| Literature DB >> 35318793 |
Anna C Fagre1,2, Lily E Cohen3, Evan A Eskew4, Max Farrell5, Emma Glennon6, Maxwell B Joseph7, Hannah K Frank8, Sadie J Ryan9,10,11, Colin J Carlson12,13, Gregory F Albery14.
Abstract
The SARS-CoV-2 pandemic has led to increased concern over transmission of pathogens from humans to animals, and its potential to threaten conservation and public health. To assess this threat, we reviewed published evidence of human-to-wildlife transmission events, with a focus on how such events could threaten animal and human health. We identified 97 verified examples, involving a wide range of pathogens; however, reported hosts were mostly non-human primates or large, long-lived captive animals. Relatively few documented examples resulted in morbidity and mortality, and very few led to maintenance of a human pathogen in a new reservoir or subsequent "secondary spillover" back into humans. We discuss limitations in the literature surrounding these phenomena, including strong evidence of sampling bias towards non-human primates and human-proximate mammals and the possibility of systematic bias against reporting human parasites in wildlife, both of which limit our ability to assess the risk of human-to-wildlife pathogen transmission. We outline how researchers can collect experimental and observational evidence that will expand our capacity for risk assessment for human-to-wildlife pathogen transmission.Entities:
Keywords: SARS-CoV-2; Zooanthroponosis; conservation; multi-host pathogen; spillback; spillover; zoonosis
Mesh:
Year: 2022 PMID: 35318793 PMCID: PMC9313783 DOI: 10.1111/ele.14003
Source DB: PubMed Journal: Ecol Lett ISSN: 1461-023X Impact factor: 11.274
FIGURE 1Pathways detailing the two general scenarios in which pathogen transmission from humans to wildlife is problematic. We separate these scenarios into concerns for 1) conservation and 2) public health. In both pathways, spillover into humans (a) results in successful zoonotic establishment; humans, which are competent transmitters of the pathogen, then transmit it onto a new animal population in an instance of “spillback” (b or d). In Pathway 1, the pathogen causes morbidity or mortality, potentially presenting a conservation risk (c). In Pathway 2, the new animal species proves a competent reservoir (d), maintaining the pathogen within the population. This population presents a spillover risk, potentially creating numerous novel infections in humans (e). This maintenance population, or additional wildlife species it shares pathogens with, may also suffer substantial morbidity and mortality, creating concerns for conservation (c). NB the sequence of events represents an idealised and simplified time‐structured scenario, comprising only one of a number of possible transmission paths through a complex metapopulation of hosts; transmission may occur in either direction between the depicted hosts, and the suggested hosts may represent a single species or a community or reservoir complex composed of multiple species. Silhouettes are taken from phylopic.org
FIGURE 2Options for investigating and understanding human‐to‐wildlife pathogen spillback. Although there is currently a limited evidence base on spillback (a–c), further investigation in the future may allow the formation of spillback datasets and the training of predictive models
Studies reviewed are summarised (Table 1a), and further stratified based on primate‐only studies (Table 1b) and non‐primate studies (Table 1c). Study counts for Table 1b and c do not equal the total count in Table 1a because four studies described pathogens present in both primate and non‐primate populations, and thus, were counted in both Table 1b and c. The fourth column, “Comparative study”, describes articles in which both free‐ranging and captive animals were sampled for human‐sourced pathogens
| (a) All studies describing spillback ( | |||||
|---|---|---|---|---|---|
| Captive | Free‐ranging | Habituated (primates) | Comparative study | Total | |
| Healthy (or not discussed) | 7 | 10 | 14 | 5 | 36 |
| Morbidity ± mortality | 37 | 11 | 10 | 3 | 61 |
| Total | 44 | 21 | 24 | 8 | 97 |
FIGURE 3Asymmetry in transmission between humans and wildlife. Many studies that investigate animals’ susceptibility to human pathogens rest on the implicit assumption that interspecific pathogen transmission is symmetrical—that is, that pathogens go through the same series of hurdles in transmitting from humans to animals as they do in the reverse direction. One of the greatest unknowns concerning zooanthroponotic transmission is its symmetry: that is, do the same processes govern transmission from humans to animals as those governing animal‐to‐human transmission? A great many processes could create asymmetry in this relationship. For example, host immune cells often use cell surface proteins such as glycans to identify self from non‐self; when one species encounters a virus that has just budded off another species’ cells, its immune response may be able to more easily identify the glycans of the other species, and the propensity to identify other species’ glycans may not be equally effective in both directions. Several mechanisms act on humans specifically—most notably non‐pharmaceutical interventions (e.g. disinfectant, face masks, bednets, etc)—to lower incidence and transmission of infectious disease, both between humans and to/from animals. We suggest that the symmetry of transmission between humans and wildlife is likely to depend on the pathogen's transmission mode. For example, humans may be less likely to inhabit areas that involve concentrated animal waste, whereas a great many animal species are subjected to human sewage or runoff, exposing them more readily to human faecal‐oral pathogens (a). Similarly, it is unlikely that humans will bite wild animals, but relatively more likely that the opposite will happen; consequently humans are regularly exposed to rabies from animals, but the reverse is not true (b). Relatively few humans are eaten by animals, but many humans eat animals, which provides a well‐established spillover route for pathogen transmission during handling, slaughter, and consumption (c). However, there are some transmission modes that are likely to be more‐or‐less symmetrical—most notably vector‐borne transmission, provided the arthropod does not have narrow host‐feeding preferences, and feeds on both humans and non‐human vertebrates (d). Animal silhouettes are from phylopic.org
In addition to in vivo experiments to characterise susceptibility of novel hosts, analytical tools (e.g. comparative genomics, structural modelling for receptor binding, and trait‐based machine learning) are being used to predict the potential host range of SARS‐CoV‐2
| Species | Zoonotic compatibility score (Fischhoff) | Damas | Citation | |
|---|---|---|---|---|
| Experimental infection | Chinese hamster ( | 0.756 | High | Bertzbach et al. ( |
| Black‐tailed prairie dog | 0.200 | Not listed | Bosco‐Lauth et al. ( | |
| Red fox ( | 0.880 | Low | Porter et al. ( | |
| Natural infection | Binturong ( | 0.796 | Not listed | Animal Plant and Health Inspection Service, United States Department of Agriculture ( |
| Fishing cat ( | 0.680 | Not listed | ||
| Spotted hyena ( | 0.892 | Not listed | Animal Plant and Health Inspection Service, United States Department of Agriculture ( | |
| Hippopotamus ( | 0.442 | Medium | British Broadcasting Corporation ( | |
| Lynx ( | 0.223 | Medium | Animal Plant and Health Inspection Service, United States Department of Agriculture ( | |
| Coati ( | 0.384 | Not listed | Animal Plant and Health Inspection Service, United States Department of Agriculture ( |
Featured is a list of wildlife species for which empirical evidence of susceptibility has been demonstrated, either through natural infection or experimental inoculation, with predicted susceptibilities as determined by structural analysis of ACE2 alone (Damas et al., 2020) and with the addition of ecological and life history traits as predictors (Fischhoff et al., 2021). Following a procedure described in Becker et al. for evaluating the performance of a classifier model when the validation data can only meaningfully be presence‐only (Becker et al., 2022), we evaluated the area under the training prevalence‐test sensitivity curve (AUC‐TPTSC). We found that the model performed slightly better than random (AUC = 0.637). Code to reproduce the analysis is available at github.com/viralemergence/fischhoff‐validation.