Abstract
The COVID-19 pandemic has killed more than 400 thousand and infected more than 7 million people in the whole world as of 06/10/2020. Many open systems, such as educations, transportations, entertainments, sports or foods, have been completely or partially locked down in many regions of the world to prevent COVID-19 spreading. Therefore, how to reopen all of the open systems after the shutdown has become a world urgent issue. Evidences of COIVD-19 have showed: person-to-person transmission occurs among close contacts; virus droplets (or aerosols) play an important role in the transmission; people can prevent COVID-19 by measures of mask-wearing or maintaining social distancing. However, how these measures work to prevent COVID-19 is still not clear in a perspective of biomedical Infophysics. In this study, (1) we think the transmission of infection can be accomplished by real or virtual person; (2) we define a virtual person as ghost airflow, a term we coin to describe virus droplets (or aerosols) that are initially generated by human carriers and remain in the air to be transmitted. These ghost airflows are as dangerous as the real virus carriers particularly in enclosed (confined) environments; (3) we propose biomedical infophysical models (BMIPM) of filtering the ghost airflows by mask-wearing and maintaining social distancing, to help people to understand the filtering mechanisms and willingly follow the guidelines of preventing covid-19, and therefore to successfully reopen all of the open systems after the shutdowns (lockdowns) and (or) to avoid the shutdowns (lockdowns) in future epidemics or pandemics. Significantly, we compare the prevention efficiencies of COVID-19 between people who are accustomed and not accustomed to masks based on the published WHO, CDC or NHC pandemic data. The compared results support our models in this investigation. Coronaviruses easily survive and have high toxicity, in dirty, wet and cold environments, and the air pollution is linked with higher COVID-19 death rates. The dingy environments and air-conditioning, freezing systems sufficiently provide such necessary dirty, wet and cold conditions and polluted airflows to exacerbate the mortality rate of COVID-19. Therefore, we strongly suggest: to use air conditioners as less as possible, to turn the wind levels as low as possible and to clean (disinfecting) the air-conditioning systems (filters and channels) and environments as frequent as possible.
Highlights
COVID-19 pandemic has killed more than 400 thousand and infected about 7 million of people in the whole world as of 06/10/2020 [1]
In this study, (1) we think the transmission of infection can be accomplished by real or virtual person; (2) we define a virtual person as ghost airflow, a term we coin to describe virus droplets that are initially generated by human carriers and remain in the air to be transmitted. These ghost airflows are as dangerous as the real virus carriers in enclosed environments; (3) we propose biomedical infophysical models (BMIPM) of filtering the ghost airflows by mask-wearing and maintaining social distancing, to help people to understand the filtering mechanisms and willingly follow the guidelines of preventing covid-19, and to successfully reopen all of the open systems after the shutdowns and to avoid the shutdowns in future epidemics or pandemics
Evidences of COIVD-19 have showed that person-to-person transmission occurs among close contacts and the virus droplets play an important role in the transmission [3,4,5,6]
Summary
COVID-19 pandemic has killed more than 400 thousand and infected about 7 million of people in the whole world as of 06/10/2020 [1]. We propose biomedical infophysical models (BMIPM) of filtering ghost airflows (GAF) through mask-wearing and maintaining social distancing, to help people to understand the filtering mechanisms and willingly follow WHO or CDC guidelines of preventing covid-19, and to successfully reopen the systems after the shutdowns (lockdowns) and (or) to avoid the shutdowns (lockdowns) in future epidemics or pandemics. We compare the prevention efficiencies of COVID-19 between people who are used to and not used to wearing masks, based on published WHO [1], CDC [2] or NHC [8] pandemic data The results from this comparison support our models; we believe our models are applicable (suitable) to prevent other respiratory infectious diseases
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