Research Insights About Covid-19

We attempt to provide selected highlights in recent research findings

Last Update on 20 September 2020

B. Science and Engineering

Jun 2020

October 2020

Oct 15 2020  (Science of the Total Environment)

Spatial analysis and GIS in the study of COVID-19. A review

IvanFranch-Pardo, Brian M.Napoletano, Fernando Rosete-Vergesa et al

https://www.sciencedirect.com/science/article/pii/S0048969720335531

The authors review data processed with GIS and spatial statistics in  COVID-19 in order to understand and help us to make informed decision. The geographical information such as spatiotemporal dynamics of population and health geography data are interdisciplinary approaches in the study of COVID-19.

 

 

October 2020  (Chaos, Solitons & Fractals)

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions

Petrônio C.L.Silva, Paulo V.C.Batista, Hélder S. Lima et al

https://www.sciencedirect.com/science/article/pii/S0960077920304859

This interesting paper proposes the COVID-ABS, a SEIR (Susceptible-Exposed- Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. They identify seven different scenarios of social distancing interventions with varying epidemiological and economic effects. The seven scenarios are: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. They explore and discuss various scenario combinations and their respective outcomes.

 

 

October 2020 (Chaos, Solitons & Fractals)

Forecasting COVID-19 pandemic: A data-driven analysis

Khondoker Nazmoon Nabi

https://www.sciencedirect.com/science/article/pii/S0960077920304434

The authors propose  a  SEI_DI_UQHRD  compartmental mathematical model to understand the transmission dynamics of the COVID-19. They report the analysis for Brazil, Russia, India and Bangladesh. One finding is that quarantine is the most significant effect in controlling the disease outbreak.

September 2020

September 2, 2020 (Journal of Biological Rhythms)

Accounting for Time: Circadian Rhythms in the Time of COVID-19

Shaon Sengupta, Thomas G. Brooks, Gregory R. Grant et al.

https://doi.org/10.1177%2F0748730420953335

It is known that various aspects of our physiology and response to pathogens are controlled by the strict biological clock. However, we still have limited knowledge of applying circadian biology in our clinical and research practices. They discuss how circadian biology may improve our diagnostic and therapeutic strategies using a focused review of the literature and original analyses of the UK Biobank data. The biological clock may be relevant to the pathophysiology and treatment of the COVID-19.

September 2, 2020 (Biocybernetics and Biomedical Engineering)

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

Bejoy Abraham, Madhu S. Nair.

https://doi.org/10.1016/j.bbe.2020.08.005

Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The authors use a combination of features extracted from multi-CNN with correlation-based feature selection technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and they claimed promising results on both the datasets. This study demonstrates the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

 

 

September 1, 2020 (The Lancet Digital Health)

Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis

Nishant Kishore, Matthew V. Kiang, Kenth Engo-Monsen et al.

https://doi.org/10.1016/S2589-7500(20)30193-X

 There is an increased interest in the use of mobility data from mobile phones to monitor physical distancing and model the spread of COVID-19. There is a need to standardise data formats. The authors study aggregation principles and procedures for different mobile phone data streams and describe a common syntax for research and policy. They also mention the issues of privacy and data protection.

Sep 2020  (Physics of Fluids)

Visualizing droplet dispersal for face shields and masks with exhalation valves

Siddhartha Verma, Manhar Dhanak, John Frankenfield

https://doi.org/10.1063/5.0022968

This is a rigorous scientific research on the physics of droplet spread. There is an increasing trend of public substituting surgical masks with clear plastic face shields and with masks equipped with exhalation valves. However, there is a concern that widespread public use of these alternatives to regular masks could hinder the mitigation efforts. Verma el al use qualitative visualizations to examine the performance of face shields and exhalation valves in impeding the spread of aerosol-sized droplets. The visualizations indicate that although face shields block the initial forward motion of the jet, the expelled droplets can move around the visor with relative ease and spread out over a large area disturbance. Visualizations for a mask equipped with an exhalation port indicate that a large number of droplets pass through the exhale valve unfiltered, which significantly reduces its effectiveness as a means of source control. The findings provide strong evidence to support the use of high quality cloth or surgical masks that are of a plain design, instead of face shields and masks equipped with exhale valves. View several excellent visualisation of the droplet spread with the use of various masks here. https://aip.scitation.org/doi/figure/10.1063/5.0022968

Sep 2020  (Chaos, Solitons & Fractals)

Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19

Yong Zhang, Xiangnan Yu, Hong Guang Sun

https://www.sciencedirect.com/science/article/pii/S0960077920303581

Fractional calculus comes into action. The authors introduce fractional model to characterize the pattern of COVID-19 death. They then use a time-dependent SEIR model for fitting and prediction. Several models are attempted, such as the bi- molecular reaction method to evaluate the success of COVID-19 mitigation.

