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dc.contributor.authorKumar, S
dc.contributor.authorSharma, R
dc.contributor.authorTsunoda, T
dc.contributor.authorKumarevel, T
dc.contributor.authorSharma, A
dc.date.accessioned2021-06-22T04:55:05Z
dc.date.available2021-06-22T04:55:05Z
dc.date.issued2021
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-021-04224-2
dc.identifier.urihttp://hdl.handle.net/10072/405273
dc.description.abstractBackground: The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. Results: The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand’s data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. Conclusion: The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofpagefrom316
dc.relation.ispartofissueSuppl 6
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume22
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.subject.keywordsCOVID-19
dc.subject.keywordsEnd date prediction
dc.subject.keywordsLong short-term memory (LSTM)
dc.subject.keywordsPandemic
dc.titleForecasting the spread of COVID-19 using LSTM network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKumar, S; Sharma, R; Tsunoda, T; Kumarevel, T; Sharma, A, Forecasting the spread of COVID-19 using LSTM network, BMC Bioinformatics, 2021, 22 (Suppl 6), pp. 316
dcterms.dateAccepted2021-06-01
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-06-22T01:46:27Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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