both overall value and the value of the
work happening independently:
[Other] Modeling. We need good estimates of the necessary system scale and
ideal network design to support optimal initial detection, response triggering,
and ongoing suppression. There are initial estimates, but because this is a
huge question (essentially pulling the whole field together), current work is
rough. Rigorous treatments would be valuable for planning, both for
independent funding allocation and for governments. This is especially
valuable to happen outside of SecureBio, as a way of checking our work, and
doesn't need to all be executed by one group (groups can pick off
subquestions).
[Comp] Red teaming. It's important to analyze existing systems to assess
how well they would handle a range of adversarially designed attacks. This can
be done with reference to the system implementation, or by treating the system
as a black box.
[Other] Parallel orgs. SecureBio Detection has historically focused on the
US. Setting up parallel orgs in other geographies, especially Europe and Asia,
would be really valuable. We are happy to advise anyone interested in doing
this work!
[Lab] Detection of stealth bacterial pathogens. Viral particles are small,
which means that you can separate them from human and bacterial cells without
specifying in advance what sequences you're interested in. Beyond this,
pathogenic viruses represent enough of the total viral portion that just
pulling it all into the computer to sort it out there is economical. This
method can't be directly applied to bacteria, at least not in wastewater,
because there is so much irrelevant bacterial genetic material. SecureBio
hasn't done any work in this area yet, but some plausible approaches include
aggressive depletion for things you know are not worrying, partially-targeted
methods, and working with samples with a more favorable background. People
have a wide range of estimates on how difficult this will be, but personally I
expect it to be very hard.
[Comp] Parallel methods development. It would be great for others to be
exploring other avenues in parallel. Since our main expertise is in
traditional bioinformatics, I'd be especially excited to see people try other
approaches, such as applying deep learning. We share much of our sequencing
data publicly
(PRJNA1247874, PRJNA1379685),
in part to facilitate exactly this type of work.
[Lab] Sampling streams beyond wastewater and nasal swabs. Not everything
sheds much into wastewater or the nose. A comprehensive system very likely
needs a wider range of sample types. Blood, air, and clinical lab discards are
all very promising here.
[Other] Response. Figure out what would get decision-makers to reliably act
rapidly in a real emergency, and lay the groundwork so that happens. As noted
above, this is probably mostly not biosurveillance. There are components (ex:
developing escalation relationships) that need to be tightly coupled with
biosurveillance, perhaps within SecureBio and parallel orgs, because of the
limitations of information sharing. Other components (ex: policy
recommendations, public outreach) make more sense as separate organizations.
This is delicate work, however, and someone coming in noisily and insensitively
could easily set the field back.
[Lab] Genome completion. A pathogen identified by a short-read
biosurveillance system like CASPER would start as just a suspicious section of
a genome. You can sometimes learn more about the genome through techniques
such as outward
assembly, but only if you happened to sequence the relevant reads. Lab
work to assemble the whole genome lets you better understand what the pathogen
would do in a human, which is likely on the critical path to both assess
whether a response is warranted (and, if so, what that response should be), as
well as to get people to act appropriately quickly.
[Comp] Analysis tooling. How do you go from "this genome looks worrying" to
a good understanding of whether it's engineered and what effect it would have
in humans?
[Other] Swab collection.
Currently, Zephyr requires field
samplers standing on street corners and interacting with the public, and a team
brings in ~40 samples per hour. If instead workplaces or schools could be
convinced to integrate sample provision into daily routines, this could be far
more scalable.
[Other] Clinical sequencing. Sequencing of clinical samples will likely
eventually be deployed broadly based on its clinical benefits alone, displacing
a wide range of pathogen-specific tests, but by default, this displacement
process is far too slow. Sequencing needs to be a standard option doctors can
easily reach for
when someone
presents with unusual symptoms. I think the tech is ready, or could be
ready with a small amount of R&D, and so this is a commercial
opportunity.
[Lab] Sensitivity increases. Untargeted metagenomic sequencing for
pathogen-agnostic initial detection is an early-stage field, with relatively
few people exploring it, and so on priors I expect there are large sensitivity
improvements waiting to be discovered.
[Comp] Deterrence tracking. You can ask models to estimate the probability
of an attack achieving its goals. If models report low probability to
defenders, they probably report low probability to attackers. The best models
I have access to can't do a good job at this in response to a simple prompt,
and if you need a series of prompts, you can't expect your answer to reflect
what an attacker would get. I expect this to change quickly, however, and it
would be good to have an automatically-updated tracker showing the range of
answers LLMs give to the question of whether existing detection systems are
sufficient to make an attack not worth an attacker's while. This would require
some thought on which threat models to poll for and how to phrase the question
in a way that is a good proxy for what an attacker would ask. It also closely
ties into red teaming work.