Sep 2020  (International Immunopharmacology)

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

Abhirup Banerjee, Surajit Ray, Bart Vorselaars et al

https://www.sciencedirect.com/science/article/pii/S1567576920315770

The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The authors reported that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). 

 

Sep 2020 (Journal of Systems Architecture)

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

Abu Sufian, Anirudha Ghosh, Ali Safaa Sadiq

https://www.sciencedirect.com/science/article/pii/S1383762120301223

The authors present a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing (EC) to mitigate COVID-19. They survey existing DL, DTL,  EC and Dataset to mitigate the pandemics with potentials and challenges. They also point out that a shortage of reliable datasets of an ongoing pandemic is a common problem.

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

August 27, 2020 (Scientific Data)

A structured open dataset of government interventions in response to COVID-19

Amélie Desvars-Larrive, Elma Dervic, Stefan Thurner

https://doi.org/10.1038/s41597-020-00609-9

The authors develop a hierarchical coding scheme for non-pharmaceutical interventions to generate a comprehensive structured dataset of government interventions and their respective timelines of implementation. They share information sources via an open library and provide codes. This dataset provides an in-depth insight into the government strategies and could be a valuable tool for developing relevant preparedness plans.

 

August 26, 2020 (Diabetes & Metabolic Syndrome: Clinical Research & Review)

Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls

Elliot Mbunge

https://www.sciencedirect.com/science/article/pii/S1871402120303325

Integrating emerging technologies into COVID-19 contact tracing is regarded as a good option for policymakers, health practitioners and IT personnel in mitigating the spread of a pandemic. The authors analyze possible opportunities and challenges of integrating emerging technologies into COVID-19 contact tracing. A literature search reviews applications such as GPS, Wi-Fi, Bluetooth, social graph and card transaction data have been used to track users. However, issues with security and privacy of people are of concern.

 

 

August 24, 2020 (Proceedings of the National Academy of Sciences)

A network-based explanation of why most COVID-19 infection curves are linear

Stefan Thurner, Peter Klimek, Rudolf Hanel

https://doi.org/10.1073/pnas.2010398117

In general, the COVID-19 infection curves reveal a linear growth over extended periods. This observation is almost impossible to understand using traditional epidemiological models. One reason is that they ignore the structure of real contact networks that are essential in the dynamics of COVID-19.  Further, the authors show the effect of non-pharmaceutical interventions such as lockdowns, could be modelled with high precision. They also question the applicability of standard compartmental models to describe the COVID-19 containment phase.

 

August 21, 2020 (JAMA Netw. Open)

Modeling Contact Tracing Strategies for COVID-19 in the Context of Relaxed Physical Distancing Measures

Alyssa Bilinski, Farzad Mostashari, Joshua A. Salomon

https://doi.org/10.1001/jamanetworkopen.2020.19217

This mathematical modeling study examines the potential for contract tracing to reduce the spread of SARS-CoV-2 in the context of reduced physical distancing under different assumptions for case detection, tracing, and quarantine efficacy.


 

August 18, 2020 (The Lancet Inf. Diseases)

Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study

Nicholas C. Grassly, Margarita Pons-Salort, Edward P K Parker et al.

https://doi.org/10.1016/S1473-3099(20)30630-7

The authors aim to investigate the potential impact of different testing and isolation strategies on transmission of SARS-CoV-2 They develop a mathematical model of SARS-CoV-2 transmission based on infectiousness and PCR test sensitivity over time since infection and report their findings in this paper.

