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Cited by 703

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis

Open access
Badawi, Alaa, Ryoo, Seung Gwan·2016·International Journal of Infectious Diseases

Abstract

The Middle East respiratory syndrome coronavirus (MERS-CoV) is associated with life-threatening severe illnesses and a mortality rate of approximately 35%, particularly in patients with underlying comorbidities. A systematic analysis of 637 MERS-CoV cases suggests that diabetes and hypertension are equally prevalent in approximately 50% of the patients. Cardiac diseases are present in 30% and obesity in 16% of the cases. These conditions down-regulate the synthesis of proinflammatory cytokines and impair the host's innate and humoral immune systems. In conclusion, protection against MERS-CoV and other respiratory infections can be improved if public health vaccination strategies are tailored to target persons with chronic disorders.

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Crossref · 10.1016/j.ijid.2016.06.015

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Metadata

Authors
Badawi, A., & Ryoo, S. G.
Venue
International Journal of Infectious Diseases
Publisher
Elsevier BV
Published
2016-08
Volume
49
Pages
129-133
ISSN
1201-9712 (ISSN-L)
OpenAlex
W2465777822
Cited by
703
References
3
FWCI
1.17
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. https://app.cochat.ai/papers/10.1016/j.ijid.2016.06.015
The paper
Badawi, A., & Ryoo, S. G. (2016). Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. International Journal of Infectious Diseases. https://doi.org/10.1016/j.ijid.2016.06.015

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.