[Lab] Partially targeted methods. Hybrid capture, tiled degenerate amplicon
arrays, and other methods of mismatch-tolerant sequence-based enrichment could
offer far higher sensitivity. While they're unlikely to be sufficiently
general to address all threats, there is a good chance that it makes sense for
a mature detection system to include a partially-targeted component as a way to
effectively exclude large areas of the threat landscape.
[Lab] Cheaper protocols. Sequencing is still an expensive proposition, but
as sequencing has become cheaper the operation of the sequencing machine is no
longer the largest cost on a per-sample basis. Bringing those other costs down
would allow more scale for a given budget.
[Lab] Shorter lab time. Current metagenomics protocols require about a day
on the bench and about a day on the sequencer. There are already strong
pressures for reducing sequencer run time, and new machines are coming out in
the ~8 hour range, but there's a lot of work that could be done to speed up the
rest of the processing.
[Lab] Pathogens outside of bacteria and viruses: fungi (and oomycetes),
parasites, and prions. These are generally much more difficult for an
attacker, especially for strategies that involve rapid spread.
[Lab] Mirror life. Current sequencing wouldn't detect mirror life at all.
Municipal wastewater samples would be a good place to look, which allows you to
re-use much of the collection infrastructure, but would require an assay
developed specifically for mirror life. I have this low because I currently
expect mirror life to take long enough to develop that there will be time later
for assay development.
How does this change for accelerating response to a wildfire pandemic?
During the COVID-19 pandemic, many groups built out PCR-based targeted
wastewater monitoring. In the US, wastewater monitoring networks
include NWSS
(CDC), WastewaterSCAN
(philanthropic), and Biobot (private). EU
member states, Canada, Australia, and other countries track wastewater as a
standard part of their public health systems. There are maybe two dozen
countries that have some form of wastewater PCR that could be retargeted to
track a new pathogen in an emergency once its genome was known.
On the other hand, none of this would move quickly enough today to address a
wildfire pandemic. There are delays throughout the process: some of this is
technical (ex: stocking consumables), but most of it is organizational (ex:
policies that permit setting production work aside, overtime budgeting,
on-calls, deals with synthesis providers for rush orders). Even in a serious
emergency, I think ten days is a good best-case estimate today. With good
preparation, however, three days is possible. I think getting these existing
networks to prepare for rapid turnaround emergency response is really valuable,
and SecureBio has started to have some of these conversations.
This is also a place where the same metagenomic sequencing system you would
build for stealth pandemic detection could help you cut off additional days in
your response. The technical and organizational delays that slow down
PCR-based detection are downstream from how changing targets requires making
changes in the physical world. Untargeted sequencing lets you skip those
steps, at the cost of much lower sensitivity. On the other hand, once you know
what you're looking for, you can use approaches (like PCR) that are
significantly more sensitive in the case of SCV2, by
a factor of about
100. If you've built a sequencing system that can flag a stealth
pandemic before 1% of people have been infected, then that factor of ~100 means
it would be able to confirm a wildfire pandemic at ~0.01%. Still,
it's not clear to me that even 0.01% is early enough. It's possible that you
need 0.001% or even lower, at which point this argues either for a
substantially larger investment in untargeted sequencing (to get enough data
quickly) or giving up on sequencing for this application (because it can't
economically reach the target sensitivity).
If you wanted to firmly decide whether to go with MGS or PCR to accelerate
initial response to a wildfire pandemic the key thing you'd need would be
estimates of (a) what fraction of the population would likely be infected when
a wildfire pandemic was noticed, and then (b) how many doubling periods there
would be before decision-makers took action in the absence of this system. On
the other hand, I'm not sure a firm decision is needed, and instead lean
towards different groups exploring these approaches in parallel.
How does this change for ongoing suppression?
The same PCR-based targeted wastewater monitoring that was built for
COVID-19 and could potentially accelerate response to a wildfire pandemic could
also be applied to ongoing monitoring to support suppression. This is already
widely understood to be valuable, but there is still less investment here than
there should be. In a legitimate emergency, I expect governments to be able to
organize existing capacity and deploy it reasonably well, but not as quickly as
would be ideal. My bigger worry is whether that existing capacity would be
large enough. For example, you would ideally have monitoring at the
neighborhood or building level, which means you'd need a very large number of
in-manhole composite samplers. Since these are low-volume products built by a
small number of manufacturers, it would be hard to build more quickly during a
crisis.
The main work is building up capacity in advance that can be quickly
deployed in an emergency. The best bet for such capacity is systems installed
for ongoing public health monitoring: this ensures that they work, including as
part of a larger system, and that lots of people know how they work. It also
gives some ongoing benefit, which may make it an easier sell than
stockpiling.
