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. 2021 Apr 20;19(4):e3001135.
doi: 10.1371/journal.pbio.3001135. eCollection 2021 Apr.

How accurately can we assess zoonotic risk?

Michelle Wille  1 , Jemma L Geoghegan  2   3 , Edward C Holmes  1
Affiliations

How accurately can we assess zoonotic risk?

Michelle Wille et al. PLoS Biol. .

Abstract

Identifying the animal reservoirs from which zoonotic viruses will likely emerge is central to understanding the determinants of disease emergence. Accordingly, there has been an increase in studies attempting zoonotic "risk assessment." Herein, we demonstrate that the virological data on which these analyses are conducted are incomplete, biased, and rapidly changing with ongoing virus discovery. Together, these shortcomings suggest that attempts to assess zoonotic risk using available virological data are likely to be inaccurate and largely only identify those host taxa that have been studied most extensively. We suggest that virus surveillance at the human-animal interface may be more productive.

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Conflict of interest statement

The authors have declared that no competing interests exist

Figures

Fig 1
Fig 1. Change in the sampling of viral diversity in 3 host taxa and 1 virus family.
Data are divided into 3 categories: (i) viruses described in 2008 regardless of ICTV status; (ii) viruses described in 2020 regardless of ICTV status; and (iii) viruses described in 2020 that have also been ratified as virus species by the ICTV. The first column shows raw counts of the number of viruses, with total virus counts shown above each bar. The second column shows the proportion of described viruses that are known to cause disease (from minor morbidity to mortality) in these hosts. The final column shows the proportion of viruses found in these taxa that have infected humans, identified through either virological or serological techniques (note that no fish viruses have been found to infect humans). Points are the proportional estimate (out of 100%), and bars are 95% confidence intervals. Statistics are shown in each plot, and p-values for post hoc tests are also shown. Animal silhouettes are from phylopic.org and distributed under a creative commons attribution. The virus silhouette was generated by M. Wille. The source code and underlying data for this figure can be found at https://github.com/jemmageoghegan/Assessing-zoonotic-risk.
Fig 2
Fig 2. Recent increases in documented virus discovery.
Bars indicate the number of new viruses described per year. Line represents the cumulative number of viruses described. All viruses described before 1995 have been presented as a single bar and thus the year is indicated as “<1995.” The dashed line indicates 2008, the year the Jones and colleagues paper [1] was published, and our cutoff for Fig 1. Animal silhouettes are from phylopic.org and distributed under a creative commons attribution. The virus silhouette was generated by M. Wille. The source code and underlying data for this figure can be found at https://github.com/jemmageoghegan/Assessing-zoonotic-risk.
Fig 3
Fig 3. Disparate publication frequency between orthomyxoviruses that cause disease in humans or domestic animals (i.e., influenza A virus and influenza B virus) and those found in underappreciated vertebrate and invertebrate hosts.
The source code and underlying data for this figure can be found at https://github.com/jemmageoghegan/Assessing-zoonotic-risk.
See this image and copyright information in PMC

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Grants and funding

E.C.H. is funded by an Australian Research Council Australian Laureate Fellowship (FL170100022). M.W. is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200100977). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.