Research Insights About Covid-19

We attempt to provide selected highlights in recent research findings

Last Update on 25 June 2020

B. Science and Engineering

Jun 2020

September 2020

September 2020   (Chaos, Solitons & Fractals)

COVID-19 created chaos across the globe: Three novel quarantine epidemic models

Bimal Kumar Mishra, Ajit Kumar Keshri,Yerra Shankar Rao

https://www.sciencedirect.com/science/article/pii/S0960077920303271?via%3Dihub

The authors developed three quarantine models of this pandemic taking into account the compartments: susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population and recovered population. Home isolation and quarantine are the two pivot parameters. These are then critically analysed with extensive numerical simulations and examples.

August 2020

Aug 1, 2020 (Science of The Total Environment)

Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth

Fábio A.M. Cássaro,  Luiz F.Pires

https://www.sciencedirect.com/science/article/pii/S0048969720323512

The authors simulate SARS-COV-2 evolution by using the cumulative distribution function (CDF). They predict the first derivative of CDF on the number of new daily cases from China and other European countries. The results presented highlighted the importance of a more realistic model of growth to check the evolution of the confirmed cases.

July 2020

July 2020   (Chaos, Solitons & Fractals)

Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression

Ricardo Manuel, Arias Velásquez, Jennifer Vanessa et al

https://www.sciencedirect.com/science/article/pii/S0960077920303234

The authors analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated  with chaotic Dynamical Systems with information obtained in 82 days with daily learning from January 21th, 2020 to April 12th.  The forecast places the peak in USA around July 14th 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Their findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully.

 

July 2020   (Chaos, Solitons & Fractals)

Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak

AminYousefpour, Hadi Jahanshahi, Stelios Bekiros

https://www.sciencedirect.com/science/article/pii/S0960077920302836

The authors claim to be the first research that proposes policies for COVID-19 by considering its economic consequences. They used a mathematical model of the novel coronavirus to research on policy. A multi-objective genetic algorithm which suggests strategies to achieve high-quality schedules by adjusting various factors was attempted..

July 10 2020 Journal of Fluid Mechanics 

The flow physics of COVID-19

Rajat Mittal, Rui Ni and Jung-Hee Seo 

https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/flow-physics-of-covid19/476E32549012B3620D2452F30F2567F1

Flow physics plays a key role in nearly every facet of the COVID-19 pandemic. This includes the generation and aerosolization of virus-laden respiratory droplets from a host, its airborne dispersion and deposition on surfaces, as well as the subsequent inhalation of these bioaerosols by unsuspecting recipients. Fluid dynamics is also key to preventative measures such as the use of face masks, hand washing, ventilation of indoor environments and even social distancing. This article summarizes what we need to learn about the science underlying these issues so that we are better prepared to tackle the next outbreak of COVID-19.

June 2020

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.

 

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.

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.

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.

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.

May 2020

April 2020

April 27, 2020 (Nature)

Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals

Yuan Liu, Zhi Ning, Ke Lan

https://www.nature.com/articles/s41586-020-2271-3

While the transmission of SARS-CoV-2 via human respiratory droplets and direct contact is clear, the potential for aerosol transmission is poorly understood. This study investigates the aerodynamic nature of SARS-CoV-2 by measuring viral RNA in aerosols in different areas of two Wuhan hospitals during the COVID-19 outbreak in February and March 2020.  The authors propose that the virus could be transmitted via aerosols. They show that room ventilation, open space, sanitization of protective apparel, and proper use and disinfection of toilet areas can effectively limit the concentration of SARS-CoV-2 RNA in aerosols.

 

April 24, 2020 (PLOS Biology)

Leveraging open hardware to alleviate the burden of COVID-19 on global health systems

Andre Maia Chagas , Jennifer C. Molloy , Lucia L. Prieto-Godino  et al

https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000730

The authors summarise community-driven approaches based on Free and Open Source scientific and medical Hardware (FOSH) as well as personal protective equipment (PPE) currently being developed to support the global response for COVID-19 prevention, treatment and diagnosis. If you are interested to explore further, do read this paper.

 

 

April 20, 2020  (J Biomol Struct Dyn)

Novel 2019 Coronavirus Structure, Mechanism of Action, Antiviral Drug Promises and Rule Out Against Its Treatment

Subramanian Boopathi , Adolfo B Poma, Ponmalai Kolandaivel

https://www.tandfonline.com/doi/full/10.1080/07391102.2020.1758788

This review addresses novel coronavirus structure, mechanism of action, and trial test of antiviral drugs in the laboratories and patients with COVID-19. Computational simulation such as computer-aided drug design has been a very useful research tool. It has very good illustrations on the structures and mechanisms of action.

