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Salehi Amiri A, Babaei A, Simic V, Tirkolaee EB. A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach. PeerJ Comput Sci 2024; 10:e2321. [PMID: 39314704 PMCID: PMC11419658 DOI: 10.7717/peerj-cs.2321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 08/21/2024] [Indexed: 09/25/2024]
Abstract
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.
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Affiliation(s)
| | - Ardavan Babaei
- Department of Industrial Engineering, Istinye University, Istanbul, Turkey
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia
- Department of Computer Science and Engineering, Korea University, Seul, Republic of Korea
| | - Erfan Babaee Tirkolaee
- Department of Industrial Engineering, Istinye University, Istanbul, Turkey
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan
- Department of Mechanics and Mathematics, Western Caspian University, Baku, Azerbaijan
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Batoure Bamana A, Shafiee Kamalabad M, Oberski DL. A systematic literature review of time series methods applied to epidemic prediction. INFORMATICS IN MEDICINE UNLOCKED 2024; 50:101571. [DOI: 10.1016/j.imu.2024.101571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2025] Open
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Duan J, Li H, Ma X, Zhang H, Lasky R, Monaghan CK, Chaudhuri S, Usvyat L, Gu M, Guo W, Kotanko P, Wang Y. Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data. FRONTIERS IN NEPHROLOGY 2023; 3:1179342. [PMID: 37675373 PMCID: PMC10479652 DOI: 10.3389/fneph.2023.1179342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/28/2023] [Indexed: 09/08/2023]
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.
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Affiliation(s)
- Juntao Duan
- Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
| | - Hanmo Li
- Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
| | - Xiaoran Ma
- Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
| | - Hanjie Zhang
- Renal Research Institute, New York NY, United States
| | - Rachel Lasky
- Fresenius Medical Care, Global Medical Office, Waltham, MA, United States
| | | | - Sheetal Chaudhuri
- Fresenius Medical Care, Global Medical Office, Waltham, MA, United States
- Division of Nephrology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Len A. Usvyat
- Fresenius Medical Care, Global Medical Office, Waltham, MA, United States
| | - Mengyang Gu
- Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
| | - Wensheng Guo
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia PA, United States
| | - Peter Kotanko
- Renal Research Institute, New York NY, United States
- Icahn School of Medicine at Mount Sinai, New York NY, United States
| | - Yuedong Wang
- Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
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Saleem F, AL-Ghamdi ASALM, Alassafi MO, AlGhamdi SA. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5099. [PMID: 35564493 PMCID: PMC9099605 DOI: 10.3390/ijerph19095099] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 01/27/2023]
Abstract
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.
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Affiliation(s)
- Farrukh Saleem
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Abdullah Saad AL-Malaise AL-Ghamdi
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Madini O. Alassafi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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