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

Ayyıldız, Ertuğrul; Erdoğan, Melike; Gümüş, Alev Taşkın

doi:10.1016/j.compbiomed.2021.105029

Cited by 22 publications

(15 citation statements)

References 111 publications

(107 reference statements)

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“…Comparamos os resultados do modelo proposto com os obtidos por modelos clássicos e recentemente apresentados na literatura: i) modelos estatísticos (autoregressivo integrado de médias móveis -ARIMA [42], arvores de regressão -REGTREE [43] e regressão de vetor de suporte -SVR [44]), ii) modelos de redes neurais (perceptron multicamadas -MLP [45], func ¸ão de base radial -RBF [46], memória de curto e longo prazo -LSTM [47], convolucional -CNN [48]), e iii) modelos dinâmicos (rede neural autoregressiva não linear com entradas exógenas -NARX [49]).…”

Section: Simulac ¸õEs E Resultados Experimentaisunclassified

“…Para os experimentos com os modelos MLP, RBF e NARX, empregamos os procedimentos apresentados em [45], [46] e [49], respectivamente, para determinar seus parâmetros, utilizando a neural networks toolbox do MATLAB. Para os experimentos com os modelos LSTM e CNN, empregamos o procedimento descrito em [47] e [48], respectivamente, para determinar seus parâmetros, utilizando a biblioteca keras do Python.…”

Section: Simulac ¸õEs E Resultados Experimentaisunclassified

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Sobre o Problema de Previsão da Covid-19 Utilizando Modelos Morfológico-Linear Profundos

Araújo¹

2021

Learn. Nonlin. Mod.

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The coronavirus disease 2019 (COVID-19) has been declared by the World Health Organization (WHO) as an unprecedented pandemic in the present days, straining healthcare systems due to the high demand for admissions to intensive care units. In this context, estimating the dynamics of the COVID19 pandemic is essential to deal with health system drawbacks. Therefore, in this work we developed an empirical study on time series related to the COVID-19 pandemic and, based on this study, we present a deep morphological-linear model, trained by a gradient-based learning process, able to predict this particular kind of time series. Trying to assess the predictive performance of the proposed model, we use daily COVID-19 time series in Brazil and United States of America. The achieved results show that the proposed model outperforms classical and recent machine learning models to estimate the dynamics of the COVID-19 pandemic.

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Section: Simulac ¸õEs E Resultados Experimentaisunclassified

Sobre o Problema de Previsão da Covid-19 Utilizando Modelos Morfológico-Linear Profundos

Araújo¹

2021

Learn. Nonlin. Mod.

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“…Before being allocated, blood units are cross-matched against a sample of blood from the named patient to avoid transfusion reactions and to confirm compatibility. Once crossmatched, units from the assigned inventory are reserved for the specific patient for between 24 (Katsaliaki Korina, 2014)'s contribution to theory of the Blood Supply Chain Game is the game's ability to facilitate students and professionals to acquire knowledge of the push-pull and cycle process view of a supply chain [ i.e. push process: blood collection system and stocking both in the NBS and hospitals' banks, pull process: doctors' ordering; cycle process: information (orders) from doctors to hospital bank, etc.…”

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confidence: 99%

Bibliometric Review of Inventory Management in Blood Supply Chain: A Systematic Review

Journal¹

2022

IJSREM

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Management of blood inventories includes all procedures involved in ordering, storing, handling, and distributing blood products. To guarantee proper use of this priceless resource: Blood; effective inventory management is required. The overall objective of this study is to explore the optimization approaches in inventory management of blood supply chains. By summarizing the optimization techniques used, a framework was created, literature review was analyzed, variables and complexities affecting blood inventory performance were found and examined. As advice for upcoming researchers in this field, this paper includes the current state of knowledge, potential research areas, and gaps that are present in this field. Concerns were raised about managing the blood supply during the coronavirus disease 2019 (COVID-19) pandemic. Due to travel restrictions and concern over contracting the virus while visiting blood banks, blood donations during the pandemic significantly decreased. As a result, the purpose of this review is to analyze the research literature already in existence that deals with the modelling of blood collection, provide a detailed classification of the selected articles, and thus identify any areas that may benefit for further research. KEYWORDS- Inventory management, Blood, Supply chain, Blood banks, Platelets

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“…After the COVID-19 epidemic, ANN models were used to construct various simulation models. Ayyildiz et al [40] proposed a prediction model to facilitate in the creation of an effective COVID-19 blood supply chain mechanism. In this study, firstly, the number of individuals recovering from COVID-19 was estimated utilizing the ANN model to identify potential defensive plasma donor’s therapy of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

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

Cited by 22 publications

References 111 publications

Sobre o Problema de Previsão da Covid-19 Utilizando Modelos Morfológico-Linear Profundos

Sobre o Problema de Previsão da Covid-19 Utilizando Modelos Morfológico-Linear Profundos

Bibliometric Review of Inventory Management in Blood Supply Chain: A Systematic Review

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

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