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Mahanty C, Patro SGK, Rathor S, Rachapudi V, Muzammil K, Islam S, Razak A, Khan WA. Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques. Sci Rep 2024; 14:18039. [PMID: 39098877 PMCID: PMC11298557 DOI: 10.1038/s41598-024-67161-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 07/08/2024] [Indexed: 08/06/2024] Open
Abstract
Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.
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Affiliation(s)
- Chandrakanta Mahanty
- Department of Computer Science & Engineering, GITAM School of Technology, GITAM Deemed to Be University, Visakhapatnam, 530045, India
| | | | - Sandeep Rathor
- Department of CEA, GLA University, Mathura, 281406, India
| | - Venubabu Rachapudi
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India
| | - Khursheed Muzammil
- Department of Public Health, College of Applied Medical Sciences, Khamis Mushait Campus, King Khalid University, Abha, Kingdom of Saudi Arabia
| | - Saiful Islam
- Civil Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Abdul Razak
- Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru, India
| | - Wahaj Ahmad Khan
- School of Civil Engineering & Architecture, Institute of Technology, Dire-Dawa University, 1362, Dire Dawa, Ethiopia.
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Vianna LS, Gonçalves AL, Souza JA. Analysis of learning curves in predictive modeling using exponential curve fitting with an asymptotic approach. PLoS One 2024; 19:e0299811. [PMID: 38635659 PMCID: PMC11025780 DOI: 10.1371/journal.pone.0299811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/15/2024] [Indexed: 04/20/2024] Open
Abstract
The existence of large volumes of data has considerably alleviated concerns regarding the availability of sufficient data instances for machine learning experiments. Nevertheless, in certain contexts, addressing limited data availability may demand distinct strategies and efforts. Analyzing COVID-19 predictions at pandemic beginning emerged a question: how much data is needed to make reliable predictions? When does the volume of data provide a better understanding of the disease's evolution and, in turn, offer reliable forecasts? Given these questions, the objective of this study is to analyze learning curves obtained from predicting the incidence of COVID-19 in Brazilian States using ARIMA models with limited available data. To fulfill the objective, a retrospective exploration of COVID-19 incidence across the Brazilian States was performed. After the data acquisition and modeling, the model errors were assessed by employing a learning curve analysis. The asymptotic exponential curve fitting enabled the evaluation of the errors in different points, reflecting the increased available data over time. For a comprehensive understanding of the results at distinct stages of the time evolution, the average derivative of the curves and the equilibrium points were calculated, aimed to identify the convergence of the ARIMA models to a stable pattern. We observed differences in average derivatives and equilibrium values among the multiple samples. While both metrics ultimately confirmed the convergence to stability, the equilibrium points were more sensitive to changes in the models' accuracy and provided a better indication of the learning progress. The proposed method for constructing learning curves enabled consistent monitoring of prediction results, providing evidence-based understandings required for informed decision-making.
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Affiliation(s)
- Leonardo Silva Vianna
- Graduate Program in Knowledge Engineering, Management, and Media, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
| | - Alexandre Leopoldo Gonçalves
- Graduate Program in Knowledge Engineering, Management, and Media, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
| | - João Artur Souza
- Graduate Program in Knowledge Engineering, Management, and Media, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
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Shboul B, Koh SCL, Veneti C, Herghelegiu AI, Zinca AE, Pourkashanian M. Evaluating sustainable development practices in a zero‑carbon university campus: A pre and post-COVID-19 pandemic recovery study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165178. [PMID: 37392889 DOI: 10.1016/j.scitotenv.2023.165178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 06/17/2023] [Accepted: 06/25/2023] [Indexed: 07/03/2023]
Abstract
This paper aims to understand the critical areas for sustainable behavioural change on a university campus in order to achieve the net zero‑carbon ambition pre- and post-COVID-19 pandemic recovery. For this purpose, the current empirical study is the first attempt to statistically examine the whole campus as a system, considering staff and student views (campus users), by developing an index measuring propensity for sustainable behavioural change to achieve a net zero‑carbon campus. The novelty of this study is based on the following: (i) The impact of environmental sustainability measures due to COVID-19 is examined on three themes: physical activity routines on a daily basis, research, and teaching and learning, and (ii) the index that is compatible with quantifying the behavioural change. A multi-indicator questionnaire is used to collect empirical data for each of the three themes. Based on 630 responses, descriptive statistical analysis, normality tests, significance tests, and t-tests are performed using statistical and graphical software, and conducting uncertainty and sensitivity analyses on this quantitative data. The study found that 95 % of campus users agreed to use reusable materials on campus, and 74 % were willing to pay more for sustainable products. In addition, 88 % agreed to seek alternative and sustainable transportation for short research trips, while 71 % prioritised online conferences and project meetings for sustainable hybrid working. Moreover, the COVID-19 pandemic had a negative impact on the frequency of reusable material usage among campus users, as indicated by the index analysis, which showed a significant decrease from 0.8536 to 0.3921. The statistical findings show that campus users are more likely to initiate and endorse environmental sustainability measures in research and daily life than in teaching and learning, and there is no difference in their propensity for change. This research provides net zero‑carbon sustainability researchers and leaders with a crucial baseline for scientific advances in the sustainability field. It also offers practical guidelines for implementing a net zero‑carbon campus, engaging users from various disciplines, which has important implications and contributions.
