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

AYYILDIZ E., Erdogan M., TAŞKIN A.

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.139, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 139
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.compbiomed.2021.105029
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, Library, Information Science & Technology Abstracts (LISTA), MEDLINE
  • Anahtar Kelimeler: Artificial neural networks, CIP Therapy, COVID-19, Forecasting, Blood supply chain, CORRELATION-COEFFICIENT, TIME-SERIES, PREDICTION, DEMAND, OUTBREAK, PREVALENCE, PERCEPTRON, ARIMA, ROOT, FUEL
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This 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.