Xiaorui Zhang, Nanjing University of Information Science & Technology, China
Zhihua Xia, Engineering Research Center of Digital Forensics, Ministry of Education, China
Suzanne McIntosh, New York University, USA
Anasse Bari, New York University, USA
Lixin Xie, PLA General Hospital, China

Summary

The COVID-19 disease has reached pandemic proportion, according to World Health Organization (WHO) situation report 61. WHO confirms 266,073 cases worldwide as of March 21, 2020, with a cumulative death toll of 11,184. Unfortunately, both numbers are expected to continue to rise.

The goal of this special issue of Computers, Materials & Continua (CMC) Journal is to offer enlightening research and effective tools that can help track and predict the spread of the virus, prevent the pandemic from spreading further, and better allocate scarce resources to optimize patient outcomes. This special issue is jointly established by the CMC and the Engineering Research Center of Digital Forensics of Ministry of Education to call upon experts from the fields of big data and artificial intelligence to conduct research on COVID-19 tracking, suppression, and treatment strategies. This special issue invites high quality articles to share and discuss the latest developments and future trends of prevention and treatment methods which are based on the analysis of data. Topics include, but are not limited to, the following areas:

• Artificial intelligence in COVID-19 drug discovery and development

• Big data in COVID-19 analysis

• Knowledge representation in COVID-19 analysis

• Pattern recognition in COVID-19 risk analysis

• Machine learning for COVID-19 tracking and prediction models

• Applications of the Internet of Things in healthcare

• Computer vision in COVID-19-related medical imaging

• Computer vision in COVID-19 treatment simulation

• Distributed system in COVID-19 treatment simulation

• Information technologies in COVID-19 patient tracking

• Information technologies in COVID-19 patient monitoring

• Information technologies in hospital management during an epidemic or pandemic

• Software and/or Hardware in computer-assisted surgery

• Social network analysis for contact tracing

• Artificial intelligence security in analysis of public health emergencies

• Security and privacy of big data in public health emergencies

• Secure and privacy-preserving analysis of data in public health emergencies

• Social media security and forensics in COVID-19 risk management

• Optimizing allocation of resources during a pandemic or epidemic

• Telemedicine for managing non-emergent patients

• Information technologies to support vaccine development

• Predictive Analytics in COVID-19 risk profiling

• Predictive Analytics in patients utilization patterns

• Predictive Analytics in aiding diagnosis

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ARTICLE

Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic
Jing Hu, Gang Hu, Jiantao Cai, Lipeng Xu, Qirun Wang
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 793-803, 2021, DOI:10.32604/cmc.2020.011110
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children’s Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children’s conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from More >

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Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spread in Saudi Arabia
Sunil Kumar Sharma, Shivam Bhardwaj, Rashmi Bhardwaj, Majed Alowaidi
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 805-825, 2021, DOI:10.32604/cmc.2020.011937
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract This article discusses short–term forecasting of the novel Corona Virus (COVID-19) data for infected and recovered cases using the ARIMA method for Saudi Arabia. The COVID-19 data was obtained from the Worldometer and MOH (Ministry of Health, Saudi Arabia). The data was analyzed for the period from March 2, 2020 (the first case reported) to June 15, 2020. Using ARIMA (2, 1, 0), we obtained the short forecast up to July 02, 2020. Several statistical parameters were tested for the goodness of fit to evaluate the forecasting methods. The results show that ARIMA (2, 1, More >

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Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data
Hussein Shahata Abdallah, Mohamed H. Khafagy, Fatma A. Omara
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1303-1320, 2020, DOI:10.32604/cmc.2020.011313
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract Nowadays, the world is fighting a dangerous form of Coronavirus that represents an emerging pandemic. Since its early appearance in China Wuhan city, many countries undertook several strict regulations including lockdowns and social distancing measures. Unfortunately, these procedures have badly impacted the world economy. Detecting and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading disease. For helping this hard effort, this research presents an innovative case study based on big data processing techniques to build a complete tracking system able to identify the… More >

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A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX
Jun Li, Jieren Cheng, Naixue Xiong, Lougao Zhan, Yuan Zhang
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data… More >

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An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic
Peipeng Yu, Zhihua Xia, Jianwei Fei, Sunil Kumar Jha
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process More >

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Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine
Muhammad Adnan Khan, Sagheer Abbas, Khalid Masood Khan, Mohammad A. Al Ghamdi, Abdur Rehman
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1329-1342, 2020, DOI:10.32604/cmc.2020.011155
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine… More >

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Data Driven Modelling of Coronavirus Spread in Spain
G. N. Baltas, F. A. Prieto, M. Frantzi, C. R. Garcia-Alonso, P. Rodriguez
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of… More >

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On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning
Mohammad Shorfuzzaman, Mehedi Masud
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1359-1381, 2020, DOI:10.32604/cmc.2020.011326
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained… More >

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Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19
Ramazan Ünlü, Ersin Namlı
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1383-1399, 2020, DOI:10.32604/cmc.2020.011335
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, More >

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Mathematical Analysis of Novel Coronavirus (2019-nCov) Delay Pandemic Model
Muhammad Naveed, Muhammad Rafiq, Ali Raza, Nauman Ahmed, Ilyas Khan, Kottakkaran Sooppy Nisar, Atif Hassan Soori
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1401-1414, 2020, DOI:10.32604/cmc.2020.011314
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract In this manuscript, the mathematical analysis of corona virus model with time delay effect is studied. Mathematical modelling of infectious diseases has substantial role in the different disciplines such as biological, engineering, physical, social, behavioural problems and many more. Most of infectious diseases are dreadful such as HIV/AIDS, Hepatitis and 2019-nCov. Unfortunately, due to the non-availability of vaccine for 2019- nCov around the world, the delay factors like, social distancing, quarantine, travel restrictions, holidays extension, hospitalization and isolation are used as key tools to control the pandemic of 2019-nCov. We have analysed the reproduction number R_nCov More >

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COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
Yixian Zhang, Jieren Cheng, Yifan Yang, Haocheng Li, Xinyi Zheng, Xi Chen, Boyi Liu, Tenglong Ren, Naixue Xiong
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, More >

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A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19
Xiaorui Zhang, Wenfang Zhang, Wei Sun, Tong Xu, Sunil Kumar Jha
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1435-1452, 2020, DOI:10.32604/cmc.2020.011359
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest… More >

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What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling
Hengyang Lu, Yutong Lou, Bin Jin, Ming Xu
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1453-1471, 2020, DOI:10.32604/cmc.2020.011270
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for… More >

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An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
Bingjie Yan, Jun Wang, Zhen Zhang, Xiangyan Tang, Yize Zhou, Guopeng Zheng, Qi Zou, Yao Lu, Boyi Liu, Wenxuan Tu, Neal Xiong
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317
（This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental… More >

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Artificial Intelligence and Information Technologies for COVID-19

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Summary

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