 

Apr 17, 2020 (Eur Radiol Exp)

Deep Learning Detection and Quantification of Pneumothorax in Heterogeneous Routine Chest Computed Tomography

S Röhrich, T Schlegl, C Bardach et al

https://pubmed.ncbi.nlm.nih.gov/32303861/

The authors developed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data to facilitate the automated triage of urgent examinations and make decision for treatment support.

They used a deep residual UNet  to evaluate automated, volume-level pneumothorax grading (i.e., labelling a volume whether a pneumothorax was present or not), and pixel-level classification (i.e., segmentation and quantification of pneumothorax), on a retrospective series of routine chest CT data.


 

April 13, 2020  (PNAS) 

Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts

https://doi.org/10.1073/pnas.1919176117

Nardus Mollentze and  Daniel G. Streicker

The authors report that variation in the frequency of zoonoses among animal orders can be explained without invoking special ecological or immunological relationships between hosts and viruses. They point to a need to reconsider current approaches aimed at finding and predicting novel zoonoses.

 

April 12, 2020  (Infect Genet Evol)

Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS

Liang K

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141629/

This paper reveals the spread rules of the three pneumonia: COVID-19, SARS and MERS, and then compares them. Stats analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days.

April 10, 2020 

Modeling the COVID-19 pandemic - parameter identification and reliability of predictions

Hackl, K.  

https://t.co/fs1E2pLvT0

This paper tries to identify the parameters in an epidemic model, the so-called SI-model, via non-linear regression using data of the COVID-19 pandemic. They attempt to estimate the reliability of predictions. They validate this procedure using data from China and South Korea and then we apply to predict for Germany, Italy and the United States.

April 9, 2020 (J Chem Inf Model)

A Community Letter Regarding Sharing Bimolecular Simulation Data for COVID-19

Rommie E. Amaro  and Adrian J. Mulholland

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00319

This letter highlights the urgent need to share methods, models and results openly and quickly to test findings, ensure reproducibility, test significance  and accelerate discovery. Sharing of data for COVID-19 applications will help connect scientists across the global biomolecular simulation community and to  improve collaboration.

April 8, 2020 (Int J Mol Sci)

Development of a Novel, Genome Subtraction-Derived, SARS-CoV-2-Specific COVID-19-nsp2 Real-Time RT-PCR Assay and Its Evaluation Using Clinical Specimens

Yip CC, Ho CC, Chan JF et al

https://www.mdpi.com/1422-0067/21/7/2574

The team developed a rapid, sensitive, SARS-CoV-2-specific real-time RT-PCR assay on COVID-19-nsp2. They tested on 96 SARS-CoV-2 and 104 non-SARS-CoV-2 coronavirus genomes and using their in-house program, GolayMetaMiner, four specific regions longer than 50 nucleotides in the SARS-CoV-2 genome were identified. Evaluation of the new assay using 59 clinical specimens from 14 confirmed cases showed 100% concordance with their previously developed COVID-19-RdRp/Hel reference assay.

April 8, 2020 (PNAS)

Phylogenetic network analysis of SARS-CoV-2 genomes

Peter Forster, Lucy Forster, Colin Renfrew and Michael Forster

https://www.pnas.org/content/early/2020/04/07/2004999117

The authors have found three main variants in a phylogenetic network analysis of 160 complete human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) genomes. The network reliably traces routes of infections for documented coronavirus disease 2019 (COVID-19) cases, indicating that the phylogenetic networks can be successfully used to help trace undocumented COVID-19 infection sources of the disease worldwide.

April 07, 2020  Patterns

COVID-19 Is a Data Science Issue

Sarah Callaghan

https://doi.org/10.1016/j.patter.2020.100022

This editorial highlights the important role of data science in this global publich health emergency. Data scientists should rise to the occasion and contribute to the solution. It has a very useful web resources.

April 2, 2020

Stochastic modeling and estimation of COVID-19 population dynamics

ttps://arxiv.org/abs/2004.00941

The authors describe a model of the development of the Covid-19 contamination of the population of a country or a region

April 2, 2020

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

https://arxiv.org/abs/2003.09871

The authors are developing an open access COVID-Net  to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and appropriate treatment to be given. 

 
 
 
 
 
 

May 29, 2020  (Front. Phys.)