August 18, 2020 (Safety Science)

A new model for the spread of COVID-19 and the improvement of safety

Costas A. Varotsos, Vladimir F. Krapivin

https://doi.org/10.1016/j.ssci.2020.104962

This study develops a method for diagnosing and predicting the COVID-19 spread and to evaluate the effectiveness of control measures to reduce and stop the spread. The COVID-19 Decision-Making System (CDMS) was developed to study disease transmission. The simulation experiments have shown a good agreement between the CDMS estimates and the data reported in Russia and Greece. The analysis showed that the instability indicator may be the precursor to the pandemic dynamics. They predicted three potential countries for a second wave: USA, Russia and Brazil.

 

 

August 16, 2020 (Rendiconti Lincei. Scienze Fisiche e Naturali)

Biological fluid dynamics of airborne COVID-19 infection

Giovanni Seminara, Bruno Carli, Guido Forni et al.

https://doi.org/10.1007/s12210-020-00938-2

We need to understand the relevant biological fluid dynamics to allow us to evaluate the contrasting effects of natural or forced ventilation of environments on the transmission of contagion, Seminara et al review the bio-fluid dynamic mechanisms involved in the transmission of the infection from SARS-CoV-2. Airborne virus transmission is by viral particles released by an infected person via coughing, sneezing, speaking or simply breathing. Speech droplets are considered for their viral load and potential for infection. They conclude that the risk decreases as the viral load are diluted by mixing effects but contagion could reach larger distances from the infected source.

 

 

August 14, 2020 (The Mathematical Intelligencer)

Are Models Useful? Reflections on Simple Epidemic Projection Models and the Covid-19 Pandemic

Marc Artzrouni

https://doi.org/10.1007/s00283-020-09997-7

“Prediction is very difficult, especially if it’s about the future” is a quotation uttered by Niels Bohr, the Nobel laureate Danish physicist. “All models are wrong, but some are useful” by statistician George Box. These quotations hold, especially for epidemiological modelling. The authors introduce a few epidemic projection models and compartmental models to capture the demographic dynamics of an infected population. They then introduce a novel variant of these models to fit data from China and the United States. However, these questions remain, “Why are epidemiological predictions so difficult, and how could we reconcile scepticism with the fact that projection models may be useful despite being wrong?”

 

 

August 13, 2020 (Computational Mechanics)

Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study

Alex Viguerie, Alessandro Veneziani, Guillermo Lorenzo et al.

https://doi.org/10.1007/s00466-020-01888-0

The COVID-19 has led to a resurgence in interest in the mathematical modelling epidemics research. In this paper, the authors propose a formulation of compartmental models based on partial differential equations. They then proceed to focus on a compartmental model to analyze mathematically with several results on its stability and sensitivity.

 

 

August 13, 2020 (Postdigital Science and Education)

Covid-19: When Species and Data Meet

Catherine Price

https://doi.org/10.1007/s42438-020-00180-x

How humans and the COVID-19 virus meet? Price attempts to offer an answer to two questions: How do humans, COVIDd-19, and contact-tracing apps meet and intra-act? What are the social justice issues and problems associated with contact-tracing apps? Price curates data from the National Health Service (NHS) app. She explains how the coming together of humans, biological-more-than-human-worlds and the digital can be considered a postdigital hybrid assemblage.

 

August 7, 2020 (Science Advances)

Low-cost measurement of facemask efficacy for filtering expelled droplets during speech

Emma P. Fischer, Martin C. Fischer, David Grass et al.

https://doi.org/10.1126/sciadv.abd3083

The authors demonstrated a simple optical measurement method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. In proof-of-principle studies, they compared a variety of commonly available mask types and observed that some mask types approach the performance of standard surgical masks, while some mask alternatives, such as bandanas, offer very little protection. This inexpensive measurement setup can be built and operated by non-experts, allowing for rapid evaluation of mask performance during speech, sneezing or coughing.

August 5, 2020 (Journal of Science in Sport and Exercise)

Are Runners More Prone to Become Infected with COVID-19? An Approach from the Raindrop Collisional Model

Francisco J. Arias

https://doi.org/10.1007/s42978-020-00071-4

This paper applies physics and maths in trying to understand the behaviour of droplets produced by runners. Arias uses graphics to help the readers visualize the research problem and the methodology used. One widespread belief is that close runners, owing to the stronger exhalation, can be more prone to be infected with COVID-19 should the runner in front be infected. However, the samples are small meaning the findings cannot be generalized. Using the raindrop collisional model and computational fluid dynamics, Arias shows that the probability of collision with respiratory droplets does not always increase with the approaching velocity of the runner. Rather, there is a maximum velocity at which the efficiency of collision decreases.