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Affiliation(s)
- Bashar Shboul
- Renewable Energy Engineering Department, Faculty of Engineering, Al Al-Bayt University, Mafraq, Jordan; Energy Institute, Advanced Resource Efficiency Centre and Energy 2050, University of Sheffield, UK.
| | - S C Lenny Koh
- Energy Institute, Advanced Resource Efficiency Centre and Energy 2050, University of Sheffield, UK; Management School, University of Sheffield, Sheffield, UK.
| | - Charoula Veneti
- Sheffield Methods Institute, University of Sheffield, Sheffield, UK
| | | | | | - Mohamed Pourkashanian
- Energy Institute, Advanced Resource Efficiency Centre and Energy 2050, University of Sheffield, UK; Department of Mechanical Engineering, Faculty of Engineering, University of Sheffield, UK
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4
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Aronoff-Spencer E, Mazrouee S, Graham R, Handcock MS, Nguyen K, Nebeker C, Malekinejad M, Longhurst CA. Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting. PLoS One 2023; 18:e0287368. [PMID: 37594936 PMCID: PMC10437830 DOI: 10.1371/journal.pone.0287368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 05/29/2023] [Indexed: 08/20/2023] Open
Abstract
PURPOSE Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future. RESULTS Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
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Affiliation(s)
- Eliah Aronoff-Spencer
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Sepideh Mazrouee
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Rishi Graham
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Mark S. Handcock
- University of California Los Angeles, Los Angeles, CA, United States of America
| | - Kevin Nguyen
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
- University of California San Diego Health, San Diego, CA, United States of America
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
| | - Mohsen Malekinejad
- California Department of Public Health, Sacramento, CA, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States of America
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Talpur N, Jadid Abdulkadir S, Akashah Patah Akhir E, Hilmi Hasan M, Alhussian H, Hafizul Afifi Abdullah M. A novel bitwise arithmetic optimization algorithm for the rule base optimization of deep neuro-fuzzy system. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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6
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A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2023. [PMCID: PMC9841947 DOI: 10.1007/s40031-022-00849-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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7
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Prediction of the COVID-19 infectivity and the sustainable impact on public health under deep learning algorithm. Soft comput 2023; 27:2695-2704. [PMID: 34456617 PMCID: PMC8380005 DOI: 10.1007/s00500-021-06142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 10/24/2022]
Abstract
The aim is to explore the development trend of COVID-19 (Corona Virus Disease 2019) and predict the infectivity of 2019-nCoV (2019 Novel Coronavirus), as well as its impact on public health. First, the existing data are analyzed through data pre-processing to extract useful feature factors. Then, the LSTM (Long-Short Term Memory) prediction model in the deep learning algorithm is used to predict the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country, respectively. Meanwhile, the impact of intervention time changes on the epidemic situation is compared. The results show that the prediction results are almost consistent with the actual values. Specifically, Hubei Province abolishes quarantine restrictions after the Spring Festival holiday, and the first COVID-19 peak is reached in late February, while the second COVID-19 peak has been reached in early March. Finally, the cumulative number of diagnoses reaches 85,000 cases, with an increase of 15,000 cases compared with the nationwide cases outside Hubei under the continuous implementation of prevention and control measures. Under the prediction of the proposed LSTM model, if the nationwide implementation of prevention and control interventions is postponed by 5 days, the epidemic will peak in early March, and the cumulative number of diagnoses will be about 200,000; and if the intervention measures are implemented five days earlier, the epidemic will peak in mid-February, with a cumulative number of diagnoses of approximately 40,000. Meanwhile, the proposed LSTM model predicts the RMSE values of the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country as 34.63, 75.42, and 50.27, respectively. Under model comparison analysis, the prediction error of the proposed LSTM model is small and has better applicability over similar algorithms. The results show that the LSTM model is effective and has high performance in infectious disease prediction, and the research results can provide scientific and effective references for subsequent related research.
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An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment. KNOWL ENG REV 2023. [DOI: 10.1017/s0269888923000036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
Abstract
In this paper, we present a model of the spread of the COVID-19 pandemic simulated by a multi-agent system (MAS) based on demographic data and medical knowledge. Demographic data are linked to the distribution of the population according to age and to an index of socioeconomic fragility with regard to the elderly. Medical knowledge are related to two risk factors: age and obesity. The contributions of this approach are as follows. Firstly, the two aggravating risk factors are introduced into the MAS using fuzzy sets. Secondly, the worsening of disease caused by these risk factors is modeled by fuzzy aggregation operators. The appearance of virus variants is also introduced into the simulation through a simplified modeling of their contagiousness. Using real data from inhabitants of an island in the Antilles (Guadeloupe, FWI), we model the rate of the population at risk which could be critical cases, if neither social distancing nor barrier gestures are respected by the entire population. The results show that hospital capacities are exceeded. The results show that hospital capacities are exceeded. The socioeconomic fragility index is used to assess mortality and also shows that the number of deaths can be significant.
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9
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Broad learning system with Takagi–Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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10
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Ahuja S, Panigrahi BK, Dey N, Taneja A, Gandhi TK. McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices. Appl Soft Comput 2022; 131:109683. [PMID: 36277300 PMCID: PMC9573862 DOI: 10.1016/j.asoc.2022.109683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/25/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022]
Abstract
Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections. The proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.
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Affiliation(s)
- Sakshi Ahuja
- Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Bijaya Ketan Panigrahi
- Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Nilanjan Dey
- Department of Computer Science and Engineering, Techno International New Town, Kolkata, 700156, India
| | - Arpit Taneja
- Department of Radiology, Avtaran Healthcare LLP, Kurukshetra, 136118, India
| | - Tapan Kumar Gandhi
- Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016, India
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11
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Sinwar D, Dhaka VS, Tesfaye BA, Raghuwanshi G, Kumar A, Maakar SK, Agrawal S. Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1306664. [PMID: 36304775 PMCID: PMC9581633 DOI: 10.1155/2022/1306664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/06/2022] [Accepted: 09/27/2022] [Indexed: 01/26/2023]
Abstract
Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.
