June 30, 2020 (J of Systems Architexture)

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Abu Sufian, Anirudha Ghosh, Ali Safaa Sadiq et al.

https://doi.org/10.1016/j.sysarc.2020.101830

The authors present a systemic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing (EC) to mitigate COVID-19. They surveyed existing DL, DTL, EC and Dataset to mitigate pandemics with potentialities and challenges. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.

 

 

June 28, 2020 (J of Chaos, Solitons and Fractals)

A SIR model assumption for the spread of COVID-19 in different communities

Ian Cooper, Argha Mondal, Chris G.Antonopoulos

​https://doi.org/10.1016/j.chaos.2020.110057

The authors present the effectiveness of the modelling approach on the pandemic and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community.The time frame is from January to June 2020. They investigate the time evolution of different populations and monitor various parameters for the spread of the disease in various communities, including China, South Korea, India, Australia, USA, Italy and the state of Texas in the USA. They show the SIR model can help to assess the impact of the disease by offering valuable predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. Finally they conclude that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease.

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June 27, 2020 (Current Problems in Diagnostic Radiology)

Current landscape of Imaging and the potential role for Artificial intelligence in the management of COVID-19

Faiq Shaikh, Michael Anderson, M. Rizwan Sohail et al.

https://doi.org/10.1067/j.cpradiol.2020.06.009

Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of COVID-19. Chest x-ray radiography (CXR) and computed tomography (CT) are the standard imaging modalities used for the structural assessment of the disease status. Artificial intelligence (AI) can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. The authors review the current landscape of these imaging modalities and AI approaches as applied in COVID-19 management.

 

 

June 26, 2020 (European Radiology)

Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome.

Matos, J., Paparo, F., Mussetto, I. et al.

https://doi.org/10.1186/s41747-020-00167-0

Volume of disease (VoD) on computed tomography (CT) scan and clinical information predict early outcome in COVID-19 patients. The authors measured VoD on CT scan of 106 COVID-19 positive patients using a simple CT post-processing tool. They found that CT and clinical data together enable accurate prediction of short-term clinical outcome.

 

 

June 26, 2020 (European Radiology)

Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation

Ezio Lanza, Riccardo Muglia, Isabella Bolengo et al.

https://doi.org/10.1007/s00330-020-07013-2

The authors performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. The authors tested quantitative CT analysis (QCT) as an outcome predictor for COVID-19. They found that may serve as a tool for the triaging process of COVID-19.

 

 

June 25, 2020 (Psychological Trauma: Theory, Research, Practice, and Policy)

The Coronavirus Pandemic in Malaysia: A Commentary

Sheena Kaur

https://doi.apa.org/fulltext/2020-45468-001.html

The author expresses her views on how the COVID-19 pandemic is affecting the Malaysian population from a mental health perspective. She also explains how the Malaysian health care system is organised and discusses the response of the people to the pandemic.

 

 

June 24, 2020 (JAMA Netw. Open)

Effect of Colchicine vs Standard Care on Cardiac and Inflammatory Biomarkers and Clinical Outcomes in Patients Hospitalized With Coronavirus Disease 2019: The GRECCO-19 Randomized Clinical Trial

Spyridon G. Deftereos, Georgios Giannopoulos, Dimitrios A. Vrachatis et al. https://doi.org/10.1001/jamanetworkopen.2020.13136

This randomized clinical trial evaluates the effect of treatment with colchicine on cardiac and inflammatory biomarkers and clinical outcomes in patients hospitalized with coronavirus disease 2019 (COVID-19).  Participants who received colchicine had statistically significantly improved time to clinical deterioration. However, this must be interpreted with caution.

 

 

June 18, 2020 (Clinical and Translational Radiation Oncology)

Conducting research in Radiation Oncology remotely during the COVID-19 pandemic: Coping with isolation

Jennifer Dhont, Marialaura Di Tella, Ludwig Dubois et al.

https://doi.org/10.1016/j.ctro.2020.06.006

The authors  carried out a survey amongst researchers in the field of radiation oncology to gain insights on the impact of social isolation and working from home and to guide future work. They found that perceived productivity was lower, with associated feelings of guilt. Additionally, Anxiety and depressive symptoms were higher than normative values in Europe. They also found that mental health symptoms were lower in participants with institutional health support.