 

 

August 4, 2020 (Scientific Reports)

A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time

Gang Xie

https://doi.org/10.1038/s41598-020-70091-1

Gang Xie develops a Monte Carlo simulation model to represent the COVID-19 spread dynamics. He performs simulations on the COVID-19 data reported for Australia and the United Kingdom. The model estimated that the number of active cases would peak around 29 March in Australia (≈ 1,700 cases) and around 22 April in the UK (≈ 22,860 cases). The results of the estimated COVID-19 reproduction numbers were consistent with the reported values. This simulation model was considered to be a useful decision making/what-if analysis tool, and for modelling any other infectious diseases that may arise.

Aug 4 2020  (Physics of Fluids)

The dispersion of spherical droplets in source–sink flows and their relevance to the COVID-19 pandemic: Physics of Fluids

C. P. Cummins, O. J. Ajayi,  F. V. Mehendale et al

https://aip.scitation.org/doi/10.1063/5.0021427

This is a great physics paper. The authors investigate the dynamics of spherical droplets in the presence of a source–sink pair flow field. They use the Maxey-Riley equation to study the dynamics of the droplets. Interesting findings:  small droplets cannot go further than a specific distance. Larger droplets can travel further from the source before getting pulled into the sink. The findings that such droplets have a very short range could help scientists in the interpretation of existing data on droplet dispersion. Further research is expected to shed more light in our understanding of this very important droplet dispersion phenomenon.

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 14 2020  (JMIR Medical Informatics)

The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic

Jiancheng Ye

https://medinform.jmir.org/2020/7/e19866/

In this viewpoint, the author argues that efforts are needed to treat critical patients, track and manage the health status of residents, and isolate suspected patients. The application of emerging health technologies and digital practices in health care, such as artificial intelligence, telemedicine or telehealth, mobile health, big data, 5G, and the Internet of Things, have become powerful tools to fight against the pandemic and provide strong support in pandemic prevention and control. He then highlights the applications of all these technologies, practices and health delivery services.

July 13, 2020 (Physical and Engineering Sciences in Medicine)

Technique, radiation safety and image quality for chest X-ray imaging through glass and in mobile settings during the COVID-19 pandemic

Zoe Brady, Heather Scoullar, Ben Grinsted et al.

https://doi.org/10.1007/s13246-020-00899-8

The authors developed a technique to perform mobile chest X-ray imaging through a glass, allowing the X-ray unit to remain outside of the patient’s room, effectively reducing the cleaning time associated with disinfecting equipment. The technique also reduced the infection risk of radiographers. Radiation measurements were performed to determine the appropriate position for staff inside and outside the room to ensure occupational doses were kept as low as reasonably achievable. Image quality was acceptable and technical parameter information collated. This method has been implemented successfully.

 

July 10, 2020 (The Lancet Digital Health)

Machine learning for COVID-19—asking the right questions

Patrik Bachtiger, Nicholas S Peters, Simon LF Walsh

Enthusiasm around around machine learning-based technology in medical imaging has been present even prior to the COVID-19 pandemic. During this pandemic, chest x-ray and CT have quickly produced a large amount of data on COVID-19, enabling the development of machine learning algorithms, a form of artificial intelligence (AI). However, the question remains as to how many of these applications will prove to be clinically useful. In this article, the authors discuss questions s that need to be answered whilst developing machine learning algorithms.

 

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.

July 10, 2020  (The Lancet Haematology)

Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study

Danying Liao, Fen Zhou, Lili Luo et al.

https://doi.org/10.1016/S2352-3026(20)30217-9

Changes in haematological characteristics in patients with COVID-19 are emerging as important features of the disease. In this retrospective study, the authors explored the haematological characteristics and related risk factors in patients with COVID-19. They found that rapid blood tests, including platelet count, prothrombin time, D-dimer, and neutrophil to lymphocyte ratio can help clinicians to assess severity and prognosis of patients with COVID-19. The sepsis-induced coagulopathy scoring system can be used for early assessment and management of patients with critical disease.