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Affiliation(s)
- Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Biniyam Alemu Tesfaye
- Department of Computer Science, College of Informatics, Bule Hora University, Bule Hora, Ethiopia
| | - Ghanshyam Raghuwanshi
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Ashish Kumar
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, India
| | - Sunil Kr. Maakar
- School of Computing Science & Engineering, Galgotias University, Greater Noida, India
| | - Sanjay Agrawal
- Department of Electrical Engineering, Rajkiya Engineering College, Akbarpur, Ambedkar Nagar, India
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12
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Dong K, Wang J, Taghizadeh-Hesary F. Assessing the embodied CO2 emissions of ICT industry and its mitigation pathways under sustainable development: A global case. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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13
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Chakraborty A, Das D, Mitra S, De D, Pal AJ. Forecasting adversities of COVID-19 waves in India using intelligent computing. INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2022:1-17. [PMID: 36186271 PMCID: PMC9512957 DOI: 10.1007/s11334-022-00486-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.
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Affiliation(s)
- Arijit Chakraborty
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Dipankar Das
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Sajal Mitra
- Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India
| | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India
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14
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Jena KK, Nayak SR, Bhoi SK, Verma KD, Prakash D, Gupta A. A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41995-42021. [PMID: 36090152 PMCID: PMC9440332 DOI: 10.1007/s11042-022-13524-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 09/22/2021] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.
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Affiliation(s)
- Kalyan Kumar Jena
- Department of Computer Science and Engineering, PMECParala Maharaja Engineering College, Berhampur, India
| | - Soumya Ranjan Nayak
- PradeshAmity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Sourav Kumar Bhoi
- Department of Computer Science and Engineering, PMECParala Maharaja Engineering College, Berhampur, India
| | - K. D. Verma
- Department of Physics, Shri Varshney (P.G.) College, Aligarh, UP 202001 India
| | - Deo Prakash
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, J&K 182320 India
| | - Abhishek Gupta
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, J&K 182320 India
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15
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Kumar S, Shastri S, Mahajan S, Singh K, Gupta S, Rani R, Mohan N, Mansotra V. LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1464-1480. [PMID: 35941931 PMCID: PMC9349394 DOI: 10.1002/ima.22770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 02/26/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.
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Affiliation(s)
- Sachin Kumar
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Sourabh Shastri
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Shilpa Mahajan
- Department of Computer Science and EngineeringNational Institute of TechnologyJalandharIndia
| | - Kuljeet Singh
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Surbhi Gupta
- Department of Electrical Engineering and Information TechnologyPunjab Agricultural UniversityLudhianaIndia
| | - Rajneesh Rani
- Department of Computer Science and EngineeringNational Institute of TechnologyJalandharIndia
| | - Neeraj Mohan
- Department of Computer Science and EngineeringIK Gujral Punjab Technical UniversityMohaliIndia
| | - Vibhakar Mansotra
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
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16
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Ashofteh A, Bravo JM, Ayuso M. An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic. Appl Soft Comput 2022; 128:109422. [PMID: 35938053 PMCID: PMC9341166 DOI: 10.1016/j.asoc.2022.109422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/20/2022] [Accepted: 07/24/2022] [Indexed: 11/25/2022]
Abstract
Quantifying and analyzing excess mortality in crises such as the ongoing COVID-19 pandemic is crucial for policymakers. Traditional measures fail to take into account differences in the level, long-term secular trends, and seasonal patterns in all-cause mortality across countries and regions. This paper develops and empirically investigates the forecasting performance of a novel, flexible and dynamic ensemble learning with a model selection strategy (DELMS) for the seasonal time series forecasting of monthly respiratory disease death data across a pool of 61 heterogeneous countries. The strategy is based on a Bayesian model averaging (BMA) of heterogeneous time series methods involving both the selection of the subset of best forecasters (model confidence set), the identification of the best holdout period for each contributed model, and the determination of optimal weights using out-of-sample predictive accuracy. A model selection strategy is also developed to remove the outlier models and to combine the models with reasonable accuracy in the ensemble. The empirical outcomes of this large set of experiments show that the accuracy of the BMA approach is significantly improved with DELMS when selecting a flexible and dynamic holdout period and removing the outlier models. Additionally, the forecasts of respiratory disease deaths for each country are highly accurate and exhibit a high correlation (94%) with COVID-19 deaths in 2020.
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17
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Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med 2022; 128:102286. [PMID: 35534142 PMCID: PMC8958821 DOI: 10.1016/j.artmed.2022.102286] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023]
Abstract
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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18
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The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic. AXIOMS 2022. [DOI: 10.3390/axioms11050242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.
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19
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A Non-Uniform Continuous Cellular Automata for Analyzing and Predicting the Spreading Patterns of COVID-19. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6020046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
During the COVID-19 outbreak, modeling the spread of infectious diseases became a challenging research topic due to its rapid spread and high mortality rate. The main objective of a standard epidemiological model is to estimate the number of infected, suspected, and recovered from the illness by mathematical modeling. This model does not capture how the disease transmits between neighboring regions through interaction. A more general framework such as Cellular Automata (CA) is required to accommodate a more complex spatial interaction within the epidemiological model. The critical issue of modeling in the spread of diseases is how to reduce the prediction error. This research aims to formulate the influence of the interaction of a neighborhood on the spreading pattern of COVID-19 using a neighborhood frame model in a Cellular-Automata (CA) approach and obtain a predictive model for the COVID-19 spread with the error reduction to improve the model. We propose a non-uniform continuous CA (N-CCA) as our contribution to demonstrate the influence of interactions on the spread of COVID-19. The model has succeeded in demonstrating the influence of the interaction between regions on the COVID-19 spread, as represented by the coefficients obtained. These coefficients result from multiple regression models. The coefficient obtained represents the population’s behavior interacting with its neighborhood in a cell and influences the number of cases that occur the next day. The evaluation of the N-CCA model is conducted by root mean square error (RMSE) for the difference in the number of cases between prediction and real cases per cell in each region. This study demonstrates that this approach improves the prediction of accuracy for 14 days in the future using data points from the past 42 days, compared to a baseline model.
