October 29, 2020 (Applied Soft Computing)

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Anunay Gupta, Anjum, Shreyansh Gupta et al.

https://doi.org/10.1016/j.asoc.2020.106859

In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities in chest X-ray images. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification and an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification.

 

 

October 28, 2020 (Computers in Biology and Medicine)

The importance of standardisation – COVID-19 CT & Radiograph Image Data Stock for deep learning purpose

Krzysztof Misztal, Agnieszka Pocha, Martyna Durak-Kozica et al.

https://doi.org/10.1016/j.compbiomed.2020.104092

The authors propose a new dataset, COVID-19 CT and Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. They aim to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from pneumonia and healthy chest. They hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models.

 

 

October 25, 2020 (Journal of Telemedicine and Telecare)

Impact of a large-scale telemedicine network on emergency visits and hospital admissions during the coronavirus disease 2019 pandemic in Brazil: Data from the UNIMED-BH system

Bruno R Nascimento, Luisa CC Brant, Ana Cristina T Castro et al.

https://doi.org/10.1177/1357633X20969529

This paper is on assessing the impact of a large-scale COVID-19 telemedicine system on emergency department visits and all-cause and cardiovascular hospital admissions in Brazil. They evaluated the database of cooperative private health insurance. The COVID-19 telemedicine system consisted of mobile app, which redirects to teleconsultations if indicated;  telemonitoring system, with regular phone calls to COVID-19 cases to monitor progression; and emergency ambulance system with internet phone triage and counselling. The most frequently utilized tool was telemonitoring, followed by teleconsultation.

 

 

October 22, 2020 (Materials Chemistry and Physics)

Review on 3D printing: Fight against COVID-19

Bankole I. Oladapo, Sikiru O. Ismail, Temitope D. Afolalu et al.

https://doi.org/10.1016/j.matchemphys.2020.123943      

This is a review of the work done on solving COVID-19 with 3D printing. The use of bio-macromolecules antiviral respiratory assistance and other medical devices have improved the survival rate. A bio-cellular face shield with relative comfortability made of bio-macromolecules polymerized polyvinyl chloride (BPVC) and other biomaterials produced with 3D printers are described.

 

 

October 22, 2020 (Materials Chemistry and Physics)

Review on 3D printing: Fight against COVID-19

Bankole I. Oladapo, Sikiru O. Ismail, Temitope D. Afolalu et al.

https://doi.org/10.1016/j.matchemphys.2020.123943

This study reviews work done on solving COVID-19 with 3D printing. The researchers printed a bio-cellular face shield with relative comfortability made of bio-macromolecules polymerized polyvinyl chloride (BPVC) and other biomaterials are produced with 3D printers. They concluded that this could reduce effects on the economy.

 

 

October 22, 2020 (Applied Mathematical Modelling)

Global analysis of the COVID-19 pandemic using simple epidemiological models

Jose Enrique Amaro, Jeremie Dudouet, Jose Nicholas Orce et al.

https://doi.org/10.1016/j.apm.2020.10.019

Several analytical models have been developed in this work to describe the evolution of fatalities arising from coronavirus COVID-19 worldwide. The Death or ‘D’ model is a simplified version of the SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery. The D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution. More accurate calculations using the extended SIR or ESIR model and Monte Carlo grid simulations predict similar trends. These results suggest a common pandemic evolution with universal parameters. The fact that the D and ESIR models predict similar results shows that COVID-19 is a highly contagious virus, but that most people become asymptomatic (D model) and eventually recover (ESIR model).

 

 

October 22, 2020 (Materials Chemistry and Physics)

Review on 3D printing: Fight against COVID-19

Bankole I. Oladapo, Sikiru. O. Ismail, Temitope D. Afolalu et al.

https://doi.org/10.1016/j.matchemphys.2020.123943

The authors review the work on solving COVID-19 with 3D printing. Many patients who need to be hospitalized because of COVID-19 can only survive on bio-macromolecules antiviral respiratory assistance and other medical devices. A bio-cellular face shield made of bio-macromolecules polymerized polyvinyl chloride (BPVC) and other biomaterials are produced with 3D printers. Innovative adaptive manufacturing applications offers great potential.

 

 

October 20, 2020 (Chaos, Solitons & Fractals)

Is spread of COVID-19 a chaotic epidemic?

Andrew Jones & Nikolay Strigul

https://doi.org/10.1016/j.chaos.2020.110376

Yes, it is. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of the epidemic. To gain a deeper understanding of its behaviour, they turn to chaotic dynamics. they hypothesize that the unpredictability of the pandemic could be a fundamental property if the disease spread is a chaotic dynamical system. They conclude that the COVID-19 epidemic demonstrates chaotic behaviour, which should be taken into account by public policymakers. They stress that the scale and behaviour of the epidemic are essentially unpredictable due to the properties of chaotic systems.

 

 

October 19, 2020 (Environmental Pollution)

Links between air pollution and COVID-19 in England

Marco Travaglio, Yizhou Yu, Rebeka Popovic et al.

https://doi.org/10.1016/j.envpol.2020.115859

The authors studied links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. They compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. The results show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. They conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study serves as a basis to guide health and emissions policies.

 

 

October 15, 2020 (Results in Physics)

Study of global dynamics of COVID-19 via a new mathematical model

Rahim ud Din, Aly R. Seadway, Kamal Shah et al.

https://doi.org/10.1016/j.rinp.2020.103468

This paper focuses on the mathematical modeling and transmission mechanism of COVID-19 using a new type epidemic four-compartmental model that is susceptible, exposed, infected and recovered (SEIR), which describes the dynamics of COVID-19 under convex incidence rate. We simulate the results by using a nonstandard finite difference method (NSFDS).

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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.

 

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October 10, 2020 (Computers in Biology and Medicine)

Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19

Debmalya Barh, Sandeep Tiwari, Marianna E. Weener et al.

https://doi.org/10.1016/j.compbiomed.2020.104051

The authors use multi-omics (interactome, proteome, transcriptome, and bibliome) data to analyse the biological events associated with SARS-CoV-2 infection and identify several candidate drugs against this viral disease. Nearly 70% of the identified agents that are previously suggested to have anti-COVID-19 effects or under clinical trials. Among our identified drugs, the ones that are not yet tested, need validation with caution while an appropriate drug combination from these candidate drugs along with a SARS-CoV-2 specific antiviral agent is needed for effective COVID-19 management.

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October 8, 2020 (Computers in Biology and Medicine)

Multi-task deep learning-based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

Amine Amyar, Romain Modzelewski, Hua Li et al.

https://doi.org/10.1016/j.compbiomed.2020.104037

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, They propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. The model is evaluated and compared with other techniques using a dataset of 1369 patients. The results show a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.

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October 7, 2020 (Virology Journal)

The effect of temperature on persistence of SARS‑CoV‑2 on common surfaces

Shane Riddell, Sarah Goldie, Andrew Hill et al.

https://doi.org/10.1186/s12985-020-01418-7

This study measured the survival rates of infectious SARS-CoV-2, suspended in a standard ASTM E2197 matrix, on several common surface types. All experiments were carried out in the dark, to negate any effects of UV light. Inoculated surfaces were incubated at 20 °C, 30 °C and 40 °C and sampled at various time points. Their findings demonstrate SARS-CoV-2 can remain infectious for significantly longer periods than generally considered possible. These results could be used to inform improved risk mitigation procedures to prevent the fomite spread of COVID-19.

 

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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.

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