Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

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dc.authorscopusid57215931449
dc.authorscopusid55778396800
dc.authorscopusid23396453200
dc.contributor.authorAyyildiz, E.
dc.contributor.authorErdogan, M.
dc.contributor.authorTaskin, A.
dc.date.accessioned2021-12-01T18:38:51Z
dc.date.available2021-12-01T18:38:51Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThis study introduces a forecasting model to help design an effective blood supply chain mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people recovered from COVID-19 is forecasted using the Artificial Neural Networks (ANNs) to determine potential donors for convalescent (immune) plasma (CIP) treatment of COVID-19. This is performed explicitly to show the applicability of ANNs in forecasting the daily number of patients recovered from COVID-19. Second, the ANNs-based approach is further applied to the data from Italy to confirm its robustness in other geographical contexts. Finally, to evaluate its forecasting accuracy, the proposed Multi-Layer Perceptron (MLP) approach is compared with other traditional models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), and Nonlinear Autoregressive Network with Exogenous Inputs (NARX). Compared to the ARIMA, LSTM, and NARX, the MLP-based model is found to perform better in forecasting the number of people recovered from COVID-19. Overall, the findings suggest that the proposed model is robust and can be widely applied in other parts of the world in forecasting the patients recovered from COVID-19. © 2021 Elsevier Ltden_US
dc.description.sponsorshipNo funds, grants, or other support was received.en_US
dc.identifier.doi10.1016/j.compbiomed.2021.105029
dc.identifier.issn00104825
dc.identifier.scopus2-s2.0-85119076008en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2021.105029
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9878
dc.identifier.volume139en_US
dc.identifier.wosWOS:000734617400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectBlood supply chainen_US
dc.subjectCIP Therapyen_US
dc.subjectCOVID-19en_US
dc.subjectForecastingen_US
dc.titleForecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chainen_US
dc.typeArticleen_US

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