Appendix: Initial Detection Scale
There is a long chain of reasoning in estimating in what fraction of attacks
a system would achieve the goal of protecting enough workers, and that chain
involves several steps where our knowledge is limited. Still, we can make the
best estimates we can. The three key parameters, are:
How quickly would the pathogen double as it spread through the population?
This is a combination of the basic reproduction number and generation time.
We've generally worked from a doubling period of ~3 days, which represents the
high end for naturally occurring pathogens. You could argue that it should be
lower, because a pathogen could be designed to spread much more quickly than
existing pathogens, or higher, because of physical limits on how quickly a
pathogen can spread without attracting attention.
What fraction of workers would need to be protected to allow the development
of medical countermeasures and avert collapse? This is also not something
we've focused on, but the difference between 35% and 70% would again represent
only a factor of two in the required detection sensitivity. Here I've put it
at 50%, following Patel et
al. in Physical
Approaches to Civilian Biodefense: "Protecting 100 percent of [Vital
Workers] is likely not strictly necessary because [National Critical Function]
operators likely have enough flex capacity to handle a small fraction of [Vital
Workers] being absent, but protecting more than single-digit percentages of
[Vital Workers] is likely necessary to keep [National Critical Functions]
operational. We therefore chose 50 percent of [Vital Workers] as a convenient
midrange protection target." [2] /p>
How long would it take from initial detection until effective mitigations
were in place to protect workers? For every doubling that happens during
response, we need the detection system to be twice as sensitive: since required
sensitivity grows exponentially with response lag, reducing that lag becomes
the best use of marginal dollars after a relatively small initial investment.
We've worked from a response time of ~15 days: this is not where the world is
today but we think it's achievable.
Taking this all together, you get the target that SecureBio has been working
towards for a while: a system sensitive enough to avert civilizational collapse
would need to flag a pathogen before, very roughly, 1% of
people had been infected: 1% * 215/3 = 32% < 50%.
[1] While mirror
bacteria could spread through the environment instead of between humans, to
the extent that they might still propagate widely before detection, I group
them in with stealth.
[2] This is not dependent on which specific workers are considered "vital" or
"essential" or even how many there are, though of course that has large impacts
on the question of how to get them protected.
Cross-posted from my SecureBio Notebook.
This is a lightly-edited version of a memo that I presented at the Summer 2026 Biosecurity Summit outside of DC. While others at SecureBio often see things similarly, I'm attempting to present my view and not a SecureBio "house view".
The Goal
We need to be robust to adversaries who want to cause very large-scale harm with biology. This includes actors (human or AI) who want to kill all humans, cause short-term incapacitation or long-term civilizational collapse, or who have strategies for sparing some while they harm others. There are multiple reasons an actor might have these targets aside from being directly omnicidal, such as reducing response capacity during an AI takeover.
That there is an attacker itself is a key constraint to any defensive system: it must be designed for adversarial attacks. The attacker can assess the state of the world's detection systems and plan accordingly. Taken to the extreme, this presents a "minimax" landscape: a system is only as good as its weakest link (the place where it is least sensitive). This is an important framing, and it correctly prioritizes getting some sensitivity towards a wide range of attacks over very high sensitivity towards just a few. On the other hand, (a) to the extent that gaps depend on non-public choices, you can maintain strategic ambiguity to prevent attackers from aiming for the gaps, and (b) reducing the number of gaps reduces attacker options.
The strongest form of success is to deter an attacker by denying them the ability to achieve their goal: an adversary who knows an attack wouldn't accomplish their goal will generally not try that attack. This deterrent effect is tightly coupled to the extent to which detection would indeed thwart the achievement of the attacker's goals. This means that achieving deterrence-by-denial means both building an effective system, from detection through to action, and making it known that you've built it.
The other main kind of deterrence is deterrence-by-punishment. If you develop strong attribution capabilities, an attacker risks identification and retaliation. The extent to which this would deter an adversary, however, depends a lot on what they have to lose. A state seeking strategic advantage might be deterred by the prospect of retaliation, while someone keen on killing everyone (including themselves) has little left to threaten.
Biosurveillance Applications
Where specifically does biosurveillance fit in? What are the threats, and where can it make the difference between an attacker succeeding and failing?
Initial Detection of Stealth Pandemics
A stealth pandemic is one where a pathogen spreads through most of the population unnoticed, with no or unremarkable symptoms, before causing very serious effects. [1] If it were subtle enough, people wouldn't realize how serious the situation was in time to respond effectively. While we call these "stealth pathogens", whether a given pathogen would cause a stealth pandemic depends on the interaction between the pathogen, the body, and humanity's many ways of noticing that something unusual is happening.
