About: SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions

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About: SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions
Creator	Agarwal, Mudit Arora, Mehak Behera, Priyamadhaba Berry, Parul Bharadiya, Vishwesh Gupta, Mohak Mazumder, Archisman Lohiya, Ayush Parameswaran, Giridara Rao, Aditi Shewade, Deepak
Source	Medline; PMC
abstract	Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age,gender distribution and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of active COVID-19 patients after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India, but will be unable to prevent the spike in number of cases.
has issue date	2020-04-30(xsd:dateTime)
bibo:doi	10.12688/f1000research.23496.1
bibo:pmid	32528664
has license	cc-by
sha1sum (hex)	1d246527a9d9a6d31c516b3c10ba0ff1d73b2eb7
schema:url	https://doi.org/10.12688/f1000research.23496.1
resource representing a document's title	SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions
has PubMed Central identifier	PMC7262570
has PubMed identifier	32528664
schema:publication	F1000Res
resource representing a document's body	covid:1d246527a9d9a6d31c516b3c10ba0ff1d73b2eb7#body_text
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