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20
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Intuitionistic fuzzy inference system with weighted comprehensive evaluation considering standard deviation-cosine entropy: a fused forecasting model. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07082-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Singh R, Kumar P, Devi M, Lal S, Kumar A, Sindhu J, Toropova AP, Toropov AA, Singh D. Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors. NEW J CHEM 2022. [DOI: 10.1039/d2nj03515d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Alla P. Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A. Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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22
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Khan M, Mehran MT, Haq ZU, Ullah Z, Naqvi SR, Ihsan M, Abbass H. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. EXPERT SYSTEMS WITH APPLICATIONS 2021; 185:115695. [PMID: 34400854 PMCID: PMC8359727 DOI: 10.1016/j.eswa.2021.115695] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/14/2021] [Accepted: 07/28/2021] [Indexed: 05/06/2023]
Abstract
During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.
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Affiliation(s)
- Muzammil Khan
- School of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan
| | - Muhammad Taqi Mehran
- School of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan
| | - Zeeshan Ul Haq
- School of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan
| | - Zahid Ullah
- School of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan
| | - Salman Raza Naqvi
- School of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan
| | - Mehreen Ihsan
- Peshawar Medical College, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan
| | - Haider Abbass
- National Cyber Security Auditing and Evaluation LAb, National University of Sciences & Technology, MCS Campus, Rawalpindi 43600, Pakistan
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23
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Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA. Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy. PATTERN RECOGNITION 2021; 119:108110. [PMID: 34149100 PMCID: PMC8205562 DOI: 10.1016/j.patcog.2021.108110] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 03/09/2021] [Accepted: 05/19/2021] [Indexed: 05/19/2023]
Abstract
COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS2) and Final Selection Stage (FS2). IS2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS2, which is the next stage of APSO. FS2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty.
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Affiliation(s)
- Warda M Shaban
- Nile higher institute for engineering and technology, Egypt
| | - Asmaa H Rabie
- Computers and Control Dept. faculty of engineering Mansoura University, Egypt
| | - Ahmed I Saleh
- Computers and Control Dept. faculty of engineering Mansoura University, Egypt
| | - M A Abo-Elsoud
- Electronics and Communication Dept. faculty of engineering Mansoura University, Egypt
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24
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Cui S, Wang Y, Wang D, Sai Q, Huang Z, Cheng TCE. A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality. Appl Soft Comput 2021; 113:107946. [PMID: 34646110 PMCID: PMC8494501 DOI: 10.1016/j.asoc.2021.107946] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/05/2021] [Accepted: 09/22/2021] [Indexed: 12/12/2022]
Abstract
The COVID-19 epidemic has had a great adverse impact on the world, having taken a heavy toll, killing hundreds of thousands of people. In order to help the world better combat COVID-19 and reduce its death toll, this study focuses on the COVID-19 mortality. First, using the multiple stepwise regression analysis method, the factors from eight aspects (economy, society, climate etc.) that may affect the mortality rates of COVID-19 in various countries is examined. In addition, a two-layer nested heterogeneous ensemble learning-based prediction method that combines linear regression (LR), support vector machine (SVM), and extreme learning machine (ELM) is developed to predict the development trends of COVID-19 mortality in various countries. Based on data from 79 countries, the experiment proves that age structure (proportion of the population over 70 years old) and medical resources (number of beds) are the main factors affecting the mortality of COVID-19 in each country. In addition, it is found that the number of nucleic acid tests and climatic factors are correlated with COVID-19 mortality. At the same time, when predicting COVID-19 mortality, the proposed heterogeneous ensemble learning-based prediction method shows better prediction ability than state-of-the-art machine learning methods such as LR, SVM, ELM, random forest (RF), long short-term memory (LSTM) etc.
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Affiliation(s)
- Shaoze Cui
- School of Economics and Management, Dalian University of Technology, Dalian 116023, China
| | - Yanzhang Wang
- School of Economics and Management, Dalian University of Technology, Dalian 116023, China
| | - Dujuan Wang
- Business School, Sichuan University, Chengdu 610064, China
| | - Qian Sai
- School of Economics and Management, Dalian University of Technology, Dalian 116023, China
| | - Ziheng Huang
- Business School, Sichuan University, Chengdu 610064, China
| | - T C E Cheng
- Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
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25
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Ekinci A. Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect. CHAOS, SOLITONS, AND FRACTALS 2021; 151:111227. [PMID: 34253942 PMCID: PMC8264537 DOI: 10.1016/j.chaos.2021.111227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/24/2021] [Accepted: 06/29/2021] [Indexed: 05/25/2023]
Abstract
COVID-19 pandemic has affected more than a hundred fifty million people and killed over three million people worldwide over the past year. During this period, different forecasting models have tried to forecast time path of COVID-19 pandemic. Unlike the COVID-19 forecasting literature based on Autoregressive Integrated Moving Average (ARIMA) modelling, in this paper new COVID-19 cases were modelled and forecasted by conditional variance and asymmetric effects employing Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Threshold GARCH (TARCH) and Exponential GARCH (EGARCH) models. ARMA, ARMA-GARCH, ARMA-TGARCH and ARMA-EGARCH models were employed for one-day ahead forecasting performance for April, 2021 and three waves of COVID-19 pandemic in nine most affected countries -USA, India, Brazil, France, Russia, UK, Italy, Spain and Germany. Empirical results show that ARMA-GARCH models have better forecast performance than ARMA models by modelling both the conditional heteroskedasticity and the heavy-tailed distributions of the daily growth rate of the new confirmed cases; and asymmetric GARCH models show mixed results in terms of reducing the root mean squared error (RMSE).
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Affiliation(s)
- Aykut Ekinci
- Samsun University, Department of Economics and Finance, Samsun, Turkey
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26
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Mansour RF, Escorcia-Gutierrez J, Gamarra M, Gupta D, Castillo O, Kumar S. Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification. Pattern Recognit Lett 2021; 151:267-274. [PMID: 34566223 PMCID: PMC8455283 DOI: 10.1016/j.patrec.2021.08.018] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/24/2021] [Accepted: 08/26/2021] [Indexed: 11/25/2022]
Abstract
At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.