 

 

June 17, 2020 (The Lancet Infectious Disease)

Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study

Qin-Long Jing, Ming-Jin Liu, Zhou-Bin Zhang et al.

https://doi.org/10.1016/S1473-3099(20)30471-0

The authors estimated the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. They found that SARS-CoV2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. 

 

 

June 16 2020  (Journal of Medical Internet Research)

Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers

Guy Fagherazzi, Catherine Goetzinger, Mohammed Ally Rashid et al

https://www.jmir.org/2020/6/e19284/

In this editorial, the authors discuss the current situation regarding digital health solutions to fight COVID-19 as well as the challenges and ethical issues. Telemedicine has been established as a successful health care model. Social media platforms such as Twitter and Google Trends analyses are very beneficial to model pandemic trends as well as to monitor the evolution of patients’ symptoms or public reaction to the pandemic over time. Digital tools can provide collective public health benefits but individual freedoms may be compromised. There is a strong potential for various digital health solutions and many more to be explored.  We need to ensure the future digital health initiatives will have a greater impact on the epidemic and meet the most strategic needs to bring about relief to sufferings.

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June 16, 2020 (Immunity)

Immunology of COVID-19: Current State of the Science

Nicolas Vabret, Graham J. Britton, Conor Gruber et al.

https://doi.org/10.1016/j.immuni.2020.05.002

In this review, the authors summarize the current state of knowledge of innate and adaptive immune responses elicited by SARS-CoV-2 infection and the immunological pathways that likely contribute to disease severity and death. They also discuss the rationale and clinical outcome of current therapeutic strategies as well as prospective clinical trials to prevent or treat SARS-CoV-2 infection.

 

 

June 15, 2020 (Science)

Potent neutralizing antibodies from COVID-19 patients define multiple targets of vulnerability

Philip J. M. Brouwer, Tom G. Caniels, Karlijn van der Straten et al.

https://doi.org/10.1126/science.abc5902

The authors describe their work in isolating monoclonal antibodies from three convalescent COVID-19 patients using a SARS-CoV-2 stabilized prefusion spike protein. They found that a subset of the antibodies were able to potently inhibit authentic SARS-CoV-2 infection as low as 0.007 μg/mL. In addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.

 

 

June 15, 2020 (The Lancet)

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Andrew Clark, Mark Jit, Charlotte Warren-Gash et al.

https://doi.org/10.1016/S2214-109X(20)30264-3

The authors estimated the number of individuals at increased risk of severe COVID-19 and to see how this varies between countries to inform the design of possible strategies to shield or vaccinate those at highest risk.  They found that about one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. 

 

 

June 12, 2020 (Science)

The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries

Patrick G. T. Walker, Charles Whittaker, Oliver J Watson et al.

https://doi.org/10.1126/science.abc0035

The ongoing COVID-19 pandemic has affected everyone. The authors combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control.

 

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June 12, 2020 (JAMA Health Forum)

Strategies for Digital Care of Vulnerable Patients in a COVID-19 World—Keeping in Touch

Darrell M. Gray II, Joshua J. Joseph, J. Nwando Olayiwola

https://doi.org/10.1001/jamahealthforum.2020.0734

The COVID-19 pandemic has resulted in a shift in health care delivery. Telehealth is emerging as an essential way to provide health care service. Here, the authors explore the potential dangers of Telehealth and offer strategies to mitigate them.

 

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June 11 2020  (PNAS)

Identifying airborne transmission as the dominant route for the spread of COVID-19

Renyi Zhang, Yixin Li, Annie L. Zhang, et al

https://www.pnas.org/content/117/26/14857

The authors describe the transmission pathways of COVID-19 by analyzing the trend and mitigation measures in three epicentres. They show that the airborne transmission route is highly virulent and dominant for the spread of COVID-19. Their analysis shows that the difference with and without mandated face covering represents the determinant in shaping the trends of the pandemic. This protective measure significantly reduces the number of infections. They also show that measures such as social distancing alone implemented in the United States are insufficient in protecting the public.

 

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June 11 2020  (Cell)

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Kang Zhang, Xiaohong Liu, Jun Shen

https://doi.org/10.1016/j.cell.2020.04.045

The authors report an AI system that can diagnose COVID-19 pneumonia using CT scans. It can also predict progression to critical illness and has the potential to improve performance of junior radiologists to the senior level. They also claim that the system can assist evaluation of drug treatment effects with CT quantification.

