Journal article
Who should we fear more: biohackers, disgruntled postdocs, or bad governments? a simple risk chain model of biorisk
- Abstract:
- The biological risk landscape continues to evolve as developments in synthetic biology and biotechnology offer increasingly powerful tools to a widening pool of actors, including those who may consider carrying out a deliberate biological attack. However, it remains unclear whether it is the relatively large numbers of low-resourced actors or the small handful of high-powered actors who pose a greater biosecurity risk. To answer this question, this paper introduces a simple risk chain model of biorisk, from actor intent to a biological event, where the actor can successfully pass through each of N steps. Assuming that actor success probability at each independent step is sigmoidally distributed and actor power follows a power-law distribution, if a biorisk event were to occur, this model shows that the expected perpetrator would likely be highly powered, despite lower-powered actors being far more numerous. However, as the number of necessary steps leading to a biological release scenario decreases, lower-powered actors can quickly overtake more powerful actors as the likely source of a given event. If steps in the risk chain are of unequal difficulty, this model shows that actors are primarily limited by the most difficult step. These results have implications for biosecurity risk assessment and health security strengthening initiatives and highlight the need to consider actor power and ensure that the steps leading to a biorisk event are sufficiently difficult and not easily bypassed.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 354.7KB, Terms of use)
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- Publisher copy:
- 10.1089/hs.2019.0115
Authors
- Publisher:
- Mary Ann Liebert
- Journal:
- Health Security More from this journal
- Volume:
- 18
- Issue:
- 3
- Pages:
- 155-163
- Publication date:
- 2020-06-10
- DOI:
- EISSN:
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2326-5108
- ISSN:
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2326-5094
- Pmid:
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32522112
- Language:
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English
- Keywords:
- Pubs id:
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1113644
- Local pid:
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pubs:1113644
- Deposit date:
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2020-06-30
- ARK identifier:
Terms of use
- Copyright holder:
- Mary Ann Leibert
- Copyright date:
- 2020
- Rights statement:
- © Mary Ann Liebert, Inc. This article is made open access via creative commons licensing.
- Licence:
- CC Attribution (CC BY)
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