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Affiliation(s)
- Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - José Escorcia-Gutierrez
- Electronic and telecommunications program, Universidad Autónoma del Caribe, Barranquilla, Colombia
| | - Margarita Gamarra
- Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, Colombia
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | | | - Sachin Kumar
- Department of Computer Science, South Ural State University, Chelyabinsk, Russian Federation
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27
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Koyuncu H, Barstuğan M. COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier. SIGNAL PROCESSING. IMAGE COMMUNICATION 2021; 97:116359. [PMID: 34219966 PMCID: PMC8241421 DOI: 10.1016/j.image.2021.116359] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/12/2021] [Accepted: 06/13/2021] [Indexed: 05/17/2023]
Abstract
In medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches (Bhattacharyya, entropy, Roc, t-test, and Wilcoxon) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs. others.
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Affiliation(s)
- Hasan Koyuncu
- Konya Technical University, Faculty of Engineering and Natural Sciences, Electrical & Electronics Engineering Department, Konya, Turkey
| | - Mücahid Barstuğan
- Konya Technical University, Faculty of Engineering and Natural Sciences, Electrical & Electronics Engineering Department, Konya, Turkey
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28
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Alfaro M, Muñoz-Godoy D, Vargas M, Fuertes G, Duran C, Ternero R, Sabattin J, Gutierrez S, Karstegl N. National Health Systems and COVID-19 Death Toll Doubling Time. Front Public Health 2021; 9:669038. [PMID: 34336766 PMCID: PMC8319632 DOI: 10.3389/fpubh.2021.669038] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/07/2021] [Indexed: 01/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the "Out-of-Pocket" model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs.
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Affiliation(s)
- Miguel Alfaro
- Departamento de Ingeniería Industrial, Universidad de Santiago de Chile, Santiago, Chile
| | - Diego Muñoz-Godoy
- Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Santiago, Chile
| | - Manuel Vargas
- Departamento de Ingeniería Industrial, Universidad de Santiago de Chile, Santiago, Chile
| | - Guillermo Fuertes
- Departamento de Ingeniería Industrial, Universidad de Santiago de Chile, Santiago, Chile
- Facultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O'Higgins, Santiago, Chile
| | - Claudia Duran
- Departamento de Industria, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago, Chile
| | - Rodrigo Ternero
- Departamento de Ingeniería Industrial, Universidad de Santiago de Chile, Santiago, Chile
- Escuela de Construcción, Universidad de las Américas, Santiago, Chile
| | - Jorge Sabattin
- Facultad de Ingeniería, Universidad Andres Bello, Santiago, Chile
| | - Sebastian Gutierrez
- Facultad de Economía, Gobierno y Comunicaciones, Universidad Central de Chile, Santiago, Chile
- Facultad de Ciencias, Universidad Mayor, Chile, Santiago, Chile
| | - Natalia Karstegl
- Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Santiago, Chile
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29
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Arbulú I, Razumova M, Rey-Maquieira J, Sastre F. Measuring risks and vulnerability of tourism to the COVID-19 crisis in the context of extreme uncertainty: The case of the Balearic Islands. TOURISM MANAGEMENT PERSPECTIVES 2021; 39:100857. [PMID: 34580625 PMCID: PMC8458003 DOI: 10.1016/j.tmp.2021.100857] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/04/2021] [Accepted: 07/08/2021] [Indexed: 05/22/2023]
Abstract
The COVID-19 crisis is dramatically affecting the world economy and, particularly, the tourism sector. In the context of extreme uncertainty, the use of probabilistic forecasting models is especially suitable. We use Monte Carlo simulations to evaluate the outcomes of four possible tourism demand recovery scenarios in the Balearic Islands, which are further used to measure the risks and vulnerability of Balearic economy to the COVID-19 crisis. Our results show that fear of contagion and loss of income in tourism emitting countries will result in a maximum 89% drop in arrivals in the Balearic Islands in 2020.Given that most tourism-related occupations are not highly skilled and are characterized by lower salaries, there are greater risks of loss of welfare, especially for women, who are a major share of the tourism labour force.The model shows important differences among minimum, average and maximum estimates for tourism sector production in 2021, reflecting considerable uncertainty regarding the speed of the sector's recovery. The results serve as a basis to prepare a range of policies to reduce destination vulnerability under different crisis outcomes.