Whether it is possible to create a pathogen that would be sufficiently difficult to notice is an open question: I've heard different things from different experts. When considering (a) the significant advances in biological design tools and general biological understanding that we've been seeing with AI progress, and (b) the speed and unpredictability of the process by which unusual symptoms today lead to attention and action, I do think there's a significant chance that within the next five years many actors would be in a position to cause stealth pandemics.
Detection of suspicious sequencing reads may not be sufficient to estimate whether they represent an ongoing stealth pandemic. A pathogen may be constructed in a way that makes its potential for rapid spread and delayed harm obvious, but that is far from guaranteed. Assessing this likely requires additional scientific work: genome completion, estimating likely effects in the human body, and considering whether the genome suggests an intentional attack.
Triggering Initial Response
A pathogen doesn't have to be stealthy to be disastrous: it could simply be very hard to contain (a "wildfire pandemic"). Beyond its direct effects, such a pathogen could be intentionally released to reduce capacity at a critical time, such as during a coup or an AI takeover attempt.
Whether an outbreak is wildfire, stealth, or has aspects of both, the time from initial discovery to serious response is critical. Initial indications are generally ambiguous, and it is often difficult to understand the extent or trajectory of the threat. With the 1976 swine flu, we overreacted and vaccinated 45M people because we didn't have the monitoring to know it wasn't spreading widely. With 2014 ebola in West Africa, we underreacted and let it spread freely for three months because the extent wasn't recognized. Similarly, with 2026 ebola, we saw another three month delay, this time in part because field PCR tests couldn't see it. The case of 2009 H1N1, however, showed how a well functioning (though flu-specific) biosurveillance system could enable timely response. In today's COVID-weary climate where public health is deeply worried about losing credibility through false alarms, biosurveillance can help avoid a default of delaying response while waiting for more information.
Enabling Ongoing Suppression
Once an initial response is in motion, you need monitoring to effectively deploy mitigations and know whether they're working. How much value there is depends on how symptoms relate to infectiousness. If symptoms are absent or highly delayed, effective response is essentially impossible without solid monitoring. At the other extreme, if symptoms are highly visible and begin immediately, monitoring is moderately valuable: you know you have a problem, but with "fog of war" you don't fully know its extent or distribution. Large-scale monitoring allows you to compare locations and track trajectories to optimize resource deployment.
I give relatively little attention to this biosurveillance application in this memo, mainly because I think it's a place where what exists today is closest to what needs to exist, so this is a lower priority for additional work.
What does success look like?
There's no single threshold, where you win by building a system with a specific level of capability. Biosurveillance systems reduce the cost-benefit tradeoff of initiating a pandemic; increasingly capable systems decrease the likelihood that an attacker deems this worth their effort and reduce the harm if they decide to attack. Still, for each application, there are some 'sweet spots' where the cost-benefit ratio is maximized.
Initial Detection of Stealth Pandemics
If you imagine the most capable system that can be deployed for a given level of investment, it will be capable of averting some fraction of expected possible stealth harm. Here's how I see it:
A system that is too slow to beat status-quo detection might still help with triggering initial response, but doesn't provide initial detection benefit.
A system that is a bit faster but doesn't give enough time to act before most people are already infected provides some benefit, especially in cases where the harm can be mitigated if it's known soon enough, but most expected harm will still occur.
A system capable enough to flag an attack in time to allow us to protect enough workers, such that (a) civilization does not collapse and (b) medical countermeasures can be developed and deployed, provides significant benefit. Attacks could still be massively disruptive.
An extremely capable system, including a very broad sampling regime, could flag outbreaks when they were still small enough to contain. At this point an attack is still costly, but the disruption is limited to the areas where the attack was seeded.
None of these are hard boundaries. There are factors that 'smear' these thresholds across many capability levels: some of this is luck (ex: who contributes to what samples), while some is uncertainty about the world that both we and an attacker would share (ex: how much shedding a given pathogen would actually produce in a large population). Here's an illustrative chart:
I've intentionally left the x-axis vague. It's not "cumulative incidence at detection", because (a) that's horizontally smeared as described above and (b) it would imply that increased capacity is downstream of sensitivity only and not other important factors like what kind of pathogens you can detect at all or how quickly you can trigger response. Instead, the x-axis represents the level of resources invested.
A system that is sufficiently capable to flag pathogens early enough to protect enough workers to avert civilizational collapse is well worth the investment; additional sensitivity beyond this is valuable, but likely substantially less cost-effective.
