Research Insights About Covid-19

We attempt to provide selected highlights in recent research findings

Last Update on 1 February 2021

B. Science and Engineering 

November 2020

November 26, 2020 (Medical Image Analysis)

COVID-AL: The diagnosis of COVID-19 with deep active learning

Xing Wu, Cheng Chen, Mingyu Zhong et al.

The researchers propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy. The results show that the proposed COVID-AL outperforms the state-of-the-art active learning approaches. With only 30% of the data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset.



November 26, 2020 (Toxicology and Industrial Health)

Ventilation use in nonmedical settings during COVID-19: Cleaning protocol, maintenance, and recommendations

Nembhard, Melanie D; Burton, D Jeff; Cohen, Joel M

In this article, the authors review the role that building ventilation can play in minimizing the risk of SARS-CoV-2 transmission in nonmedical environments (e.g. office buildings and school classrooms) and some recommended protocols to follow for its proper use, including cleaning and maintaining mechanical ventilation systems for businesses, schools and homes.



November 21, 2020 (Physica A: Statistical Mechanics and its Applications)

Exact properties of SIQR model for COVID-19

Takashi Odagaki

Dr Odagaki reformulated the SIQR model where compartments for infected and quarantined are redefined to suit COVID-19 situation. He shows that the maximum number of infected depends strongly on the quarantine rate and that the quarantine measure is more effective than the lockdown measure.  He then proposes a theoretical framework to find out an optimum strategy during lockdown and quarantine for minimizing the number of infection and for controlling the early outbreak of a pandemic.



November 19, 2020 (International Journal of Information Management)

Information and communication technologies (ICT)-enabled severe moral communities and how the (Covid19) pandemic might bring new ones

Carlos M. Parra, Manjul Gupta & Patrick Mikalef.

In this study, they present an autopoietic social systems model based on Collectively Prevalent Interpretants (CPIs). They adapt this model to represent and exemplify how Information and Communication Technologies (ICTs) may have led to severe moral communities and promote increasingly polarized, radicalized and even extremist viewpoints. They also present recommendations for theory and practice which may help in advancing digital resiliency (by empowering individuals and communities to recognize when this happens).



November 17, 2020 (Journal of Cleaner Production)

COVID-19 pandemic lessons to facilitate future engagement in the global climate crisis

Krystal M. Perkins, Nora Munguia, Michael Ellenbecker et al.

This article discusses six lessons drawn from the COVID-19 pandemic that can inform and facilitate greater future engagement in the global climate crisis. These lessons were identified through monitoring and analyzing media coverage of COVID-19 related events from late January 2020 to June 2020. The key lessons included the potential of reducing fossil fuel consumption and greenhouse emissions, a case for strong sustainability, a (mis)trust in science, and the possibility of a large-scale change.



November 12, 2020 (Frontiers in Physics)

Trend Analysis of COVID-19 Based on Network Topology Description

Jun Zhu, Yangqianzi Jiang, Tianrui Li et al.

In this study, the trend of the epidemic situation of COVID-19 is analyzed based on the analysis method for network topology. Combining with the sliding window method, the dynamic networks with different topologies for each window are built to consider the relationship of the data on different days. Then the static statistical features on network topologies at different times are extracted during the dynamic evolution of complex networks. It is found that if the value of the trend function tends to decrease, then the epidemic will be effectively controlled.



November 12, 2020 (Frontiers in Physics)

Can Non-steroidal Anti-inflammatory Drugs Affect the Interaction Between Receptor Binding Domain of SARS-COV-2 Spike and the Human ACE2 Receptor? A Computational Biophysical Study

Lenin A. Gonzalez-Paz, Carla A. Lossada, Francelys V. Fernandez Materan et al.

These researchers carried out an exhaustive computational biophysical study of various NSAIDs targeting the RBD-ACE2 complex using multiple comparative analysis of docking and molecular dynamics. Only the Ibuprofen (Propionic acid derivative), Aspirin (Salicylate), and the Acetaminophen (p-aminophenol derivative) had a thermodynamically favourable docking with the interface of the RBD-ACE2 complex. Their results point to Ibuprofen is an NSAID that has the highest probability of generating a disturbance in the stability of the RBD-ACE2 complex. This remains only a theory and requires experiments to demonstrate clinically relevant interaction.

November 11, 2020 (Scientific Reports)

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Linda Wang, Zhong Qiu Lin, Alexander Wong

COVID-Net, a deep convolutional neural network is for the detection of COVID-19 cases from chest X-ray images that are open source and available to the general public. They introduce COVIDx, an open-access benchmark dataset comprising of 13,975 images with the largest number of publicly available COVID-19 positive cases. Furthermore, they investigate how COVID-Net makes predictions using an explainability method to discover deeper insights



November 11, 2020 (Proceedings of the National Academy of Sciences)

Network interventions for managing the COVID-19 pandemic and sustaining economy

Akihiro Nishi, George Dewey, Akira Endo et al.