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Affiliation(s)
- Italo Arbulú
- Department of Applied Economics, University of the Balearic Islands, Cra. deValldemossa, km 7.5. Palma de Mallorca, Baleares 07122, Spain
- Department of Economics, Universidad del Pacífico, 2020 Salaverry Ave, Jesús María, Lima, Peru
| | - Maria Razumova
- Felipe Moreno University College of Tourism, University of the Balearic Islands, C/ Sol, 3. Palma de Mallorca, Balearic Islands 07001, Spain
| | - Javier Rey-Maquieira
- Department of Applied Economics, University of the Balearic Islands, Cra. deValldemossa, km 7.5. Palma de Mallorca, Baleares 07122, Spain
| | - Francesc Sastre
- Department of Applied Economics, University of the Balearic Islands, Cra. deValldemossa, km 7.5. Palma de Mallorca, Baleares 07122, Spain
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30
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Qasem SN, Mohammadzadeh A. A deep learned type-2 fuzzy neural network: Singular value decomposition approach. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107244] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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31
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Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 PMCID: PMC8545197 DOI: 10.1109/access.2021.3093633] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/02/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
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Affiliation(s)
- Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | | | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW)SydneyNSW2052Australia
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32
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Banerjee S, Lian Y. Data driven covid-19 spread prediction based on mobility and mask mandate information. APPL INTELL 2021; 52:1969-1978. [PMID: 34764603 PMCID: PMC8172182 DOI: 10.1007/s10489-021-02381-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/24/2021] [Indexed: 11/20/2022]
Abstract
COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace. The epidemiological models used in the literature have considerable drawbacks in the assumption of homogeneous mixing among the populace. Moreover, the effect of mitigation strategies such as mask mandate and stay at home orders cannot be efficiently accounted for in these models. In this work, we propose a novel data driven approach using LSTM (Long Short Term Memory) neural network model to form a functional mapping of daily new confirmed cases with mobility data which has been quantified from cell phone traffic information and mask mandate information. With this approach no pre-defined equations are used to predict the spread, no homogeneous mixing assumption is made, and the effect of mitigation strategies can be accounted for. The model learns the spread of the virus based on factual data from verified resources. A study of the number of cases for the state of New York (NY) and state of Florida (FL) in the USA are performed using the model. The model correctly predicts that with higher mobility the cases would increase and vice-versa. It further predicts the rate of new cases would see a decline if a mask mandate is administered. Both these predictions are in agreement with the opinions of leading medical and immunological experts. The model also predicts that with the mask mandate option even a higher mobility would reduce the daily cases than lower mobility without masks. We additionally generate results and provide RMSE (Root Mean Square Error) comparison with ARIMA based model of other published work for Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Our model reports lower RMSE than the ARIMA based work for all eight countries which were tested. The proposed model would provide administrations with a quantifiable basis of how mobility, mask mandates are related to new confirmed cases; so far no epidemiological models provide that information. It gives fast and relatively accurate prediction of the number of cases and would enable the administrations to make informed decisions and make plans for mitigation strategies and changes in hospital resources.
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Affiliation(s)
- Sandipan Banerjee
- Department of Mechanical Engineering, University of Louisville, Louisville, KY 40292 USA
| | - Yongsheng Lian
- Department of Mechanical Engineering, University of Louisville, Louisville, KY 40292 USA
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33
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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34
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Kao IH, Perng JW. Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning. RESULTS IN PHYSICS 2021; 25:104287. [PMID: 33996401 PMCID: PMC8105308 DOI: 10.1016/j.rinp.2021.104287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory. The epidemic data were obtained from the World Health Organization and the US Centers for Disease Control and Prevention from January 1st to April 6th, 2020. We used data from the first 366,607 confirmed cases in the United States. In this study, the data from the Centers for Disease Control and Prevention were gridded by latitude and longitude and the grids were categorized into six epidemic levels based on the number of confirmed cases. The input of the convolutional autoencoder with long short-term memory was the distribution of confirmed cases 14 days before, whereas the output was the distribution of confirmed cases 7 days after the date of testing. The mean square error in this model was 1.664, the peak signal-to-noise ratio was 55.699, and the structural similarity index was 0.99, which were better than those of the corresponding results of the convolutional autoencoder. These results showed that the convolutional autoencoder with long short-term memory effectively and reliably predicted the spread of infectious disease in the contiguous United States.
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Affiliation(s)
- I-Hsi Kao
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Jau-Woei Perng
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
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35
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Ghosh A, Roy S, Mondal H, Biswas S, Bose R. Mathematical modelling for decision making of lockdown during COVID-19. APPL INTELL 2021; 52:699-715. [PMID: 34764599 PMCID: PMC8109847 DOI: 10.1007/s10489-021-02463-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 01/12/2023]
Abstract
Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.
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Affiliation(s)
- Ahona Ghosh
- Department of Computational Science, Brainware University, Kolkata, India
| | - Sandip Roy
- Department of Computational Science, Brainware University, Kolkata, India
| | - Haraprasad Mondal
- Electronics and Communication Engineering, Dibrugarh University, Dibrugarh, Assam India
| | - Suparna Biswas
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal India
| | - Rajesh Bose
- Department of Computational Science, Brainware University, Kolkata, India
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36
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Abstract
Predicting the number of COVID-19 cases in a geographical area is important for the management of health resources and decision making. Several methods have been proposed for COVID-19 case predictions but they have important limitations in terms of model interpretability, related to COVID-19's incubation period and major trends of disease transmission. To be able to explain prediction results in terms of incubation period and transmission trends, this paper presents the Multivariate Shapelet Learning (MSL) model to learn shapelets from historical observations in multiple areas. An experimental evaluation was done to compare the prediction performance of eleven algorithms, using the data collected from 50 US provinces/states. Results show that the proposed method is effective and efficient. The learned shapelets explain increasing and decreasing trends of new confirmed cases, and reveal that the COVID-19 incubation period in the USA is around 28 days.
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37
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Mukhopadhyay T, Naskar S, Gupta KK, Kumar R, Dey S, Adhikari S. Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions. ADVANCED THEORY AND SIMULATIONS 2021. [DOI: 10.1002/adts.202000291] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- T. Mukhopadhyay
- Department of Aerospace Engineering Indian Institute of Technology Kanpur Kanpur India
| | - S. Naskar
- Department of Aerospace Engineering Indian Institute of Technology Bombay Mumbai India
| | - K. K. Gupta
- Department of Mechanical Engineering National Institute of Technology Silchar Silchar India
| | - R. Kumar
- Department of Mechanical Engineering National Institute of Technology Silchar Silchar India
| | - S. Dey
- Department of Mechanical Engineering National Institute of Technology Silchar Silchar India
| | - S. Adhikari
- College of Engineering Swansea University Swansea United Kingdom
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38
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Bedi P, Dhiman S, Gole P, Gupta N, Jindal V. Prediction of COVID-19 Trend in India and Its Four Worst-Affected States Using Modified SEIRD and LSTM Models. SN COMPUTER SCIENCE 2021; 2:224. [PMID: 33899004 PMCID: PMC8057011 DOI: 10.1007/s42979-021-00598-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 03/17/2021] [Indexed: 12/12/2022]
Abstract
Since the beginning of COVID-19 (corona virus disease 2019), the Indian government implemented several policies and restrictions to curtail its spread. The timely decisions taken by the government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread. Future predictions about the spread can be helpful for future policy-making, i.e., to plan and control the COVID-19 spread. Further, it is observed throughout the world that asymptomatic corona cases play a major role in the spread of the disease. This motivated us to include such cases for accurate trend prediction. India was chosen for the study as the population and population density is very high for India, resulting in the spread of the disease at high speed. In this paper, the modified SEIRD (susceptible–exposed–infected–recovered–deceased) model is proposed for predicting the trend and peak of COVID-19 in India and its four worst-affected states. The modified SEIRD model is based on the SEIRD model, which also uses an asymptomatic exposed population that is asymptomatic but infectious for the predictions. Further, a deep learning-based long short-term memory (LSTM) model is also used for trend prediction in this paper. Predictions of LSTM are compared with the predictions obtained from the proposed modified SEIRD model for the next 30 days. The epidemiological data up to 6th September 2020 have been used for carrying out predictions in this paper. Different lockdowns imposed by the Indian government have also been used in modeling and analyzing the proposed modified SEIRD model.
