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Mathematics
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Modelling, Analysis and Control of COVID-19 Spread Dynamics
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Modelling, Analysis and Control of COVID-19 Spread Dynamics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: closed (6 July 2023) | Viewed by 10783

Special Issue Editors

Dr. Constantinos Siettos

E-Mail Website
Guest Editor
Department of Mathematics and Applications, University of Naples Federico II, 80138 Naples, Italy
Interests: numerical analysis; machine learning; nonlinear dynamics; mathematical physics; complex systems and network
Dr. Maira Aquiar

E-Mail Website
Guest Editor
Laboratory of Mathematical and Computational Biology, Department of Mathematics, University of Trento, Trento, Italy
Interests: public health epidemiology; modeling infectious disease dynamics; non-linear dynamics; stochastic processes

Special Issue Information

Dear Colleagues;

The quest for developing new mathematical models to understand COVID-19 spreading dynamics and the impact of intervention measures is the major and timely challenge of our era. Developing such mathematical tools to guide public health authorities with projections for the national health systems necessities during an outbreak is urgently needed, as well as long-term predictions, while the lockdown measures are gradually lifted, allowing an efficient pandemic response.

Recent advances in different fields have enhanced and deepened our knowledge in different aspects of disease epidemiology, ranging from the molecular structure of the virus to the impact of the contact transmission network in a population. State-of-the-art mathematical/computational techniques allow the integration of the new information generated on virology, field epidemiology, and social behavior, for example, allowing us to build better and more detailed models in a feedback-based manner.

In this Special Issue, we invite authors and groups to contribute with high-quality original research and review articles with a strong focus on mathematical and data-driven modeling analysis at all scales to support and guide public health frontline workers and policy makers during COVID-19 responses.

Dr. Constantinos Siettos
Dr. Maira Aquiar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
  • mathematical modeling
  • data analysis
  • prediction
  • control
  • social networks
  • transmission network
  • intervention policies

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Published Papers (4 papers)

