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Cited by 685

The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.

COVID-19: Epidemiology, Evolution, and Cross-Disciplinary Perspectives

Open access
Sun, Jiumeng, He, Wan-Ting, Wang, Lifang, Lai, Alexander, Ji, Xiang, Zhai, Xiaofeng, Li, Gairu, Suchard, Marc A., Tian, Jin, Zhou, Jiyong, Veit, Michael, Su, Shuo·2020·Trends in Molecular Medicine

Abstract

The recent outbreak of COVID-19 in Wuhan turned into a public health emergency of international concern. With no antiviral drugs nor vaccines, and the presence of carriers without obvious symptoms, traditional public health intervention measures are significantly less effective. Here, we report the epidemiological and virological characteristics of the COVID-19 outbreak. Originated in bats, 2019-nCoV/ severe acute respiratory syndrome coronavirus (SARS-CoV)-2 likely experienced adaptive evolution in intermediate hosts before transfer to humans at a concentrated source of transmission. Similarities of receptor sequence binding to 2019-nCoV between humans and animals suggest a low species barrier for transmission of the virus to farm animals. We propose, based on the One Health model, that veterinarians and animal specialists should be involved in a cross-disciplinary collaboration in the fight against this epidemic.

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Crossref · 10.1016/j.molmed.2020.02.008

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Metadata

Authors
Sun, J., He, W.-T., Wang, L., Lai, A., Ji, X., Zhai, X., Li, G., Suchard, M. A., Tian, J., Zhou, J., Veit, M., & Su, S.
Venue
Trends in Molecular Medicine
Publisher
Elsevier BV
Published
2020-05
Volume
26
Issue
5
Pages
483-495
ISSN
1471-4914 (ISSN-L)
OpenAlex
W3012841297
Cited by
685
References
88
FWCI
8.72
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: COVID-19: Epidemiology, Evolution, and Cross-Disciplinary Perspectives. https://app.cochat.ai/papers/10.1016/j.molmed.2020.02.008
The paper
Sun, J., He, W.-T., Wang, L., Lai, A., Ji, X., Zhai, X., Li, G., Suchard, M. A., Tian, J., Zhou, J., Veit, M., & Su, S. (2020). COVID-19: Epidemiology, Evolution, and Cross-Disciplinary Perspectives. Trends in Molecular Medicine. https://doi.org/10.1016/j.molmed.2020.02.008

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.