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Affiliation(s)
- Punam Bedi
- Department of Computer Science, University of Delhi, Delhi, India
| | - Shivani Dhiman
- Department of Computer Science, University of Delhi, Delhi, India
| | - Pushkar Gole
- Department of Computer Science, University of Delhi, Delhi, India
| | - Neha Gupta
- Department of Computer Science, University of Delhi, Delhi, India
| | - Vinita Jindal
- Keshav Mahavidyalaya, University of Delhi, Delhi, India
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39
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A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data. SUSTAINABILITY 2021. [DOI: 10.3390/su13094888] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.
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40
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Piccialli F, di Cola VS, Giampaolo F, Cuomo S. The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2021; 23:1467-1497. [PMID: 33935585 PMCID: PMC8072097 DOI: 10.1007/s10796-021-10131-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/28/2021] [Indexed: 05/25/2023]
Abstract
The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.
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Affiliation(s)
- Francesco Piccialli
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
| | - Vincenzo Schiano di Cola
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80125 Italy
| | - Fabio Giampaolo
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
| | - Salvatore Cuomo
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
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41
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Kristjanpoller W, Michell K, Minutolo MC. A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19. Appl Soft Comput 2021; 104:107241. [PMID: 33679272 PMCID: PMC7920818 DOI: 10.1016/j.asoc.2021.107241] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/18/2021] [Accepted: 02/24/2021] [Indexed: 12/19/2022]
Abstract
Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.
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Affiliation(s)
- Werner Kristjanpoller
- Departamento de Industrias, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
| | - Kevin Michell
- Departamento de Industrias, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile
| | - Marcel C Minutolo
- Robert Morris University, 6001 University Blvd Moon Township, PA 15108, United States of America
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42
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Rostami-Tabar B, Rendon-Sanchez JF. Forecasting COVID-19 daily cases using phone call data. Appl Soft Comput 2021; 100:106932. [PMID: 33269029 PMCID: PMC7687495 DOI: 10.1016/j.asoc.2020.106932] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/17/2020] [Accepted: 11/18/2020] [Indexed: 01/12/2023]
Abstract
The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish different scenarios during the emergency, but their accuracy has been disappointing, forecasts ignore uncertainties and less attention is given to local areas. In this study, we propose a simple Multiple Linear Regression model, optimised to use phone call data to forecast the number of daily confirmed cases. Moreover, we produce a probabilistic forecast that allows decision makers to better deal with risk. Our proposed approach outperforms ARIMA, ETS, Seasonal Naive, Prophet and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures. The simplicity, interpretability and reliability of the model, obtained in a careful forecasting exercise, is a meaningful contribution to decision makers at local level who acutely need to organise resources in already strained health services. We hope that this model would serve as a building block of other forecasting efforts that on the one hand would help front-line personal and decision makers at local level, and on the other would facilitate the communication with other modelling efforts being made at the national level to improve the way we tackle this pandemic and other similar future challenges.
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Affiliation(s)
| | - Juan F Rendon-Sanchez
- Cardiff School of Computer Science and Informatics, Queen's Buildings, 5 The Parade, Roath, CF24 3AA, Cardiff, UK
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43
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Majumdar S, Nandi SK, Ghosal S, Ghosh B, Mallik W, Roy ND, Biswas A, Mukherjee S, Pal S, Bhattacharyya N. Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model. Cognit Comput 2021:1-13. [PMID: 33552306 PMCID: PMC7852055 DOI: 10.1007/s12559-021-09840-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 01/15/2021] [Indexed: 11/11/2022]
Abstract
To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug-target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.
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Affiliation(s)
- Shatadru Majumdar
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Soumik Kumar Nandi
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Shuvam Ghosal
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Bavrabi Ghosh
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Writam Mallik
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Nilanjana Dutta Roy
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India
| | - Arindam Biswas
- Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, India
| | - Subhankar Mukherjee
- Agri and Environmental Electronics (AEE), Centre for Development of Advanced Computing, Kolkata, India
| | - Souvik Pal
- Agri and Environmental Electronics (AEE), Centre for Development of Advanced Computing, Kolkata, India
| | - Nabarun Bhattacharyya
- Agri and Environmental Electronics (AEE), Centre for Development of Advanced Computing, Kolkata, India
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44
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Shakibaei S, de Jong GC, Alpkökin P, Rashidi TH. Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis. SUSTAINABLE CITIES AND SOCIETY 2021; 65:102619. [PMID: 33251093 PMCID: PMC7682431 DOI: 10.1016/j.scs.2020.102619] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/16/2020] [Accepted: 11/21/2020] [Indexed: 05/02/2023]
Abstract
The COVID-19 pandemic, which was reported in early January 2020 in China and spread rapidly around the globe, will certainly remain as one of the most impactful disruptive events of the 21st century. To contain the spread of the virus while awaiting a vaccine, countries applied different approaches from simply giving advice on personal hygiene and applying progressive measures to total lockdown. This paper aims to investigate the impacts of the pandemic on travel behavior in Istanbul, Turkey, through a longitudinal panel study conducted in three phases during the early stages of the epidemic and pandemic. The paper reflects the travel behavior evolution during the development of the outbreak resulting from residents' self- regulation and governmental measures, distinguishing travel for commute, Social/Recreational/Leisure (SRL), and shopping activities, as well as use of different travel modes based on various socio-economic characteristics. Due to the application of the social distancing of at least 1.5 m, closure of numerous non-essential venues, encouraging teleworking and distance education, job losses and cancellation of all social gatherings in Istanbul between the second and third phase of our data collection, the transition in travel activity pattern and transport mobility appears to be quite extreme, particularly for commuting and SRL trips.