This means that success looks like a pathogen-agnostic system that flags attacks in time to protect enough workers to prevent civilizational collapse and buy time for pathogen-specific mitigations. How early this needs to be depends on how quickly response can happen. If effective response takes a month from detection, you need to flag before ~0.05% of people have been infected. On the other hand, if it takes two weeks, you only need to flag before ~2% of people have been infected, and if you can get it down to seven days, then even flagging at 10% cumulative infections would be enough. See appendix for more detailed reasoning.
Note that the "fast enough to beat status-quo detection" regime would be a far higher bar for wildfire than stealth. This means that, on time scales rapid enough to factor into planning, I don't expect it to be economically feasible to build a system where biosurveillance would be your first indication of a wildfire pathogen.
Triggering Initial Response
We don't know very much about what would actually get decision-makers to take sufficiently prompt action. In a stealth scenario, this is extremely challenging, since response must begin before the main symptoms manifest, but even in a wildfire scenario, there's a huge difference between a response that follows immediately from when someone identifies the first cluster vs one where it kicks off in earnest only after deaths start to become highly visible.
Success looks like a very short time, ideally under a week, from when a catastrophic pathogen is flagged until the danger has been recognized, PPE has been distributed to essential workers, biohardening has been deployed or activated, lockdowns have been instituted, and development of rapid diagnostics and other medical countermeasures has begun. These are very costly actions, in economic terms but also via anteing political capital and institutional trust. Biosurveillance can contribute by giving decision-makers the information to determine whether those costs are worth paying. This looks like clarifying the extent of spread to date, estimating trajectory, and performing initial wet lab work such as genome completion.
Beyond the technical work, response requires trust. Before someone will act on an alert from a system they need to believe that it indicates something real, and that trust needs to be built over time. Non-catastrophic detections are key, showing you can track trends that match what other evidence shows, surface matters of public health concern, and turn up real engineered 'benign positives'.
Most of the work in reducing time to response, however, is outside biosurveillance. This could include helping government agencies develop better plans for how to handle various indications, running exercises that get the actual principals to experience the feeling of making these specific calls with realistically incomplete information, or streamlining inter-agency communication so available clinical and epidemiological data gets to the right people quickly.
Enabling Ongoing Suppression
Wastewater PCR was used by Australia, New Zealand, and Singapore (down to the building level), among others, as part of their suppression strategies to guide public health response during COVID-19. At the point when transmission has been diminished to some threshold, or if an attack is identified before the pathogen has spread widely, a sensitive biosurveillance system can tell you when and where you need to focus more expensive and intrusive detection methods and interventions.
This means that a system for successfully maintaining suppression looks like a larger-scale and more fine-grained implementation of the biosurveillance component for triggering initial response. Knowing that something is spreading in ten major cities around the US might be enough to spur rapid action, but you need a much more detailed picture if you're trying to maintain ongoing suppression.
What gets us there?
Initial detection of stealth pandemics is SecureBio Detection's focus, and where I have the most developed view. I'll walk through what I think is needed for this scenario, and then discuss how this changes for other scenarios. Please don't interpret this structure as a claim about the relative likelihood of stealth scenarios!
Sampling Strategies
Municipal wastewater is a very helpful sampling modality: in most cities, sewage is processed at a small number of locations, letting you track pathogens across hundreds of thousands of people from a single easily-collected sample. Since many pathogens don't shed heavily into wastewater, however, and it's a very noisy sample type, comprehensive initial detection at a reasonable cost likely requires tracking additional sample types. SecureBio runs a nasal swab program; beyond swabs I see air, blood, aircraft wastewater, and leftover material from clinical tests (clinical lab discards) as the strongest candidates for supplemental sampling. SecureBio has looked into these strategies in some depth (initial overview, blood, aircraft wastewater, clinical discards), but there's still a lot that could be learned here.
Lab Technology
You need technology that is pathogen agnostic: if you monitor only specific pathogens, the adversary can choose ones you don't monitor. With current and near-future tech, "pathogen agnostic" means sequencing. For viruses, it is practical to concentrate particles by size, and then perform untargeted and enriched metagenomic sequencing metagenomic sequencing. This lets you do the rest of the detection in the computer, for maximum flexibility and generality, and this is what SecureBio does today.
For bacteria, I'm pessimistic about adapting this approach directly, at least with wastewater, because the genomes are much larger and there is a rich background of sewer bacteria that can't be physically separated from potentially threatening bacteria before sequencing in the same way that viruses can. How to handle this is still an open question; see below.
For mirror life, the initial stages of spread might be very hard to recognize, and an important step would be learning that mirror life was spreading at all. It's likely that a highly sensitive and relatively cheap assay could be developed, but no one has started on this yet.