 

July 10, 2020  (JAMA)

Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review

W, Joost Wiersinga, Andrew Rhodes, Allen C. Cheng et al.

https://doi.org/10.1001/jama.2020.12839

This comprehensive and up-to-date review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19.

July 8, 2020  (JAMA Netw. Open)

Association of a Public Health Campaign About Coronavirus Disease 2019 Promoted by News Media and a Social Influencer With Self-reported Personal Hygiene and Physical Distancing in the Netherlands

Hamza Yousuf, Jonathan Corbin, Govert Sweep et al.

https://doi.org/10.1001/jamanetworkopen.2020.14323

This survey study examines a nationwide social media campaign about personal hygiene and physical distancing in the Netherlands and evaluates its effectiveness in improving behaviour and curbing the spread of the coronavirus disease 2019 (COVID-19) pandemic.

 

July 6, 2020 (Clin Inf Dis)

It is Time to Address Airborne Transmission of COVID-19 

Lidia Morawska, Donald K Milton

https://doi.org/10.1093/cid/ciaa939

The authors review existing evidence of transmission of COVID-19 and are of the viewpoint that it is important to recognize the potential for airborne spread of COVID-19.

 

July 6, 2020  (JAMA)

Developing a SARS-CoV-2 Vaccine at Warp Speed

Kevin P. O’Callaghan, Allison M. Blatz, Paul A. Offit

https://doi.org/10.1001/jama.2020.12190

In this Viewpoint, we describe the proposed mechanisms and current status of each of these leading candidates, all of which are aimed at inducing antibodies directed against the receptor-binding domain of the surface spike (S) protein of SARS-CoV-2.

 

July 3, 2020  (Cell)

Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear

Nathan D. Grubaugh, William P. Hanage, Angela L. Rasmussen

https://doi.org/10.1016/j.cell.2020.06.040

Korber et al. (2020) found that a SARS-CoV-2 variant in the spike protein, D614G, rapidly became dominant around the world. While clinical and in vitro data suggest that D614G changes the virus phenotype, the impact of the mutation on transmission, disease, vaccine and therapeutic development are largely unknown. Here the authors try to answer questions on the potential impacts, if any, that D614G has on the COVID-19 pandemic.

 

July 3, 2020  (Science)

SARS-CoV-2 productively infects human gut enterocytes

Mart M. Lamers, Joep Beumer, Jelte van der Vaart et al.

https://doi.org/10.1126/science.abc1669

SARS-CoV-2 causes an influenza-like disease with a respiratory transmission route;, however, patients often present with gastrointestinal symptoms such as diarrhoea. Lamers et al. used human intestinal organoids, a “mini-gut” cultured in a dish, to demonstrate that SARS-CoV-2 readily replicates in an abundant cell type in the gut lining—the enterocyte—resulting in the production of large amounts of infective virus particles in the intestine. This work demonstrates that intestinal organoids can serve as a model to understand SARS-CoV-2 biology and infectivity in the gut.

 

July 3, 2020  (The Lancet Haematology)

Effects of the COVID-19 pandemic on supply and use of blood for transfusion

Simon J Stanworth, Helen V New, Torunn O Apelseth et al.

https://doi.org/10.1016/S2352-3026(20)30186-1

The COVID-19 pandemic has major implications for blood transfusion. The authors systematically searched for relevant studies addressing the transfusion chain—from donor, through collection and processing, to patients—to provide a synthesis of the published literature and guidance during times of potential or actual shortage.

 

July 2, 2020 (The Lancet Child & Adolescent Health)

Emergence of Kawasaki disease related to SARS-CoV-2 infection in an epicentre of the French COVID-19 epidemic: a time-series analysis

Naim Ouldali, Marie Pouletty, Patricia Mariani et al.

https://doi.org/10.1016/S2352-4642(20)30175-9

Kawasaki disease is an acute febrile systemic childhood vasculitis, which is suspected to be triggered by respiratory viral infections. The authors examined whether the ongoing COVID-19 epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with an increase in the incidence of Kawasaki disease.