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Affiliation(s)
- Shahin Shakibaei
- Transportation Engineering Department, Civil Engineering Faculty, Istanbul Technical University, Istanbul, Turkey
| | | | - Pelin Alpkökin
- Transportation Engineering Department, Civil Engineering Faculty, Istanbul Technical University, Istanbul, Turkey
- Department of Rail Systems, Istanbul Metropolitan Municipality, Istanbul, Turkey
| | - Taha H Rashidi
- Research Centre for Integrated Transport Innovation (RCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia
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45
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Jena KK, Bhoi SK, Prasad M, Puthal D. A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients. Neural Comput Appl 2021; 34:11361-11382. [PMID: 33526959 PMCID: PMC7838018 DOI: 10.1007/s00521-021-05719-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 01/07/2021] [Indexed: 01/10/2023]
Abstract
Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.
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Affiliation(s)
- Kalyan Kumar Jena
- Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur, India
| | - Sourav Kumar Bhoi
- Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur, India
| | - Mukesh Prasad
- School of Computer Science, University of Technology Sydney, Sydney, Australia
| | - Deepak Puthal
- School of Computing, Newcastle University, Newcastle, UK
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Chen T, Wang YC, Wu HC. Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach. Healthcare (Basel) 2021; 9:71. [PMID: 33451165 PMCID: PMC7828742 DOI: 10.3390/healthcare9010071] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/08/2021] [Accepted: 01/09/2021] [Indexed: 02/07/2023] Open
Abstract
The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert's authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic.
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Affiliation(s)
- Toly Chen
- Department of Industrial Engineering and Management, National Chiao Tung University, University Road, Hsinchu 1001, Taiwan;
| | - Yu-Cheng Wang
- Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Hsin-Chieh Wu
- Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 41349, Taiwan;
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Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021; 9:e23811. [PMID: 33326405 PMCID: PMC7806275 DOI: 10.2196/23811] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/27/2020] [Accepted: 11/15/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. METHODS A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. RESULTS The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. CONCLUSIONS In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
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Affiliation(s)
- Hafsa Bareen Syeda
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Kevin Wayne Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Farhanuddin Syed
- College of Medicine, Shadan Institute of Medical Sciences, Hyderabad, India
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Feliciano Yu
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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Rauf HT, Lali MIU, Khan MA, Kadry S, Alolaiyan H, Razaq A, Irfan R. Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. PERSONAL AND UBIQUITOUS COMPUTING 2021; 27:733-750. [PMID: 33456433 PMCID: PMC7797027 DOI: 10.1007/s00779-020-01494-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 11/18/2020] [Indexed: 05/22/2023]
Abstract
The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.
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Affiliation(s)
- Hafiz Tayyab Rauf
- Department of Computer Science, University of Gujrat, Gujrat, Pakistan
| | - M. Ikram Ullah Lali
- Department of Computer Science, University of Education, Lahore, 54770 Pakistan
| | | | - Seifedine Kadry
- Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon
| | - Hanan Alolaiyan
- Department of Mathematics, King Saud University, Riyadh, 11451 Saudi Arabia
| | - Abdul Razaq
- Division of Science and Technology, Department of Mathematics, University of Education, Lahore, 54000 Pakistan
| | - Rizwana Irfan
- Department of Mathematics and Computer Science, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia
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49
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Ahmad A, Garhwal S, Ray SK, Kumar G, Malebary SJ, Barukab OM. The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:2645-2653. [PMID: 32837183 PMCID: PMC7399353 DOI: 10.1007/s11831-020-09472-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 07/23/2020] [Indexed: 05/08/2023]
Abstract
Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
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Affiliation(s)
- Amir Ahmad
- College of Information Technology, United Arab Emirates University, Al Ain, UAE
| | - Sunita Garhwal
- Department of Computer Science and Engineering, Thapar University, Patiala, India
| | - Santosh Kumar Ray
- Department of Information Technology, Khawarizmi International College, Al Ain, UAE
| | - Gagan Kumar
- Department of Physics, Indian Institute of Technology Guwahati, Guwahati, Assam 781039 India
| | - Sharaf Jameel Malebary
- Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 411, Rabigh, Jeddah 21911 Saudi Arabia
| | - Omar Mohammed Barukab
- Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 411, Rabigh, Jeddah 21911 Saudi Arabia
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50
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AI-Empowered Data Analytics for Coronavirus Epidemic Monitoring and Control. ARTIFICIAL INTELLIGENCE FOR CORONAVIRUS OUTBREAK 2021. [PMCID: PMC7307709 DOI: 10.1007/978-981-15-5936-5_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Governments and authorities knew little about the virus since the emergency of COVID-19 outbreak. The Chinese government upon the discovery of the early patients in Wuhan, informed WHO on 31 December 2019, as pneumonia of unknown causes. Epidemiologists, data scientists and biostatisticians have been working hand-in-hand for a common mission of trying to characterize and understand the characteristics of the infection as well as the virus itself, which is SARS alike.
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