Ayyildiz, E.Erdogan, M.Taskin, A.2021-12-012021-12-01202100104825https://doi.org/10.1016/j.compbiomed.2021.105029https://hdl.handle.net/20.500.12684/9878This 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 Ltden10.1016/j.compbiomed.2021.105029info:eu-repo/semantics/closedAccessArtificial neural networksBlood supply chainCIP TherapyCOVID-19ForecastingArticle1392-s2.0-85119076008WOS:000734617400001Q1Q1