dc.creatorTomar, Anuradha
dc.creatorGupta, Neeraj
dc.date.accessioned2020-07-13T20:27:50Z
dc.date.accessioned2022-09-23T18:34:54Z
dc.date.available2020-07-13T20:27:50Z
dc.date.available2022-09-23T18:34:54Z
dc.date.created2020-07-13T20:27:50Z
dc.identifier0048-9697
dc.identifierhttps://doi.org/10.1016/j.scitotenv.2020.138762
dc.identifierhttp://hdl.handle.net/20.500.12010/10465
dc.identifierhttps://doi.org/10.1016/j.scitotenv.2020.138762
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3503560
dc.description.abstractThe spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals, some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.
dc.publisherScience Direct
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourcereponame:Expeditio Repositorio Institucional UJTL
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozano
dc.subjectCOVID-19
dc.subjectRecurrent neural network
dc.subjectLSTM
dc.subjectCurve fitting
dc.subjectPrediction
dc.titlePrediction for the spread of COVID-19 in India and effectiveness of preventive measures


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