About: Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models 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
title	Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models
Creator	Kumar, Pavan Singh, Kumar Akshaya, Srikanth Bhagavathula, Bai, Rani Chintan Nanda, Mr Datt Sharma, Yagya Kalita, Himangshu Kalita, Mr Meenu, Mrs Nanda, Chintan Patairiya, Mrs Patairiya, Shashikanta Rani, Meenu Sharma, Datt Srikanth Bhagavathula, Akshaya Singh, Ram Yagya, Mr Ram, Mr
source	MedRxiv
abstract	The ongoing pandemic of the coronavirus disease 2019 (COVID-19) started in China and devastated a vast majority of countries. In India, COVID-19 cases are steadily increasing since January 30, 2020, and the government-imposed lockdown across the country to curtail community transmission. COVID-19 forecasts have played an important role in capturing the probability of infection and the basic reproduction rate. In this study, we predicted some trajectories of trajectories associated with COVID-19 in the coming days in India using an Auto-regression integrated moving average model (ARIMA) and Richards model. By the end of April 2020, the incidence of new cases is predicted to be 5200 (95% CI: 4650 to 6002) through the ARIMA model versus be 6378 (95% CI: 4904 to 7851) Richard model. We estimated that there would be a total of 197 (95% CI: 118 to 277) deaths and drop down in the recovery rates will reach around 501 (95% CI: 245 to 758) by the end of April 2020. These estimates can help to strengthen the implementation of strategies to increase the health system capacity and enactment of social distancing measures all over India.
has issue date	2020-04-02(xsd:dateTime)
bibo:doi	10.1101/2020.03.30.20047803
has license	medrxiv
sha1sum (hex)	66bc90ad788ec702b6d3e930016b56747af81c7c
schema:url	https://doi.org/10.1101/2020.03.30.20047803
resource representing a document's title	Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models
resource representing a document's body	covid:66bc90ad788ec702b6d3e930016b56747af81c7c#body_text
is schema:about of	named entity 'enactment' named entity 'played' named entity 'forecasts' named entity 'infection' named entity 'majority' named entity 'trajectories' named entity 'model' named entity '95% CI' named entity 'India' named entity 'China' named entity 'ARIMA' named entity 'social distancing measures' named entity 'Autoregression' named entity 'coming days' named entity 'April 2020' named entity 'probability' named entity 'India' named entity 'ongoing' named entity 'nonlinear regression' named entity 'Cumulative incidence' named entity 'SIR models' named entity 'doi' named entity 'coronavirus disease 2019' named entity 'medRxiv' named entity 'preprint' named entity 'preprint' named entity 'Time Series' named entity 'file format' named entity 'medRxiv' named entity 'preprint' named entity 'medRxiv' named entity 'ARIMA' named entity 'medRxiv' named entity 'correlation' named entity 'fastly' named entity 'exponential smoothing' named entity 'time series' named entity 'R-language' named entity 'peer-reviewed' named entity 'preprint' named entity 'preprint' named entity 'public health' named entity 'COVID-19' named entity 'COVID-19' named entity 'medRxiv' named entity 'preprint' named entity 'India' named entity 'COVID-19' named entity 'preprint' named entity 'COVID-19 outbreak' named entity 'epidemic' named entity 'India' named entity 'partial autocorrelation function' named entity 'ARIMA' named entity 'Coefficient of determination' named entity 'ARIMA' named entity 'growth model' named entity 'preprint' named entity 'ARIMA' named entity 'preprint' named entity 'COVID-19' named entity 'algorithm' named entity 'COVID-19' named entity 'nonlinear model' named entity 'sigmoidal' named entity 'peer-reviewed' named entity 'point of inflection'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software