Computational Technology
Metagenomic sequencing moves much of the problem of initial detection from the lab into the computer. This means massively more sequencing reads than humans could evaluate (8+ orders of magnitude), so we need a detection system that can identify which reads indicate something concerning is happening. Some reads can easily be recognized as concerning (ex: nucleic acid subsequences unique to the smallpox genome should not be in wastewater) while others require very sophisticated processing (ex: understanding the complex background well enough to flag de novo genomes with no sequence similarity to anything currently existing). The general approaches are looking for sequences with one or more of the following features:
The computational approach is very difficult given the scale of the data, uncertainty over what an attack might look like, and the need for rapid analysis. On the other hand, these are the kinds of highly computational problems where I expect AI can be very productively applied, whereas many other aspects of this system require relatively slow real-world effort.
What exists today?
Pathogen-agnostic biosurveillance is still in its early stages. I know of four systems doing untargeted metagenomic sequencing for biosurveillance today:
CASPER (SecureBio + Marc Johnson's lab at the University of Missouri and other academic partners). This is wastewater, primarily municipal, from 49 facilities representing 24 US cities. It's virus-focused, and generated with very deep short-read sequencing. Lab work happens independently at MU and SecureBio, with bioinformatics at SecureBio.
Zephyr (SecureBio + Helena Solo-Gabriele's lab at the University of Miami). This is pooled nasal swabs from Boston and Miami, collected primarily in public places, bringing in swabs from about 1,000 people weekly. It's virus-focused, and unlike CASPER uses long-read sequencing.
ANTI-DOTE (DoW + PHC + SecureBio). This is wastewater from five US military facilities, which SecureBio processes under contract from PHC. These samples go through the same lab and bioinformatic processes as CASPER samples.
mSCAPE (UKGOV). This is bronchoalveolar lavage from UK hospitals, currently including relatively few samples, which limits sensitivity. Unlike the previous three, mSCAPE uses combined viral and bacterial sequencing. These samples are sequenced to a relatively low depth with long-read sequencing, and the data supports clinical practice, public health, and biodefense.
There are also several hybrid-capture sequencing projects that might detect an attack if the agent was similar enough to existing pathogens.
In addition to detection via pathogen-agnostic sequencing, there are also paths where an outbreak becomes visible via showing symptoms in a sufficiently large fraction of infected people. This could lead to suspicious clusters, and then to sequencing and noticing that a genome looked edited. This is not a well-developed path today, but (as discussed below) I'd like to see investment here.
What's missing?
Here's an overview of what I think most needs doing. It represents the current state of my thinking, but it's not as thoroughly considered as I wish it were: please don't overweight it in your own decision-making! While SecureBio Detection is exploring some of these, I think the ideal structure is a healthy ecosystem of organizations taking on different parts of the problem in parallel. SecureBio is often able to share samples, sequence prepared nucleic acids, or share data to help others make progress.
In roughly descending order of how valuable I estimate non-SecureBio work would be, representing a combination of both overall value and the value of the work happening independently:
[Other] Modeling. We need good estimates of the necessary system scale and ideal network design to support optimal initial detection, response triggering, and ongoing suppression. There are initial estimates, but because this is a huge question (essentially pulling the whole field together), current work is rough. Rigorous treatments would be valuable for planning, both for independent funding allocation and for governments. This is especially valuable to happen outside of SecureBio, as a way of checking our work, and doesn't need to all be executed by one group (groups can pick off subquestions).
[Comp] Red teaming. It's important to analyze existing systems to assess how well they would handle a range of adversarially designed attacks. This can be done with reference to the system implementation, or by treating the system as a black box.
[Other] Parallel orgs. SecureBio Detection has historically focused on the US. Setting up parallel orgs in other geographies, especially Europe and Asia, would be really valuable. We are happy to advise anyone interested in doing this work!
[Lab] Detection of stealth bacterial pathogens. Viral particles are small, which means that you can separate them from human and bacterial cells without specifying in advance what sequences you're interested in. Beyond this, pathogenic viruses represent enough of the total viral portion that just pulling it all into the computer to sort it out there is economical. This method can't be directly applied to bacteria, at least not in wastewater, because there is so much irrelevant bacterial genetic material. SecureBio hasn't done any work in this area yet, but some plausible approaches include aggressive depletion for things you know are not worrying, partially-targeted methods, and working with samples with a more favorable background. People have a wide range of estimates on how difficult this will be, but personally I expect it to be very hard.
[Comp] Parallel methods development. It would be great for others to be exploring other avenues in parallel. Since our main expertise is in traditional bioinformatics, I'd be especially excited to see people try other approaches, such as applying deep learning. We share much of our sequencing data publicly (PRJNA1247874, PRJNA1379685), in part to facilitate exactly this type of work.
