Special Issue "Artificial Intelligence and Information Technologies for COVID-19"

Submission Deadline: 30 April 2020 (closed)
Guest Editors
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


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

Published Papers
  • Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic
  • 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 other places. A M/G/2 queuing… More
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  • Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spread in Saudi Arabia
  • 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, 0) gave a better forecast… More
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  • Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data
  • 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 central areas of infected/suspected people,… More
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  • An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic
  • 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 of epidemic control, many algorithms… More
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  • Data Driven Modelling of Coronavirus Spread in Spain
  • 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 these measures, depends on the… More
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  • On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning
  • 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 models with fine-tuning the weights… More
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  • Machine Learning and Classical Forecasting Methods Based Decision Support Systems 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, Prophet, and Long-Short Term Memory… More
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  • Mathematical Analysis of Novel Coronavirus (2019-nCov) Delay Pandemic Model
  • 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 RnCov of delayed model. Two… More
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  • COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
  • 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, and can also reflect the… More
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  • A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of 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 regions. Then the integrity of… More
  •   Views:1901       Downloads:1582        Download PDF

  • An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
  • 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 results of the improved LSTM… More
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