The authors use agent-based simulations of a network-based susceptible−exposed−infectious−recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. They assume that people engage in group activities in multiple sectors where they interact with others in the same group and potentially become infected.  The simulation results show that the dividing groups' strategy greatly reduces transmission, and the joint implementation of the two strategies could effectively control the spread of transmission.



November 10, 2020 (Proceedings of the National Academy of Sciences)

The interplay of movement and spatiotemporal variation in transmission degrades pandemic control

Nicholas Kortessis, Margaret W. Simon, Michael Barfield et al.

Kortessis et al use a susceptible-infectious-recovered (SIR) model for two coupled populations to make the conceptual point that asynchronous, variable local control, together with movement between populations, elevates long-term regional Rt and cumulative cases. For effective pandemic mitigation strategies, models must include both spatiotemporal heterogeneity in transmission and movement.



November 6, 2020 (Environmental Research)

Exploring the linkage between PM2.5 levels and COVID-19 spread and its implications for socio-economic circles

Syeda Mahnoor Ali, Fatima Malik, Muhammad Shehzaib Anjum et al.

This paper focuses on how the particulate matter pollution was reduced during the lockdown period (23 March to April 15, 2020) as compared to before lockdown. Both ground-based and satellite observations were used to identify the improvement in the air quality of Pakistan. Both datasets have shown a substantial reduction in PM2.5 pollution levels (ranging from 13% to 33% in case of satellite observations, while 23%–58% in ground-based observations) across Pakistan. The result shows a higher rate of COVID-19 spread in major cities of Pakistan with poor air quality conditions. However, it can be partially attributed to both a higher rate of population density and frequent exposure of the population to enhanced levels of PM2.5 concentrations before the lockdown period.



November 5, 2020 (Education for Chemical Engineers)

Delivering remote food engineering labs in COVID-19 time

Marie Debacq, Giana Almeida, Kevin Lachin et al.

They describe implementing a remote educational device within a few weeks, designed as a viable alternative to conventional food engineering labs for Master level French students. Four engineering labs (corresponding to four unit operations widely found in the food industry) were transposed: appertization of cans; concentration in a falling film evaporator; frontal filtration in a plate filter; and spray drying. In the remote labs, hands-on experiments were replaced with various types of virtual tours of the equipment, a detailed description and illustration of its operation, and analysis of real data. The effectiveness of the system was evaluated through direct observation and discussions. The educational resources and practices implemented are an opportunity to develop new teaching methods in the future.



November 4, 2020 (European Journal of Radiology)

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

D. Javor, H. Kaplan, A. Kaplan et al.

Novel deep learning derived machine learning (ML) classifier was developed and an open-source dataset consisting of 6868 chest CT images from 418 patients which were split into training and validation subsets. The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients.



November 4, 2020 (Biomedical Signal Processing and Control)

Control of COVID-19 system using a novel nonlinear robust control algorithm

Musadaq A. Hadi & Hazem I. Ali.

The researchers introduce a new mathematical-engineering strategy to control the epidemic. A new robust control algorithm is introduced to compensate for the nonlinear system. The Most Valuable Player Algorithm (MVPA) is applied to optimize the parameters of the proposed controller. They simulate based on the data from two cities Hubei (China) and Lazio (Italy) since the outbreak. It can be concluded that the proposed control algorithm can effectively compensate for the COVID-19 system.



November 4, 2020 (Biomedical Signal Processing and Control)

Control of COVID-19 system using a novel nonlinear robust control algorithm

Musadaq A. Hadi & Hazem I. Ali.

In this paper, a new mathematical-engineering strategy is introduced to control the epidemic. Control theory is involved in controlling the unstable epidemic with the other suggested strategies until the vaccine is developed. A new robust control algorithm is introduced to compensate for the nonlinear system. Also, the Variable Transformation Technique is used to simplify the COVID-19 system. They claimed that the proposed control algorithm can effectively compensate the pandemic.



November 2, 2020 (Pattern Recognition)

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

Adel Oulefki, Sos Agaian, Thaweesak Trongtirakul et al.

The authors designed an automatic COVID-19 Lung Infection segmentation and measurement tool using chest CT images. The extensive computer simulations show better efficiency and flexibility learning approach on CT image segmentation with image enhancement compared to the segmentation approaches. Experiments performed on COVID-CT-Dataset containing 275 CT scans that are positive and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. Their results proved that the proposed approach is more robust, accurate and straightforward.