Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review
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Abstract
Coronaviruses are an extensive family of viruses that can cause disease in both animals and humans. The current classification of coronaviruses recognizes 39 species in 27 subgenera that belong to the family Coronaviridae. From those, at least 7 coronaviruses are known to cause respiratory infections in humans. Four of these viruses can cause common cold-like symptoms. Those that infect animals can evolve and become infectious to humans. Three recent examples of these viral jumps include SARS CoV, MERS-CoV and SARS CoV-2 virus. They are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently discovered coronavirus disease during 2019 (COVID-19). COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. The rapid spread of the disease has taken the scientific and medical community by surprise. Latest figures from 20 May 2020 show more than 5 million people had been infected with the virus, causing more than 330,000 deaths in over 210 countries worldwide. The large amount of information received daily relating to COVID-19 is so abundant and dynamic that medical staff, health authorities, academics and the media are not able to keep up with this new pandemic. In order to offer a clear insight of the extensive literature available, we have conducted a comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19).
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Key risk factors associated with severe COVID-19 include advanced age (>80 years), diabetes, obesity, male gender, and hypertension.
“Key risk factors associated with severe COVID-19 include advanced age (>80 years), diabetes, obesity, male gender, and hypertension (8–12).”
From Lung epithelial and endothelial damage, loss of tissue repair, inhibition of fibrinolysis, and cellular senescence in fatal COVID-19 (2021) · cites this paper as [10]
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COVID-19: consider cytokine storm syndromes and immunosuppression
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A serological assay to detect SARS-CoV-2 seroconversion in humans
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- Authors
- Ortiz-Prado, E., Simbaña-Rivera, K., Gómez- Barreno, L., Rubio-Neira, M., Guaman, L. P., Kyriakidis, N. C., Muslin, C., Jaramillo, A. M. G., Barba-Ostria, C., Cevallos-Robalino, D., Sanches-SanMiguel, H., Unigarro, L., Zalakeviciute, R., Gadian, N., & López-Cortés, A.
- Affiliations
- Pediatric Hospital "Baca Ortiz" (EC)
- Universidad de Las Américas (EC)
- Hospital General del Sur de Quito, IESS (EC)
- SOLCA Núcleo de Quito (EC)
- University of Southampton (GB)
- Venue
- Diagnostic Microbiology and Infectious Disease
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 98
- Issue
- 1
- Pages
- 115094
- ISSN
- 0732-8893 (ISSN-L)
- PMC
- PMC7260568
- OpenAlex
- W3017016822
- Cited by
- 458
- References
- 248
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 36 · 2023: 72 · 2022: 108 · 2021: 151 · 2020: 65
- FWCI
- 4.68
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Diagnostic Microbiology and Infectious Disease (publishedVersion)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- SARS-CoV-2, COVID-19 pandemic, coronavirus, Coronaviridae, SARS, MERS, zoonotic transmission, respiratory infections, pandemic, clinical characterization, molecular characterization
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Humans, Pneumonia, Viral, Virus Replication, HIV Infections, Genome, Viral, Coronavirus Infections, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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