[Lab] Sampling streams beyond wastewater and nasal swabs. Not everything sheds much into wastewater or the nose. A comprehensive system very likely needs a wider range of sample types. Blood, air, and clinical lab discards are all very promising here.
[Other] Response. Figure out what would get decision-makers to reliably act rapidly in a real emergency, and lay the groundwork so that happens. As noted above, this is probably mostly not biosurveillance. There are components (ex: developing escalation relationships) that need to be tightly coupled with biosurveillance, perhaps within SecureBio and parallel orgs, because of the limitations of information sharing. Other components (ex: policy recommendations, public outreach) make more sense as separate organizations. This is delicate work, however, and someone coming in noisily and insensitively could easily set the field back.
[Lab] Genome completion. A pathogen identified by a short-read biosurveillance system like CASPER would start as just a suspicious section of a genome. You can sometimes learn more about the genome through techniques such as outward assembly, but only if you happened to sequence the relevant reads. Lab work to assemble the whole genome lets you better understand what the pathogen would do in a human, which is likely on the critical path to both assess whether a response is warranted (and, if so, what that response should be), as well as to get people to act appropriately quickly.
[Comp] Analysis tooling. How do you go from "this genome looks worrying" to a good understanding of whether it's engineered and what effect it would have in humans?
[Other] Swab collection. Currently, Zephyr requires field samplers standing on street corners and interacting with the public, and a team brings in ~40 samples per hour. If instead workplaces or schools could be convinced to integrate sample provision into daily routines, this could be far more scalable.
[Other] Clinical sequencing. Sequencing of clinical samples will likely eventually be deployed broadly based on its clinical benefits alone, displacing a wide range of pathogen-specific tests, but by default, this displacement process is far too slow. Sequencing needs to be a standard option doctors can easily reach for when someone presents with unusual symptoms. I think the tech is ready, or could be ready with a small amount of R&D, and so this is a commercial opportunity.
[Lab] Sensitivity increases. Untargeted metagenomic sequencing for pathogen-agnostic initial detection is an early-stage field, with relatively few people exploring it, and so on priors I expect there are large sensitivity improvements waiting to be discovered.
[Comp] Deterrence tracking. You can ask models to estimate the probability of an attack achieving its goals. If models report low probability to defenders, they probably report low probability to attackers. The best models I have access to can't do a good job at this in response to a simple prompt, and if you need a series of prompts, you can't expect your answer to reflect what an attacker would get. I expect this to change quickly, however, and it would be good to have an automatically-updated tracker showing the range of answers LLMs give to the question of whether existing detection systems are sufficient to make an attack not worth an attacker's while. This would require some thought on which threat models to poll for and how to phrase the question in a way that is a good proxy for what an attacker would ask. It also closely ties into red teaming work.
[Lab] Partially targeted methods. Hybrid capture, tiled degenerate amplicon arrays, and other methods of mismatch-tolerant sequence-based enrichment could offer far higher sensitivity. While they're unlikely to be sufficiently general to address all threats, there is a good chance that it makes sense for a mature detection system to include a partially-targeted component as a way to effectively exclude large areas of the threat landscape.
[Lab] Cheaper protocols. Sequencing is still an expensive proposition, but as sequencing has become cheaper the operation of the sequencing machine is no longer the largest cost on a per-sample basis. Bringing those other costs down would allow more scale for a given budget.
[Lab] Shorter lab time. Current metagenomics protocols require about a day on the bench and about a day on the sequencer. There are already strong pressures for reducing sequencer run time, and new machines are coming out in the ~8 hour range, but there's a lot of work that could be done to speed up the rest of the processing.
[Lab] Pathogens outside of bacteria and viruses: fungi (and oomycetes), parasites, and prions. These are generally much more difficult for an attacker, especially for strategies that involve rapid spread.
[Lab] Mirror life. Current sequencing wouldn't detect mirror life at all. Municipal wastewater samples would be a good place to look, which allows you to re-use much of the collection infrastructure, but would require an assay developed specifically for mirror life. I have this low because I currently expect mirror life to take long enough to develop that there will be time later for assay development.
How does this change for accelerating response to a wildfire pandemic?
During the COVID-19 pandemic, many groups built out PCR-based targeted wastewater monitoring. In the US, wastewater monitoring networks include NWSS (CDC), WastewaterSCAN (philanthropic), and Biobot (private). EU member states, Canada, Australia, and other countries track wastewater as a standard part of their public health systems. There are maybe two dozen countries that have some form of wastewater PCR that could be retargeted to track a new pathogen in an emergency once its genome was known.
