About: Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization

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About: Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization
Creator	Teeling, Emma Carlson, Colin Becker, Daniel Han, Barbara Simmons, Nancy Sjodin, Anna Dallas, Tad Albery, Gregory Farrell, Maxwell Poisot, Timothée Guth, Sarah Eskew, A Stock,
Source	BioRxiv
abstract	Despite massive investment in research on reservoirs of emerging pathogens, it remains difficult to rapidly identify the wildlife origins of novel zoonotic viruses. Viral surveillance is costly but rarely optimized using model-guided prioritization strategies, and predictions from a single model may be highly uncertain. Here, we generate an ensemble of eight network- and trait-based statistical models that predict mammal-virus associations, and we use model predictions to develop a set of priority recommendations for sampling potential bat reservoirs and intermediate hosts for SARS-CoV-2 and related betacoronaviruses. We find over 200 bat species globally could be undetected hosts of betacoronaviruses. Although over a dozen species of Asian horseshoe bats (Rhinolophus spp.) are known to harbor SARS-like coronaviruses, we find at least two thirds of betacoronavirus reservoirs in this bat genus might still be undetected. Although identification of other probable mammal reservoirs is likely beyond existing predictive capacity, some of our findings are surprisingly plausible; for example, several civet and pangolin species were highlighted as high-priority species for viral sampling. Our results should not be over-interpreted as novel information about the plausibility or likelihood of SARS-CoV-2’s ultimate origin, but rather these predictions could help guide sampling for novel potentially zoonotic viruses; immunological research to characterize key receptors (e.g., ACE2) and identify mechanisms of viral tolerance; and experimental infections to quantify competence of suspected host species.
has issue date	2020-06-25(xsd:dateTime)
bibo:doi	10.1101/2020.05.22.111344
has license	biorxiv
sha1sum (hex)	4806b77bcfb79ddc9388aa2a9049529c2b584fef
schema:url	https://doi.org/10.1101/2020.05.22.111344
resource representing a document's title	Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization
schema:publication	bioRxiv
resource representing a document's body	covid:4806b77bcfb79ddc9388aa2a9049529c2b584fef#body_text
is schema:about of	named entity 'betacoronaviruses' covid:arg/4806b77bcfb79ddc9388aa2a9049529c2b584fef named entity 'Phylogeographic' named entity 'Vespertilionidae' named entity 'Hipposideridae' named entity 'Erinaceomorpha' named entity 'species diversity' named entity 'BART' named entity 'China' named entity 'BART' named entity 'betacoronaviruses' named entity 'mammal' named entity 'bat' named entity 'infection' named entity 'viruses' named entity 'Figure 8' named entity 'bat species' named entity 'Pholidota' named entity 'betacoronaviruses' named entity 'mouse deer' named entity 'Indiana University' named entity 'cetaceans' named entity 'longitudinal sampling' named entity 'China' named entity 'morphological' named entity 'phylogeography' named entity 'Kernel Ridge Regression' named entity 'host species' named entity 'Rhinolophus affinis' named entity 'mammal' named entity 'human coronaviruses' named entity 'Rhinolophus ferrumequinum' named entity 'probability' named entity 'Figure 8' named entity 'genus' named entity 'Rhinolophus' named entity 'bat' named entity 'probability' named entity 'nucleotide' named entity 'pathogen' named entity 'mustelids' named entity 'Myanmar' named entity 'Genbank' named entity 'SARS-CoV-2' named entity 'Bat' named entity 'BART' named entity 'cetaceans' named entity 'betacoronavirus' named entity 'viruses' named entity 'betacoronaviruses' named entity 'PubMed' named entity 'SARS-CoV-2' named entity 'index case' named entity 'pathogens' named entity 'bat' named entity 'betacoronavirus' named entity 'k-nearest neighbor' named entity 'overfitting' named entity 'gene' named entity 'linear filter' named entity 'viruses' named entity 'polymerase' named entity 'mammals' named entity 'viruses' named entity 'reservoir species' named entity 'phylogeny' named entity 'bat' named entity 'proofreading' named entity 'Rhinolophus' named entity 'bat' named entity 'Asia' named entity 'contact tracing'

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