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Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

Abeer AlSereidi

Faculty of Engineering & IT, The British university in Dubia, United Arab Emirates

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Sarah Qahtan M. Salih

Department of Computer Center, Middle Technical University’s, Baghdad, Iraq

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R. T. Mohammed

https://orcid.org/0000-0001-7187-052X

Department of Computing Science, College of Science, Komar University of Science and Technology (KUST), Sulaymaniyah, Iraq

E-mail Address: [email protected]

Corresponding author.

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A. A. Zaidan

Faculty of Engineering & IT, The British university in Dubia, United Arab Emirates

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Hassan Albayati

Department of Business Administration, College of Administrative Science, The University of Mashreq, 10021 Baghdad, Iraq

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

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Dragan Pamucar

University of Defence in Belgrade, Department of Logistic, Pavla Jurisica Sturma 33, 11000 Belgrade, Serbia

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A. S. Albahri

Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

University of Information Technology and Communications (UOITC), Baghdad, Iraq

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B. B. Zaidan

Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

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Khaled Shaalan

Faculty of Engineering & IT, The British university in Dubia, United Arab Emirates

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Jameel Al-Obaidi

Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia

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O. S. Albahri

Computer Techniques Engineering Department Mazaya University College, Thi-Qar, Nassiriya, Iraq

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Abdulah Alamoodi

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

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Nazia Abdul Majid

Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

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Salem Garfan

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

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M. S. Al-Samarraay

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

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A. N. Jasim

Foundation of Alshuhda, Baghdad, Iraq

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, and

M. J. Baqer

Foundation of Alshuhda, Baghdad, Iraq

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https://doi.org/10.1142/S021962202250050XCited by:17 (Source: Crossref)

Abstract

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.

Keywords:

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