Research Insights About Covid-19

We attempt to provide selected highlights in recent research findings

Last Update on 1 December 2020

May 2020

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.

B. Science and Engineering 

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