 

July 2, 2020  (JAMA Neurology)

Risk of Ischemic Stroke in Patients With Coronavirus Disease 2019 (COVID-19) vs Patients With Influenza

Alexander E. Merkler, Neal S. Parikh, Saad Mir et al.

https://doi.org/10.1001/jamaneurol.2020.2730

This cohort study compares the rate of ischemic stroke among patients with COVID-19 vs patients with influenza in 2 hospitals in New York City, New York. They found that patients with COVID-19 appear to have a heightened risk of acute ischemic stroke compared with patients with influenza.

 

July 2, 2020  (Science)

Primary exposure to SARS-CoV-2 protects against reinfection in rhesus macaques

Wei Deng, Linlin Bao, Jiangning Liu et al.

https://doi.org/10.1126/science.abc5343

As the COVID-19 pandemic evolves, there are still many questions we need to answer. Currently, it remains unclear whether convalescing patients have a risk of reinfection. The authors generated a rhesus macaque model of SARS-CoV-2 infection that was characterized by interstitial pneumonia and systemic viral dissemination mainly in the respiratory and gastrointestinal tracts. Rhesus macaques reinfected with the identical SARS-CoV-2 strain during the early recovery phase of the initial SARS-CoV-2 infection did not show detectable viral dissemination, clinical manifestations of viral disease, or histopathological changes. Comparing the humoral and cellular immunity between primary infection and rechallenge revealed notably enhanced neutralizing antibody and immune responses. These results suggest that primary SARS-CoV-2 exposure protects against subsequent reinfection in rhesus macaques.

July 2 2020  (PNAS)

The challenges of modeling and forecasting the spread of COVID-19

 Andrea L. Bertozzi, Elisa Franco, George Mohler,et al

https://www.pnas.org/content/early/2020/07/07/2006520117

Modeling and forecasting the spread of COVID-19 remain a challenge. In this paper the authors  present three models for forecasting and assessing the course of the pandemic. They aim to demonstrate the utility of these models for understanding the pandemic and to provide a framework for generating policy-relevant insights into its course. These models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or therapeutic agent.

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 1, 2020  (JAMA Psychiatry)

Telehealth for Substance-Using Populations in the Age of Coronavirus Disease 2019: Recommendations to Enhance Adoption

Lewei (Allison) Lin, Anne C. Fernandez, Erin E. Bonar

https://doi.org/10.1001/jamapsychiatry.2020.1698

This Viewpoint discusses the need for and implementation of telemedicine for patients with substance use disorder in the era of coronavirus disease 2019.

 

July 1, 2020  (JAMA Network Open)

Prevalence of and Risk Factors Associated With Mental Health Symptoms Among the General Population in China During the Coronavirus Disease 2019 Pandemic

Le Shi, Zheng-An Lu, Jian-Yu Que, et al.

https://doi.org/10.1001/jamanetworkopen.2020.14053

This survey involving more than 50,000 participants estimated the prevalence of depression and anxiety, and also looked at the risk factors associated with mental health symptoms. The mental health burden associated with COVID-19 is considerable among the general population of China, suggesting that mental health interventions are in urgent demand during the COVID-19 pandemic, especially for some at-risk populations.

 

 

July 1, 2020  (Heliyon)

A one-step, one-tube real-time RT-PCR based assay with an automated analysis for detection of SARS-CoV2

Bhasker Dharavath, Neelima Yadav, Sanket Desai et al.

https://doi.org/10.1016/j.heliyon.2020.e4405

The authors present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories.

July 2020  (Infectious Disease Modelling)

Generalized logistic growth modeling of the COVID-19 pandemic in Asia

Elinor Aviv-Sharon, Asaph Aharoni

https://www.sciencedirect.com/science/article/pii/S2468042720300270

The authors report a modeling approach using the generalized logistic model (GLM) to predict the outbreak spreading potential and the pandemic cessation dates in Chinese mainland, Iran, the Philippines and Chinese Taiwan. The short-term predicted number of cumulative COVID-19 cases matched the confirmed reports of across the four countries and regions.  They suggest that GLM as a valuable tool for characterizing the transmission dynamics process and the trajectory of COVID-19 pandemic.

June 2020

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.

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.

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.

 

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  (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.

 

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 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.

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.

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.

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.

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.

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.

