Research Insights About Covid-19

We attempt to provide selected highlights in recent research findings

Last Update on 1 December 2020

B. Science and Engineering 

B. Science and Engineering 

August 2020

August 27, 2020 (Energy)

The energy and environmental footprints of COVID-19 fighting measures – PPE, disinfection, supply chains

Jiří Jaromír Klemeš, Yee Van Fan, Peng Jiang

https://doi.org/10.1016/j.energy.2020.118701

The pandemic has a great impact on energy structure, requirements and related emissions. However, as the pandemic continues, the impacts on energy and environment should be assessed and  reduced.

This study provides an overview of energy sources and environmental footprints in fighting the pandemic. The required energy and resources consumption of Personal Protection Equipment (PPE) and testing kits have been discussed. They conclude that with a proper design standard, material selection and user guideline, reusable PPE could be an effective option with lower energy consumption. 

 

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 10, 2020 (Nonlinear Dynamics)

The COVID-19 pandemic: model-based evaluation of non-pharmaceutical interventions and prognoses

Alex De Visscher

https://doi.org/10.1007/s11071-020-05861-7

De Visscher develops an epidemiological model for COVID-19 for use by public health practitioners, policymakers, and the general public. The model distinguishes four stages in the disease: infected, sick, seriously sick, and better. The model assumes a case mortality rate of 1.5%. Preliminary simulations model indicate that concepts such as “herd immunity” and containment (“flattening the curve”) are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R0 of the virus below 1 is effective. The model is illustrated with the cases of Italy, France, and Iran and can describe the number of deaths as a function of time in all these cases.

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 6, 2020 (Nonlinear Dynamics)

COVID-19: data-driven dynamics, statistical and distributed delay models, and observations

Xiaobo Liu, Xie Zheng, Balakumar Balachandran

https://doi.org/10.1007/s11071-020-05863-5

A generalized logistic function model and extended compartmental models have been developed to study the responses and distributions of infection. The role of data is discussed as to how the compartmental model can be used to capture responses to various measures such as quarantine. Data for different parts of the world are considered and compared.

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.