On the other hand, none of this would move quickly enough today to address a wildfire pandemic. There are delays throughout the process: some of this is technical (ex: stocking consumables), but most of it is organizational (ex: policies that permit setting production work aside, overtime budgeting, on-calls, deals with synthesis providers for rush orders). Even in a serious emergency, I think ten days is a good best-case estimate today. With good preparation, however, three days is possible. I think getting these existing networks to prepare for rapid turnaround emergency response is really valuable, and SecureBio has started to have some of these conversations.
This is also a place where the same metagenomic sequencing system you would build for stealth pandemic detection could help you cut off additional days in your response. The technical and organizational delays that slow down PCR-based detection are downstream from how changing targets requires making changes in the physical world. Untargeted sequencing lets you skip those steps, at the cost of much lower sensitivity. On the other hand, once you know what you're looking for, you can use approaches (like PCR) that are significantly more sensitive in the case of SCV2, by a factor of about 100. If you've built a sequencing system that can flag a stealth pandemic before 1% of people have been infected, then that factor of ~100 means it would be able to confirm a wildfire pandemic at ~0.01%. Still, it's not clear to me that even 0.01% is early enough. It's possible that you need 0.001% or even lower, at which point this argues either for a substantially larger investment in untargeted sequencing (to get enough data quickly) or giving up on sequencing for this application (because it can't economically reach the target sensitivity).
If you wanted to firmly decide whether to go with MGS or PCR to accelerate initial response to a wildfire pandemic the key thing you'd need would be estimates of (a) what fraction of the population would likely be infected when a wildfire pandemic was noticed, and then (b) how many doubling periods there would be before decision-makers took action in the absence of this system. On the other hand, I'm not sure a firm decision is needed, and instead lean towards different groups exploring these approaches in parallel.
How does this change for ongoing suppression?
The same PCR-based targeted wastewater monitoring that was built for COVID-19 and could potentially accelerate response to a wildfire pandemic could also be applied to ongoing monitoring to support suppression. This is already widely understood to be valuable, but there is still less investment here than there should be. In a legitimate emergency, I expect governments to be able to organize existing capacity and deploy it reasonably well, but not as quickly as would be ideal. My bigger worry is whether that existing capacity would be large enough. For example, you would ideally have monitoring at the neighborhood or building level, which means you'd need a very large number of in-manhole composite samplers. Since these are low-volume products built by a small number of manufacturers, it would be hard to build more quickly during a crisis.
The main work is building up capacity in advance that can be quickly deployed in an emergency. The best bet for such capacity is systems installed for ongoing public health monitoring: this ensures that they work, including as part of a larger system, and that lots of people know how they work. It also gives some ongoing benefit, which may make it an easier sell than stockpiling.
Appendix: Initial Detection Scale
There is a long chain of reasoning in estimating in what fraction of attacks a system would achieve the goal of protecting enough workers, and that chain involves several steps where our knowledge is limited. Still, we can make the best estimates we can. The three key parameters, are:
How quickly would the pathogen double as it spread through the population? This is a combination of the basic reproduction number and generation time. We've generally worked from a doubling period of ~3 days, which represents the high end for naturally occurring pathogens. You could argue that it should be lower, because a pathogen could be designed to spread much more quickly than existing pathogens, or higher, because of physical limits on how quickly a pathogen can spread without attracting attention.
What fraction of workers would need to be protected to allow the development of medical countermeasures and avert collapse? This is also not something we've focused on, but the difference between 35% and 70% would again represent only a factor of two in the required detection sensitivity. Here I've put it at 50%, following Patel et al. in Physical Approaches to Civilian Biodefense: "Protecting 100 percent of [Vital Workers] is likely not strictly necessary because [National Critical Function] operators likely have enough flex capacity to handle a small fraction of [Vital Workers] being absent, but protecting more than single-digit percentages of [Vital Workers] is likely necessary to keep [National Critical Functions] operational. We therefore chose 50 percent of [Vital Workers] as a convenient midrange protection target." [2] /p>
How long would it take from initial detection until effective mitigations were in place to protect workers? For every doubling that happens during response, we need the detection system to be twice as sensitive: since required sensitivity grows exponentially with response lag, reducing that lag becomes the best use of marginal dollars after a relatively small initial investment. We've worked from a response time of ~15 days: this is not where the world is today but we think it's achievable.
Taking this all together, you get the target that SecureBio has been working towards for a while: a system sensitive enough to avert civilizational collapse would need to flag a pathogen before, very roughly, 1% of people had been infected: 1% * 215/3 = 32% < 50%.
[1] While mirror bacteria could spread through the environment instead of between humans, to the extent that they might still propagate widely before detection, I group them in with stealth.
[2] This is not dependent on which specific workers are considered "vital" or "essential" or even how many there are, though of course that has large impacts on the question of how to get them protected.
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