Special Issue "Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19"

Submission Deadline: 30 September 2020
Submit to Special Issue
Guest Editors
Prof. Yu-Dong Zhang, University of Leicester, UK
Prof. Qilong Wang, Nanjing Medical University, China
Dr. Sean H. Y. Yuan, City University of Hong Kong, China

Summary

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was declared to be a Public Health Emergency of International Concern and recognized as a pandemic by the World Health Organization. The outbreak of COVID-19 has rapidly spread to most countries in the world. To date (21 April 2020), there have been more than 2.5 million confirmed cases and 174,336 deaths around the world.

 

Computer modelling (CM) plays an important role in fighting COVID-19. For example, CM techniques can help develop vaccine & targeted drugs for COVID-19. In epidemiology, CM can provide tracking and prediction of the spread speed of infected territories and areas, so as to assist policymakers to make appropriate decisions. The visualization technology provides a global overview for policy-makers. AI and CM methods are efficient in making fast and accurate diagnoses of COVID-19 using trained models, based on routine CT or X-ray or other imaging tools. Wearable sensors can monitor abnormality for home-based mild COVID-19 patients. Human behavioral data can be analyzed to make better individual or community quarantine and social control policies. CM can help predict the protein structures of the coronavirus. The emotional data in mental health can be utilized to help people to cope with self-quarantined people. CM can manage medical resources (e.g., face masks, ventilator, et al.) supply chain.

 

This Special Section aims to invite original research papers that report the latest advances of medical images based health informatics for COVID-19. Submissions should clarify the substantive improvements on work that has already been published, accepted for publication, or submitted in parallel to other conferences or journals.

 

The topics of interest include, but are not limited to computer modelling for COVID-19 and other infectious diseases:

• Early prediction and early detection using AI and advanced signal processing methods;

• Genotype, phenotype, and pathogenesis;

• Supervised or semi-supervised learning for classification & segmentation;

• Diagnosis using biomarkers and imaging-based data-driven methods;

• Tracking and prediction of spread speed of infected territories and areas;

• Transfer learning methods for diagnosis and segmentation;

• Explainable AI-based prediction, segmentation, and diagnosis;

• Medical and healthcare equipment/resources supply chain management;

• Wearable sensors or IoT based public health support, patient behavior and emotion monitoring;

• VR/AR computer-aided diagnosis system;

• Design and development of vaccine & targeted drug;

• Epidemic dynamics prediction and forecast;

• Computational prediction of protein structure associated with virus;

• Epidemic prevention and control; 

• Socio-economic impacts of COVID-19 interventions;

• Evaluating human responses and social distancing during the outbreak;

• Survival and risk of recurrence estimation;

• 2D and 3D visualization;

• Recovery prediction in rehabilitation;

• Potential therapeutics;

• Public health system or strategies;

• Psychological stress and intervention during outbreak of COVID-19.


Publication Fee: All fees are waived for this special issue.



Published Papers
  • Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets
  • Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with three parameters based on the… More
  •   Views:370       Downloads:159        Download PDF