Accidental lab–acquired infections (LAIs) risk releasing potential pandemic pathogens (PPPs) from BSL–3/4 facilities. We constructed a stochastic network infectious disease model to simulate how the probability of an outbreak of a pathogen resembling wild–type SARS–COV–2, following an initial LAI would be influenced by test–and–isolate interventions over a 100–day horizon. We varied test frequency (0–7 tests/week), peak sensitivity (50–100%), and isolation delay (0–3 days). For each of 192 parameter combinations, we conducted 1,000 simulations and used logistic regression to quantify how each parameter influenced the likelihood of an outbreak of 50 or more infections. Results indicated that even relatively infrequent routine testing significantly reduced the risk of outbreaks under diverse plausible scenarios, with greater reductions achieved at higher test frequencies. Once-weekly testing reduced outbreak risk by 52% under optimistic assumptions (80% sensitivity, 1–day delay) and by 29% under pessimistic assumptions (50% sensitivity, 2–day delay). Testing two and five times weekly yielded risk reductions of up to 62% and 71%, respectively, under optimistic assumptions, and 43% and 55%, respectively, under pessimistic assumptions. Logistic regression showed each additional weekly test decreased outbreak odds by 20%, each 10–point increase in test sensitivity reduced odds by 10%, and each additional isolation delay day increased odds by 15.5%. Interaction analyses revealed that longer isolation delays attenuated the protective effects of higher testing frequency and sensitivity. Routine lab worker screening with prompt isolation substantially mitigates PPP escape risks. High–frequency testing has the greatest impact, and policymakers should consider implementing regular screening protocols.

The authors have declared no competing interest.

This study did not receive any funding.

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