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Research

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20 pages, 3540 KiB  
Article
A Comparative Evaluation between Convolutional Neural Networks and Vision Transformers for COVID-19 Detection
by Saad I. Nafisah, Ghulam Muhammad, M. Shamim Hossain and Salman A. AlQahtani
Mathematics 2023, 11(6), 1489; https://doi.org/10.3390/math11061489 - 18 Mar 2023
Cited by 16 | Viewed by 3069
Abstract
Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging modalities for the identification of [...] Read more.
Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging modalities for the identification of respiratory system disorders. At present, the COVID-19 pandemic is the most well-known illness. We propose a system based on explainable artificial intelligence to detect COVID-19 from CXR images by using several cutting-edge convolutional neural network (CNN) models, as well as the Vision of Transformer (ViT) models. The proposed system also visualizes the infected areas of the CXR images. This gives doctors and other medical professionals a second option for supporting their decision. The proposed system uses some preprocessing of the images, which includes the segmentation of the region of interest using a UNet model and rotation augmentation. CNN employs pixel arrays, while ViT divides the image into visual tokens; therefore, one of the objectives is to compare their performance in COVID-19 detection. In the experiments, a publicly available dataset (COVID-QU-Ex) is used. The experimental results show that the performances of the CNN-based models and the ViT-based models are comparable. The best accuracy was 99.82%, obtained by the EfficientNetB7 (CNN-based) model, followed by the SegFormer (ViT-based). In addition, the segmentation and augmentation enhanced the performance. Full article
(This article belongs to the Special Issue Modelling, Analysis and Control of COVID-19 Spread Dynamics)
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16 pages, 797 KiB  
Article
A New Incommensurate Fractional-Order COVID 19: Modelling and Dynamical Analysis
by Abdallah Al-Husban, Noureddine Djenina, Rania Saadeh, Adel Ouannas and Giuseppe Grassi
Mathematics 2023, 11(3), 555; https://doi.org/10.3390/math11030555 - 20 Jan 2023
Cited by 10 | Viewed by 1435
Abstract
Nowadays, a lot of research papers are concentrating on the diffusion dynamics of infectious diseases, especially the most recent one: COVID-19. The primary goal of this work is to explore the stability analysis of a new version of the SEIR [...] Read more.
Nowadays, a lot of research papers are concentrating on the diffusion dynamics of infectious diseases, especially the most recent one: COVID-19. The primary goal of this work is to explore the stability analysis of a new version of the SEIR model formulated with incommensurate fractional-order derivatives. In particular, several existence and uniqueness results of the solution of the proposed model are derived by means of the Picard–Lindelöf method. Several stability analysis results related to the disease-free equilibrium of the model are reported in light of computing the so-called basic reproduction number, as well as in view of utilising a certain Lyapunov function. In conclusion, various numerical simulations are performed to confirm the theoretical findings. Full article
(This article belongs to the Special Issue Modelling, Analysis and Control of COVID-19 Spread Dynamics)
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17 pages, 503 KiB  
Article
A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors
by Harsh Mankodiya, Priyal Palkhiwala, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Bogdan-Constantin Neagu, Gheorghe Grigoras, Fayez Alqahtani and Ahmed M. Shehata
Mathematics 2022, 10(16), 2927; https://doi.org/10.3390/math10162927 - 14 Aug 2022
Cited by 5 | Viewed by 1698
Abstract
Artificial intelligence has been utilized extensively in the healthcare sector for the last few decades to simplify medical procedures, such as diagnosis, prognosis, drug discovery, and many more. With the spread of the COVID-19 pandemic, more methods for detecting and treating COVID-19 infections [...] Read more.
Artificial intelligence has been utilized extensively in the healthcare sector for the last few decades to simplify medical procedures, such as diagnosis, prognosis, drug discovery, and many more. With the spread of the COVID-19 pandemic, more methods for detecting and treating COVID-19 infections have been developed. Several projects involving considerable artificial intelligence use have been researched and put into practice. Crowdsensing is an example of an application in which artificial intelligence is employed to detect the presence of a virus in an individual based on their physiological parameters. A solution is proposed to detect the potential COVID-19 carrier in crowded premises of a closed campus area, for example, hospitals, corridors, company premises, and so on. Sensor-based wearable devices are utilized to obtain measurements of various physiological indicators (or parameters) of an individual. A machine-learning-based model is proposed for COVID-19 prediction with these parameters as input. The wearable device dataset was used to train four different machine learning algorithms. The support vector machine, which performed the best, received an F1-score of 96.64% and an accuracy score of 96.57%. Moreover, the wearable device is used to retrieve the coordinates of a potential COVID-19 carrier, and the YOLOv5 object detection method is used to do real-time visual tracking on a closed-circuit television video feed. Full article
(This article belongs to the Special Issue Modelling, Analysis and Control of COVID-19 Spread Dynamics)
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Review

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14 pages, 1305 KiB  
Review
Mathematical Modeling and the Use of Network Models as Epidemiological Tools
by Javier Cifuentes-Faura, Ursula Faura-Martínez and Matilde Lafuente-Lechuga
Mathematics 2022, 10(18), 3347; https://doi.org/10.3390/math10183347 - 15 Sep 2022
Cited by 2 | Viewed by 3738
Abstract
Mathematical modeling has served as an epidemiological tool to enhance the modeling efforts of the social and economic impacts of the pandemic. This article reviews epidemiological network models, which are conceived as a flexible way of representing objects and their relationships. Many studies [...] Read more.
Mathematical modeling has served as an epidemiological tool to enhance the modeling efforts of the social and economic impacts of the pandemic. This article reviews epidemiological network models, which are conceived as a flexible way of representing objects and their relationships. Many studies have used these models over the years, and they have also been used to explain COVID-19. Based on the information provided by the Web of Science database, exploratory, descriptive research based on the techniques and tools of bibliometric analysis of scientific production on epidemiological network models was carried out. The epidemiological models used in the papers are diverse, highlighting those using the SIS (Susceptible-Infected-Susceptible), SIR (Susceptible-Infected-Recovered) and SEIR (Susceptible-Exposed-Infected-Removed) models. No model can perfectly predict the future, but they provide a sufficiently accurate approximation for policy makers to determine the actions needed to curb the pandemic. This review will allow any researcher or specialist in epidemiological modeling to know the evolution and development of related work on this topic. Full article
(This article belongs to the Special Issue Modelling, Analysis and Control of COVID-19 Spread Dynamics)
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