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A supportive electrostatic model of the COVID-19 airborne transmission
- Source :
- International Journal of Modeling, Simulation, and Scientific Computing
- Publication Year :
- 2020
- Publisher :
- World Scientific Pub Co Pte Lt, 2020.
-
Abstract
- A mechanism-oriented model is proposed here as a speculative but robust attempt to understand whether there might be any increased risk of electrostatically induced contamination, with relevant consequences from the epidemiological viewpoint. This could also be the case for the COVID-19 spreading because an amount of micro-sized droplet nuclei, often carrying net electric charge, are expected to be electro-dynamically involved in a physical process originated by the natural and unperceivable static electrification of human beings. The effects of the triboelectric charging have long been successfully tested because the phenomenon under examination is also implied in the genesis of the electrostatic discharge (ESD), a demanding key objective in the special context of electromagnetic compatibility (EMC). Therefore, the ultimate purpose of this technical paper is to provide valuable insights into infection control, building on what is already being done for maintaining static-safe environments. The stature of the applied model can be further appreciated because some currently observed climate-dependent and sex-linked different vulnerabilities to COVID-19 are critically examined by unique sound arguments. These ultimately focus attention on ambient relative humidity and worn shoes, the latter differing for typology, size and material, for their integrated control of the inadvertent human aptitude to buildup tribocharges. These would appear as a dreadful prerequisite for charge bearing droplets in the airborne state to be efficiently attracted/repelled according to the described electrostatic mechanism.
- Subjects :
- 2019-20 coronavirus outbreak
010504 meteorology & atmospheric sciences
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Contamination
01 natural sciences
Virology
Airborne transmission
Computer Science Applications
Human-body model
03 medical and health sciences
0302 clinical medicine
Increased risk
Modelling and Simulation
Modeling and Simulation
Environmental science
030217 neurology & neurosurgery
Electrostatic model
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 17939615 and 17939623
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- International Journal of Modeling, Simulation, and Scientific Computing
- Accession number :
- edsair.doi.dedup.....b2bdacd836bfc6428bc7c9625db8185e