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    A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach
    (PeerJ Inc., 2024) Amiri, Amirreza Salehi; Babaei, Ardavan; Simic, Vladimir; Tirkolaee, Erfan Babaee
    The global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern’s unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country’s performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape. Copyright 2024 Salehi Amiri et al. Distributed under Creative Commons CC-BY 4.0
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    Country-level assessment of COVID-19 performance: A cluster-based MACONT-CRITIC analysis
    (Elsevier ltd, 2025) Amiri, Amirreza Salehi; Babaei, Ardavan; Khedmati, Majid
    COVID-19, a highly contagious respiratory virus, emerged in Wuhan in December 2019, leading to a global health emergency and subsequent pandemic declaration. Despite preventive measures, millions have been diagnosed and millions more have lost their lives, highlighting the urgent need for efficient diagnostics and effective interventions. This study presents a comprehensive framework based on integrated machine learningdecision making (ML-MCDM) to assess and compare the performance of countries during the COVID-19. The aim is to evaluate the performance of countries and identify the effective strategies for controlling the pandemic. The framework introduces a new criterion entitled 'Resilience' which aims to assess a country's capability to address peak diseases by identifying the occurrence of peaks and calculating the duration between the peak and the return to a normal state. Then, it employs K-Means clustering to group countries based on their performance indicators. The countries are then ranked within each cluster using the CRITIC-MACONT framework. The present study introduces a novel approach by integrating MACONT and CRITIC methodologies, marking the first instance of such integration. Additionally, the incorporation of machine learning techniques enhances their proficiency in effectively ranking the alternatives. The results of the analysis, conducted until March 2023, using the COVID-19 dataset, demonstrate that four clusters effectively evaluate the performance of countries and, the 'Resilience' criterion emerges as the most significant among the evaluated criteria. Based on the results, the proposed framework effectively ranks the countries and provides valuable insights for pandemic control strategies.