A Deck Full of Aces

• Source: A Deck Full of AcesPublisher: The Skeptical Scientist | Author: Bob Morris, MD, PhDPublished: March 20, 2026 | Archived: July 18, 2026
• The Skeptical Scientist
• A Deck Full of Aces
  • The Trick Required to Make a Lab Leak Plausible
  • Adding it Up
  • What does “1 in 100 billion” actually mean?

§Source: A Deck Full of Aces
Publisher: The Skeptical Scientist | Author: Bob Morris, MD, PhD
Published: March 20, 2026 | Archived: July 18, 2026

§The Skeptical Scientist

Skepticism lies at the core of the scientific method. This blog applies that lens to current science and its dissemination seeking to distill available research, expose misinformation and bad science, and reimagine science communication.

§A Deck Full of Aces

§The Trick Required to Make a Lab Leak Plausible

[

<img src=“https://substackcdn.com/image/fetch/\(s_!P1OZ!,w_36,h_36,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F4851d0f1-bf30-4115-95a4-eafa2a4b661c_2136x3216.jpeg" alt="Bob Morris, MD, PhD's avatar" width="36" height="36" style="display:inline-block;vertical-align:middle;border-radius:50%"> ](https://substack.com/@rdmorris) ### A Lab Leak? Let’s play the odds. [In an earlier piece](https://open.substack.com/pub/drbobmorris/p/ten-trillion-mice-and-the-jenga-tower?r=c5emj&utm_campaign=post&utm_medium=web), I laid out five events that would need to happen for SARS-CoV-2 to have originated in the Wuhan Institute of Virology. That post generated an extended [discussion](https://drbobmorris.substack.com/p/ten-trillion-mice-and-the-jenga-tower/comments?utm_source=substack&utm_medium=web&utm_campaign=post_viewer), which I encourage you to read. In response to that discussion, I have expanded that list to six events to reflect the fact that a laboratory accident is a distinct event from the absence of a community cluster of cases after such an event. 1. **From among thousands of wildlife viruses collected**, one specific SARS-CoV-2–like virus, showing *no prior evidence of human infectivity*, was selected for laboratory research. 2. **Despite selecting it to study**, researchers made an explicit decision *not* to deposit the virus’ sequence in GenBank. 3. **When the virus failed to replicate in standard systems**, researchers somehow identified its target receptor and engineered mice to sustain infection. 4. **A laboratory accident infected a worker**. In fact lab leak proponents suggest there were three infected workers. 5. **There was no identified community cluster for any of the infected workers** and the virus spread unnoticed until a large outbreak appeared around the Wuhan wildlife market. 6. **No physical, documentary, or genetic trace** of this virus or of the research conducted on it was ever [discovered](https://www.who.int/publications/m/item/independent-assessment-of-the-origins-of-sars-cov-2-from-the-scientific-advisory-group-for-the-origins-of-novel-pathogens). So what are the chances of each event occurring individually, and what is the cumulative probability of all the events happening? ### **The Six Required Events (with Conservative Probabilities)** ##### **Event 1: A specific SARS-CoV-2–like virus was selected for intensive study Assigned probability: 1%** Prior to 2020, the Wuhan Institute of Virology and collaborators collected [on the order of 15,000 bat samples](https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1006698), roughly 5-10% (750-1,500) of which were PCR positive for bat-CoV RNA. Of these, only a small fraction were ever advanced to full genomes, live virus isolation, or deep functional work. Reviews indicate that only a handful of bat SARS-related coronaviruses (e.g., WIV1, WIV16, Rs4874) were successfully cultured and studied intensively. Assigning a 1% probability that any one specific virus would be selected for this level of work is therefore deliberately conservative. ##### **Event 2: The virus was studied but its sequence was never published or deposited** **Assigned probability: 1%** Viruses taken to intensive study—particularly those involving full genome sequencing, culture, or functional assays—are almost invariably reported and deposited in public databases. Sequence publication is central to scientific priority, funding justification, and career advancement. While delays or incomplete reporting can occur, the probability that a virus undergoing such work would leave no public sequence record is extremely low. Notably, all of the beta-CoV, SARSr-CoV sequences from WIVs sampling were collated and submitted for publication in late 2018, and sequences submitted to GenBank. If there were a good reason not to publish the sequence of a newly discovered virus, GenBank wouldn’t exist. Assigning 1% is a conservative upper bound. ##### **Event 3: Researchers identified receptor usage and developed a workable model system despite no prior evidence of human infectivity** **Assigned probability: 1%.** Pre-2020 work by WIV and collaborators did include ACE2-centered functional testing, but only for a narrow subset of selected SARS-related viruses. However, [such work](https://www.nature.com/articles/nm.3985) was concentrated on a few prioritized candidates. The SARS-CoV-2 genome is less than an 80% match **to SARS-CoV-1**, and would not obviously stand out based on sequence similarity alone. Assigning 1% for this combined set of technical successes is conservative. ##### **Event 4: A laboratory accident infected a worker** **Assigned probability: 1%.** Laboratory-acquired infections are [rare](https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2014.00116/full) in modern biosafety settings. Estimates suggest rates on the order of **[1 infection per 1,000 to 10,000](https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2014.00116/full) laboratory worker-years**. Even allowing for repeated handling of a specific virus by multiple workers, the probability that such work would result in infection remains low. Assigning 1% is therefore a conservative upper bound. Three infected laboratory workers would either require a major contamination event affecting three workers at once or three independent events, the probability of which is one in a million (1% x 1% x 1%). ##### **Event 5: The infection failed to generate a recognized community cluster before the Wuhan outbreak** **Assigned probability: 10% (single worker); 0.1% (three workers).** [Early estimates](https://www.mdpi.com/1660-4601/19/18/11613) place the basic reproduction number of SARS-CoV-2 at approximately **R₀ ≈ 2.5–3**. In a simple branching process, the probability that transmission chains die out without sustained spread is roughly **6–11%** for a single introduction. Using **10%** is therefore conservative. If three independent workers were infected, the probability that all three chains would fail to establish sustained transmission drops to approximately **0.02–0.13%**, conservatively rounded to **0.1%**. These estimates are likely conservative for a dense urban environment, where transmission opportunities are greater. ##### **Event 6: No physical, documentary, funding, or genetic trace of the virus or research was ever discovered** **Assigned probability: 1%.** Intensive laboratory work of this kind would normally generate multiple independent records, including grant documentation, sequence data, laboratory logs, and publications, often involving multiple collaborators. The complete absence of any such trace would therefore require not merely non-publication, but effective non-disclosure across all of these systems. Assigning 1% is a deliberately conservative assumption. s_!Tz8W!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffdd976b0-70d1-4d4b-9387-fb2c73117810_1352x1572.png)

