About: Modelling SARS-CoV-2 Dynamics: Implications for Therapy

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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	Modelling SARS-CoV-2 Dynamics: Implications for Therapy
Creator	Ejima, Keisuke Nakaoka, Shinji Watashi, Koichi Asai, Yusuke Kim, Kwang Iwami, Shingo Ito, Yusuke Iwanami, Shoya Koizumi, Yoshiki Ohashi, Hirofumi Thompson,
Source	MedRxiv
abstract	The scientific community is focussed on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data collected from the same specimen (throat / nasal swabs or nasopharyngeal / sputum / tracheal aspirate), we compare within-host dynamics for patients infected in the current outbreak with analogous dynamics for MERS-CoV and SARS-CoV infections. Our quantitative analyses revealed that SARS-CoV-2 infection dynamics are more severe than those for mild cases of MERS-CoV, but are similar to severe cases, and that the viral dynamics of SARS-CoV infection are similar to those of MERS-CoV in mild cases but not in severe case. Consequently, SARS-CoV-2 generates infection dynamics that are more severe than SARS-CoV. Furthermore, we used our viral dynamics model to predict the effectiveness of unlicensed drugs that have different methods of action. The effectiveness was measured by AUC of viral load. Our results indicated that therapies that block de novo infections or virus production are most likely to be effective if initiated before the peak viral load (which occurs around three days after symptom onset on average), but therapies that promote cytotoxicity are likely to have only limited effects. Our unique mathematical approach provides insights into the pathogenesis of SARS-CoV-2 in humans, which are useful for development of antiviral therapies.
has issue date	2020-03-27(xsd:dateTime)
bibo:doi	10.1101/2020.03.23.20040493
has license	medrxiv
sha1sum (hex)	a64c4974c979487512a016eb09b1b4e0c8e5571e
schema:url	https://doi.org/10.1101/2020.03.23.20040493
resource representing a document's title	Modelling SARS-CoV-2 Dynamics: Implications for Therapy
resource representing a document's body	covid:a64c4974c979487512a016eb09b1b4e0c8e5571e#body_text
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