Predicting COVID-19 Peaks Around the World

Constantino Tsallis and Ugur Tirnakli

https://www.frontiersin.org/articles/10.3389/fphy.2020.00217/full?utm_source=F-AAE&utm_medium=EMLF&utm_campaign=MRK_1342238_64_Physic_20200602_arts_A

Soon after the beginning of the pandemics, several studies analyzing the available data and employing different models and candidate functions started to be published. Most of them are interested in the behavior of total cases and fatality curves. These authors focus on the analysis of the active cases and deaths per day with mathematical models. They illustrate their predictions with tables and graphs.

 

May 22 2020  (CHEM)

Chemistry and Biology of SARS-CoV-2

Alexander Dömling,  Li Gao

https://www.sciencedirect.com/science/article/pii/S2451929420301959

 An overview is given on the current knowledge of the spread, disease course, and molecular biology of SARS-CoV-2. Yhe authors discuss potential treatment developments in the context of recent outbreaks, drug repurposing and the development timelines.

 

May 20 2020   (IRBM)

Deep Transfer Learning based Classification Model for COVID-19 Disease

Yadunath Pathak, Prashant Kumar Shukla,  AkhileshTiwari,  et al

https://www.sciencedirect.com/science/article/pii/S1959031820300993

In this study, the deep transfer learning model is used to classify COVID-19 infected patients by considering their chest CT images. The deep transfer learning model is trained on a benchmark open dataset of chest CT images.

 

May 19 2020  (Nature Climate Change)

Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement

Corinne Le Quéré, Robert B. Jackson, Matthew W. Jones, et al

https://www.nature.com/articles/s41558-020-0797-x

During the pandemic many international borders were closed and populations were confined to their homes, which reduced transport and changed the consumption patterns. The authors compile government policies and activity data to estimate the decrease in CO2 emissions during stay-at-home. Daily global CO2 emissions decreased by about –17% by early April 2020 compared with the mean 2019 levels. At their peak, emissions in individual countries decreased by –26% on average. They comment that government actions and economic incentives after the crisis will likely influence the global CO2 emissions.

 

May 14 2020  (Nature)

Infection of dogs with SARS-CoV-2

Sit, T.H.C., Brackman, C.J., Ip, S.M. et al.

https://www.nature.com/articles/s41586-020-2334-5

Very little is known about the susceptibility of domestic pet animals to SARS-CoV-2. Two out of fifteen dogs from households with confirmed human cases of COVID-19 in Hong Kong SAR were found to be infected using quantitative RT–PCR, serology, sequencing the viral genome, and in one dog, virus isolation. The evidence so far suggests that these are instances of human-to-animal transmission of SARS-CoV-2. It is unclear whether infected dogs can transmit the virus to other animals or back to humans.

May 12, 2020  (Internet of Things)

Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing

ShreshthTuli, Shikhar Tuli, RakeshTuli et al

https://doi.org/10.1016/j.iot.2020.100222

The authors proposed a novel scheme to predict the impact of COVID-19 Pandemic. A model was designed  based on Cloud Computing and Machine Learning for real-time prediction. They claimed improved prediction accuracy compared to the baseline method.

 

 

May 8, 2020  (ACS Energy Lett)  

OVID-19, Climate Change, and Renewable Energy Research: We Are All in This Together, and the Time to Act Is Now

Song Jin

https://doi.org/10.1021/acsenergylett.0c00910

This editorial makes a plea for scientists and policy makes to act and work together in battling against the Covid-19 along with climate change and renewable energy. It is interesting read with the key message for us to act now before it is too late.

May 5, 2020  (Science)

Rapid implementation of mobile technology for real-time epidemiology of COVID-19

David A. Drew, Long H. Nguyen, Claire J. Steves et al.

https://doi.org/10.1126/science.abc0473

The authors share their work in establishing the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that was launched in the UK on March 24, 2020 and the USA on March 29, 2020 garnering more than 2.8 million users as of May 2, 2020. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

 

May 3, 2020  (Biology)

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

Jacques Demongeot , Yannis Flet-Berliac  and Hervé Seligmann

https://www.mdpi.com/2079-7737/9/5/94

The authors collected and analysed external temperature and new covid-19 cases in 21 countries and in the French administrative regions. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. They demonstrated  in the first stages of the epidemic, the velocity of contagion decreases with country- or region-wise temperature. The results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the pandemic remain unanswered.

March 2020

March 29, 2020   

Understanding the COVID19 Outbreak: A Comparative Data Analytics and Study

https://arxiv.org/abs/2003.14150

The authors present a comprehensive analytics visualization to address some research questions. This is the first systematic analytical paper that pave the way towards a better understanding of COVID-19. 

 

©2020 by Covid-19 Bibliometrics