May 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 7, 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 7, 2020 (Maturitas)

COVID-19: The forgotten priorities of the pandemic

Cristina Mesa Vieira, Oscar H. Franco, Carlos Gomez Restrepo, Thomas Abel

https://doi.org/10.1016/j.maturitas.2020.04.004

This article has been awarded the Editor’s choice for the June edition of Maturitas. In the paper, the authors describe some implications of social distancing that can be detrimental to people’s mental health, especially of those who do not have an extensive support network.

 

April 3, 2020 (Asian J of Psychiatry)

Issues relevant to mental health promotion in frontline health care providers managing quarantined/isolated COVID19 patients

Ritin Mohindra, Ravaki R, Vikas Suri et al.

https://doi.org/10.1016/j.ajp.2020.102084

The authors conducted interviews with health care providers managing COVID-19 patients to find out the perceived motivations influencing morale. They identified three themes: positive Motivational factors (that need to be strengthened), Negatives, frustrations associated with patient care, and personal fears and annoyances experienced by doctors. They present their findings with a view to disseminate so that hospitals facing or preparing for COVID-19 can factor in these issues.

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. 

 
 
 
 
 
 

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. 

 

May 29, 2020 (Chaos, Solitons & Fractals)

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

H. Swapnarekha Himansu, Sekhar Behera, Janmenjoy Nayak, Bighnaraj Naik

https://doi.org/10.1016/j.chaos.2020.109947

The authors perform a review on various intelligent computing based research for COVID-19, analysing the limitations of predication based models for COVID-19. They also discuss the impact of clinical data and online data for COVID-19 research, focusing on advanced intelligent systems on symptom- based identification of COVID-19.

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 28, 2020 (European Radiology)

Any unique image biomarkers associated with COVID-19?

Pu, J., Leader, J., Bandos, A. et al. 

https://doi.org/10.1007/s00330-020-06956-w

The authors compared CT scans of COVID-19 and Non-COVID19 community acquired pneumonia patients to define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. They found that unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizeable subset of non-COVID-19 cases.

 

 

May 27, 2020 (Malaysian Orthopaedic Journal)

N95 Filtering Facepiece Respirators during the COVID-19 Pandemic: Basics, Types, and Shortage Solutions

Srinivasan S, Peh WCG

http://www.morthoj.org/2020/v14n2/N95-covid-19.pdf

There is much global concern about protective measures for health care professionals, particularly those performing surgery or other procedures with close patient contact during this COVID-19 pandemic. Here the authors discuss N95 respirators and the additional role of powered air-purifying respirators (PAPR) is also discussed.

 

May 22, 2020 (JAMA)

Translating Science on COVID-19 to Improve Clinical Care and Support the Public Health Response

Carlos del Rio, Preeti Malani

https://doi.org/10.1001/jama.2020.9252

The authors summarize the flood of communication about the most important aspects of the COVID-19-pandemic published in the last five months. 

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 18, 2020 (EMJ)

The COVID-19 Conundrum and Cancer - Making Perfect Sense of Imperfect Data

Utkarsh Acharya

https://doi.org/10.33590/emj/200518

The author reviews current available evidence of COVID-19 and cancer. He discusses the epidemiological data, COVID-19 and cancer therapies and also identifies current gaps in knowledge with regards to COVID-19 and cancer.

 

May 18, 2020 (JAMA)

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV)

An Unprecedented Partnership for Unprecedented Times

Francis S. Collins, Paul Stoffels

https://doi.org/10.1001/jama.2020.8920

In this viewpoint, the authors describe a partnership involving all sectors of society to work together to address COVID-19.

 

 

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

Shreshth Tuli, Shikhar Tuli, Rakesh Tuli et. al.

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

Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the COVID-19 disease, predict growth of the epidemic and design strategies and policies to manage its spread. This study applies an improved mathematical model based on Cloud Computing and ML to analyse and predict the growth of the epidemic. Their method showed improved prediction accuracy compared to baseline. They also highlight key future research directions and emerging trends.

May 12, 2020 (Int J of Inf Dis)

A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions

Gabriel Rainisch, Eduardo A. Undurraga, Gerardo Chowell

https://doi.org/10.1016/j.ijid.2020.05.043

The authors developed a tool to estimate healthcare demand stemming from the COVID-19 pandemic. They applied this model to three different regions in Chile to illustrate its use and describe their findings in this article. This tool is able to help local authorities examine the impacts of intervention strategies.

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.

April 2020

 
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