Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/99621
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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorSong, Hen_US
dc.creatorFan, Gen_US
dc.creatorZhao, Sen_US
dc.creatorLi, Hen_US
dc.creatorHuang, Qen_US
dc.creatorHe, Den_US
dc.date.accessioned2023-07-18T03:11:43Z-
dc.date.available2023-07-18T03:11:43Z-
dc.identifier.issn1547-1063en_US
dc.identifier.urihttp://hdl.handle.net/10397/99621-
dc.language.isoenen_US
dc.publisherAmerican Institute of Mathematical Sciencesen_US
dc.rights© 2021 the Author(s), licensee AIMS Press. T hi s is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)en_US
dc.rightsThe following publication Haitao Song, Guihong Fan, Shi Zhao, Huaichen Li, Qihua Huang, Daihai He. Forecast of the COVID-19 trend in India: A simple modelling approach[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9775-9786 is available at https://doi.org/10.3934/mbe.2021479.en_US
dc.subjectCOVID 19en_US
dc.subjectIndiaen_US
dc.subjectMathematical modellingen_US
dc.subjectIterated filteringen_US
dc.subjectForecasten_US
dc.titleForecast of the COVID-19 trend in India : a simple modelling approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage9775en_US
dc.identifier.epage9786en_US
dc.identifier.volume18en_US
dc.identifier.issue6en_US
dc.identifier.doi10.3934/mbe.2021479en_US
dcterms.abstractBy February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. Multiple factors (e.g., Delta variants with increased transmissibility) could have driven the rapid growth of the epidemic in India and led to a large number of deaths within a short period. We aim to reconstruct the transmission rate, estimate the infection fatality rate and forecast the epidemic size. We download the reported COVID-19 mortality data in India and formulate a simple mathematical model with a flexible transmission rate. We use iterated filtering to fit our model to deaths data. We forecast the infection attack rate in a month ahead. Our model simulation matched the reported deaths well and is reasonably close to the results of the serological study. We forecast that the infection attack rate (IAR) could have reached 43% by July 24, 2021, under the current trend. Our estimated infection fatality rate is about 0.07%. Under the current trend, the IAR will likely reach a level of 43% by July 24, 2021. Our estimated infection fatality rate appears unusually low, which could be due to a low case to infection ratio reported in previous study. Our approach is readily applicable in other countries and with other types of data (e.g., excess deaths).en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationMathematical biosciences and engineering, 2021, v. 18, no. 6, p. 9775-9786en_US
dcterms.isPartOfMathematical biosciences and engineeringen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85118995771-
dc.identifier.pmid34814368-
dc.identifier.eissn1551-0018en_US
dc.description.validate202307 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextProgram for the Outstanding Innovative Teams (OIT) of Higher Learning Institutions of Shanxi; Scientific and Technological Innovation Programs; National Natural Science Foundation of China; Shanxi Scholarship Council of China; Shanxi Provincial Key Research and Development Project; Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Provinceen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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