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Register a new userPersonal Ultraviolet Respiratory Germ Eliminating Machine (PUR$diamond$GEM) for COVID-19
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The current COVID-19 pandemic has highlighted the need for cheap reusable personal protective equipment. The disinfection properties of Ultraviolet (UV) radiation in the 200-300 nm have been long known and documented. Many solutions using UV radiation, such as cavity disinfection and whole room decontamination between uses, are in use in various industries, including healthcare. Here we propose a portable wearable device which can safely, efficiently and economically, continuously disinfect inhaled/exhaled air using UV radiation with possible 99.99% virus elimination. We utilize UV radiation in the 260 nm range where no ozone is produced, and because of the self-contained UV chamber, there would be no UV exposure to the user. We have optimized the cavity design such that an amplification of 10-50 times the irradiated UV power may be obtained. This is crucial in ensuring enough UV dosage is delivered to the air flow during breathing. Further, due to the turbulent nature of airflow, a series of cavities is proposed to ensure efficient actual disinfection. The Personal Ultraviolet Respiratory Germ Eliminating Machine (PUR$diamond$GEM) can be worn by people or attached to devices such as ventilator exhausts/intakes, or be used free-standing as a portable local air disinfection unit, offering modularity with multiple avenues of usage. Patent pending.
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In this article we detail the prototype development of the Personal Ultraviolet Respiratory Germ Eliminating Machine(PUR$diamond$GEM) to safely, efficiently and economically, continuously disinfect inhaled/exhaled air using Ultraviolet (UV) radiation with possible 99.99% virus elimination. The PUR$diamond$GEM consists of a series of UV disinfection chambers through which constant airflow is maintained via fans. A minimum air flow rate of $sim20-30$ L/min is sufficient to keep CO$_2$ levels $lesssim$ 0.5% in the hood/helmet. We validated that using easily available PTFE wrap, a factor of $sim 18$ enhancement in UV power can be obtained in our spherical chambers. Detailed analysis is presented for the air travel time distributions through the cavities, and the expectation value for actual pathogen elimination is computed. We provide the scaling of pathogen elimination with the number of cavities in series, reflective enhancement, UV source power, sphere radius and airflow rate. We show that disinfection greater than 4-log is achievable for a series of three or more spheres. We 3D printed our prototype, consisting of two spheres of 10 cm diameter each in series for each direction of disinfection. Using UVC LEDs emitting $sim$ 40 mW of power each with an airflow rate of 30 L/min, actual SARS-COV2 virus elimination of $sim$ 98% is expected. While not manually feasible to construct smaller spheres in the lab, smaller cavities can be commercially manufactured, leading to significantly higher actual pathogen elimination, as well as reducing fingerprint and cost of cavity manufacture. Patent pending.
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
Recent study reported that an aerosolised virus (COVID-19) can survive in the air for a few hours. It is highly possible that people get infected with the disease by breathing and contact with items contaminated by the aerosolised virus. However, the aerosolised virus transmission and trajectories in various meteorological environments remain unclear. This paper has investigated the movement of aerosolised viruses from a high concentration source across a dense urban area. The case study looks at the highly air polluted areas of London: University College Hospital (UCH) and King Cross and St Pancras International Station (KCSPI). We explored the spread and decay of COVID-19 released from the hospital and railway stations with the prescribed meteorological conditions. The study has three key findings: the primary result is that it is possible for the virus to travel from meters up to hundred meters from the source location. The secondary finding shows viruses released into the atmosphere from entry and exit points at KCSPI remain trapped within a small radial distance of < 50m. This strengthens the case for the use of face coverings to reduce the infection rate. The final finding shows that there are different levels of risk at various door locations for UCH, depending on which door is used there can be a higher concentration of COVID-19. Although our results are based on London, since the fundamental knowledge processes are the same, our study can be further extended to other locations (especially the highly air polluted areas) in the world.
The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, we develop an integrated network model, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Results show that, the shortage contagion patterns are mainly decided by top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. Besides, export restrictions lead to ineffective and inefficient allocation of PPE around the world, which has no benefits for the world to fight against the pandemic.
Breathing is vital to life. Therefore, the real-time monitoring of breathing pattern of a patient is crucial to respiratory rehabilitation therapies such as magnetic resonance exams for respiratory-triggered imaging, chronic pulmonary disease treatment, and synchronized functional electrical stimulation. While numerous respiratory devices have been developed, they are often in direct contact with a patient, which can yield inaccurate or limited data. In this study, we developed a novel, non-invasive, and contactless magnetic sensing platform that can precisely monitor breathing, movement, or sleep patterns of a patient, thus providing efficient monitoring at a clinic or home. A magneto-LC resonance (MLCR) sensor converts the magnetic oscillations generated by breathing of the patient into an impedance spectrum, which allows for a deep analysis of breath variation to identify respiratory-related diseases like COVID-19. Owing to its ultrahigh sensitivity, the MLCR sensor yields a distinct breathing pattern for each patient tested. The sensor also provides an accurate measure of the strength of breath at multiple stages as well as anomalous variations in respiratory rate and amplitude. This suggests that the MLCR sensor can detect symptoms of COVID-19 in a patient due to shortness of breath or difficulty breathing as well as track the progress of the disease in real time.
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