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Deep Learning: AI Steps Up in Battle against COVID-19
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Deep Learning: AI Steps Up in Battle against COVID-19

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 27368

Special Issue Editors

Dr. Santoso Wibowo

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Melbourne, QLD 4701, Australia
Interests: artificial intelligence; deep learning; machine learning; intelligent information systems; performance evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. A. B.M.Shawkat Ali

E-Mail Website
Guest Editor
School of Information and Communication Technology, Central Queensland University, Rockhampton, Australia
Interests: data analysis; Big Data; access protocols; business data processing; cloud computing; computerised monitoring; data communication; developing countries; electronic health records; graph theory; health care

Special Issue Information

Dear Colleagues,

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The World Health Organization has announced that COVID-19 is a pandemic and has now become an international public health emergency. In the advent of COVID-19 epidemic since December 2019, health professionals, policy makers and governments have been struggling to make critical decisions under high uncertainty. The fast and accurate detection of the COVID-19 infection is essential to identify, make better decisions and ensure treatment for the patients which will help save their lives. Time is also of the essence in stopping the epidemic so as to reduce its damages as soon as possible. In addition, uncertainties are the largest obstacle to obtain an accurate approach for forecasting the future behaviours of the epidemic.

In data science, this represents a typical problem of deep learning over incomplete or limited data in the early stage of an epidemic. Over the last decades, numerous machine learning including deep learning algorithms have been developed for dealing with various health-related problems. Given the proliferation of such approaches, it is important to have a thorough understanding of their value, usability and applicability in the fight against COVID -19 to minimize the loss of human lives, societal costs and economic losses caused by this infectious disease.

This Special Issue aims to gather a selection of papers presenting original and innovative contributions in the field of deep learning and other machine learning approaches for detecting, assessing and predicting the outbreak of COVID-19 virus.

Dr. Santoso Wibowo
Dr. A. B.M.Shawkat Ali
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • COVID-19
  • Artificial Intelligence (AI)
  • Deep learning
  • Drug discovery
  • Evaluation
  • Forecasting
  • Machine learning
  • Novel solution
  • Social distance
  • Visualization and Detection

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Published Papers (4 papers)

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Research

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16 pages, 2916 KiB  
Article
One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan
by Essam A. Rashed and Akimasa Hirata
Int. J. Environ. Res. Public Health 2021, 18(11), 5736; https://doi.org/10.3390/ijerph18115736 - 27 May 2021
Cited by 25 | Viewed by 5426
Abstract
With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious [...] Read more.
With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner. Full article
(This article belongs to the Special Issue Deep Learning: AI Steps Up in Battle against COVID-19)
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18 pages, 6750 KiB  
Article
Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment
by Anargyros Chatzitofis, Pierandrea Cancian, Vasileios Gkitsas, Alessandro Carlucci, Panagiotis Stalidis, Georgios Albanis, Antonis Karakottas, Theodoros Semertzidis, Petros Daras, Caterina Giannitto, Elena Casiraghi, Federica Mrakic Sposta, Giulia Vatteroni, Angela Ammirabile, Ludovica Lofino, Pasquala Ragucci, Maria Elena Laino, Antonio Voza, Antonio Desai, Maurizio Cecconi, Luca Balzarini, Arturo Chiti, Dimitrios Zarpalas and Victor Savevskiadd Show full author list remove Hide full author list
Int. J. Environ. Res. Public Health 2021, 18(6), 2842; https://doi.org/10.3390/ijerph18062842 - 11 Mar 2021
Cited by 9 | Viewed by 4915
Abstract
Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most [...] Read more.
Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively. Full article
(This article belongs to the Special Issue Deep Learning: AI Steps Up in Battle against COVID-19)
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16 pages, 3324 KiB  
Article
A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis
by Jie Hua, Guohua Wang, Maolin Huang, Shuyang Hua and Shuanghe Yang
Int. J. Environ. Res. Public Health 2020, 17(11), 3973; https://doi.org/10.3390/ijerph17113973 - 3 Jun 2020
Cited by 9 | Viewed by 4727
Abstract
Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant [...] Read more.
Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective. Full article
(This article belongs to the Special Issue Deep Learning: AI Steps Up in Battle against COVID-19)
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Review

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24 pages, 2617 KiB  
Review
Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions
by Tarik Alafif, Abdul Muneeim Tehame, Saleh Bajaba, Ahmed Barnawi and Saad Zia
Int. J. Environ. Res. Public Health 2021, 18(3), 1117; https://doi.org/10.3390/ijerph18031117 - 27 Jan 2021
Cited by 105 | Viewed by 10414
Abstract
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. [...] Read more.
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided. Full article
(This article belongs to the Special Issue Deep Learning: AI Steps Up in Battle against COVID-19)
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