§Adding it Up

Using these conservative values:

§**Single-worker scenario:

**

0.01×0.01×0.01×0.01×0.10×0.01=10-11

→ ~1 in 100 billion

§Three-worker scenario:

0.01×0.01×0.01×0.01×0.10×0.01=10-14

→ ~1 in 100 trillion

These are not formal posterior probabilities, but they are useful as a plausibility check: even with generous assumptions at every step, the full chain becomes extraordinarily unlikely.

§What does “1 in 100 billion” actually mean?

Imagine I hand you a deck of cards and ask you to shuffle it as thoroughly as you possibly can. Before I touch the deck, I say: I’m going to draw the ace of spades.

You finish shuffling. I reach in, pull a card—and it is the ace of spades.

That would already feel extraordinary.

Now I hand the deck back to you and ask you to shuffle again. Take your time. Mix it as much as you like. Again, before I pick, I say: ace of spades.

I draw. It’s the ace of spades.

At this point you’re no longer thinking “lucky.” Something feels off.

But I keep going.

Shuffle again. I call it. I draw it.

And again.

And again.

Seven times in a row I draw exactly the same card from a fully shuffled deck.

That is the scale of a one-in-100-billion event.

And if you prefer the version of the story with three infected workers, then I do it again. Twice.

At some point, you stop trying to calculate the odds. You stop saying “that’s unlikely.” You conclude that this is not a fair game.

The only way this works is if the deck is not what it appears to be—if every card in it is the ace of spades.

The lab-leak argument works the same way. Each step, taken alone, is made to seem plausible, even if unlikely. But for the full chain to hold, every uncertainty needs to resolve in the same direction, again and again.

The assertion requires a deck in which every card is a lab leak.