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June 10, 2020  (Dialogues in Human Geography)                                                       

Charting COVID-19 futures: Mapping, anticipation, and navigation

Jeremy Brice

https://doi.org/10.1177/2043820620934331

This commentary argues that visualisations of COVID-19 transmission and mortality map out possible futures. It outlines a navigational approach to such mappings which interrogates their role in guiding anticipatory actions that are shaping COVID-19’s emerging geographies.

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June 10, 2020 (Dialogues in Human Geography)

Smart cities and a data-driven response to COVID-19

Philip James, Ronnie Das, Agata Jalosinska, Luke Smith

https://doi.org/10.1177/2043820620934211

This commentary describes the rapid development of a COVID-19 data dashboard utilising existing Urban Observatory Internet of Things (IoT) data and analytics infrastructure. Existing data capture systems were rapidly repurposed to provide real-time insights into the impacts of lockdown policy on urban governance.

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June 10, 2020 (AJR)

Diagnostic Ultrasound Services During the Coronavirus Disease (COVID-19) Pandemic

Apoorva Gogna, Praveen Yogendra, Sally Hsueh Er Lee et al.

https://doi.org/10.2214/AJR.20.23167

The authors share their experiences and protocols of performing diagnostic ultrasounds during the COVID-19 pandemic to prevent risk of transmission of COVID-19 from patients to healthcare workers.

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June 1 2020  (Journal of Applied Clinical Medical Physics)

The COVID‐19 Pandemic — Can open access modeling give us better answers more quickly?

Mary Beth Allen  Michael Mills  Mehdi Mirsaeidi

https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/acm2.12941?campaign=wolearlyview

In this editorial, the authors introduce ‘System dynamics as the process of representing a complex system with interrelated parts that interact in a nonlinear and unpredictable method within a system and predicting those interactions and outcomes’. They then review several models that had been used during the pandemic, however  most of models have focused specifically on the epidemiology of disease. They argue for an open access model to serve as an important resource as new data continues to emerge and social distancing policies relax across the world.  They encourage the medical physics community to take advantage of systems dynamics as a useful tool for research.

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June 2020   (Current Research in Green and Sustainable Chemistry)

Tackling COVID-19 pandemic through nanocoatings: Confront and exactitude

Pradeep Kumar Rai,  Zeba Usmani. Vijay Kumar Thakur,  et al

https://www.sciencedirect.com/science/article/pii/S266608652030014X

The authors review the research and possible protection and care possibilities against COVID19. COVID19. Previous research findings suggest the usage of nanotechnology as an important avenue to develop antiviral drugs and materials to effectively minimize the acquired infection of COVID-19 in public places like hospitals, transport, schools, worship places, malls, etc. Antimicrobial nanocoatings at these places and development of targeted antiviral drugs through capped nanoparticles could likely be a major effective option to halt the spread of this disease.

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June 2020  (Computers in Biology and Medicine)

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

AA Ardakani, AR Kanafi, UR Acharya et al

https://www.sciencedirect.com/science/article/pii/S0010482520301645

Fast diagnostic methods can control and prevent the spread of pandemic diseases like

coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in

high workload conditions. The researchers used 10 CNNs to distinguish infection of COVID-19 from non-COVID-19 groups. They concluded that ResNet-101 and Xception represented the best performance with an AUC of 0.994.

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June 2020  (Chaos, Solitons & Fractals)

Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries

Xiaolei Zhang.Renjun Ma, Lin Wang

https://www.sciencedirect.com/science/article/pii/S0960077920302290

The authors use a segmented Poisson model incorporating the power law and the exponential to study the COVID-19 outbreaks. They estimate  the turning point, final size, duration and the attack rate. They then report the findings of daily new cases of the six Western countries in the Group of Seven.

 

 

June 2020  (Computers in Biology and Medicine)

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, et al

https://www.sciencedirect.com/science/article/pii/S0010482520301621

The authors propose deep model for early detection of COVID-19 cases using X-ray images.They claim accuracy of 98.08% and 87.02% for binary and multi-classes. The proposed heatmaps can help the radiologists to locate the affected regions on chest X-rays.The authors  conclude that   DarkCovidNet model can assist the clinicians to make faster and accurate diagnosis.

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