About: Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models
Creator	Asirvatham, Edwin Jeyaseelan, L Joy, Melvin Malavika, B Marimuthu, S Nadaraj, Ambily Sam, Edwin
Source	Elsevier; Medline; PMC
abstract	BACKGROUND: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and select high-incidence states; and evaluate the impact of three weeks lock down period using different models. METHODS: We used Logistic growth curve model for short term prediction; SIR models to forecast the cumulative, maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. RESULTS: The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020 As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of 149 daily new cases after the lock down period which is statistically not significant. CONCLUSION: The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.
has issue date	2020-06-27(xsd:dateTime)
bibo:doi	10.1016/j.cegh.2020.06.006
bibo:pmid	32838058
has license	no-cc
sha1sum (hex)	5c31ad02f6f10c75877c3066f1fecee9fe867dcc
schema:url	https://doi.org/10.1016/j.cegh.2020.06.006
resource representing a document's title	Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models
has PubMed Central identifier	PMC7319934
has PubMed identifier	32838058
schema:publication	Clin Epidemiol Glob Health
resource representing a document's body	covid:5c31ad02f6f10c75877c3066f1fecee9fe867dcc#body_text
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