ABSTRACT
Ultraviolet germicidal irradiation (UVGI) is known to inactivate various viruses and bacteria, including SARS-CoV-2, and is widely applied especially in medical facilities. This inactivation results from the high photon energies causing molecular bonds to break, but when nonpathogen molecules are affected, unwanted effects may occur. Here, we explored the effect of a commercial high intensity (similar to 2 kW) UVC disinfection device on the composition and concentration of gases and particles in indoor air. We find that the UVC (254 nm) caused dramatic increases in particle number concentrations, and nearly all (similar to 1000) monitored gas phase species also increased. These responses were unsurprising when considering the typical impacts of UVC on atmospheric chemistry. High particle concentrations are associated with adverse health effects, suggesting that the impact of UVGI devices on indoor air quality (IAQ) should be studied in much more detail. The high-intensity device in this study was intended for short durations in unoccupied rooms, but lower-intensity devices for continuous use in occupied rooms are also widely applied. This makes further studies even more urgent, as the potential IAQ effects of these approaches remain largely unexplored.
ABSTRACT
SARS-CoV-2 has infected more than 600 million people worldwide and caused more than 6 million deaths. The emerging novel variants have made the epidemic rebound in many places. Meteorological factors can affect the epidemic spread by changing virus activity, transmission dynamic parameters and host susceptibility. This paper systematically analyzed the currently available laboratory and epidemiological studies on the association between the meteorological factors and COVID-19 incidence, in order to provide scientific evidence for future epidemic control and prevention, as well as developing early warning system.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Meteorological Concepts , Laboratories , Epidemiologic StudiesABSTRACT
Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how. In this demo, we present Sevi an end-to-end data visualization system that acts as a virtual assistant to allow novices to create visualizations through either natural language or speech. Sevi is powered by two main components: Speech2Text which is based on Google Cloud Speech-to-Text Rest API, and Text2VIS, which uses an end-to-end neural machine translation model called ncNet trained using a cross-domain benchmark called nvBench. Both ncNet and nvBench have been developed by us. We will walk the audience through two general domain datasets, one related to COVID-19 and the other on NBA player statistics, to highlight how Sevi enables novices to easily create data visualizations. Because nvBench contains Text2VIS training samples from 105 domains (e.g., sport, college, hospital, etc.), the audience can play with speech or text input with any of these domains.
ABSTRACT
Hydrogen peroxide (H2O2) solution and its aerosols are common disinfectants, especially for urgent reuse of personal protective equipment during the COVID-19 pandemic. Highly sensitive and selective evaluation of the H2O2 concentration is key to customizing the sufficient disinfection process and avoiding disinfection overuse. Amperometric electrochemical detection is an effective means but poses challenges originated from the precarious state of H2O2. Here, an atomic Co-N-x-C site anchored neuronal-like carbon modified amperometric sensor (denoted as the CoSA-N/C@rGO sensor) is designed, which exhibits a broad detection range (from 250 nM to 50 mM), superior sensitivity (743.3 mu A mM(-1) cm(-2), the best among carbon-based amperometric sensors), strong selectivity (no response to interferents), powerful reliability (only 2.86% decay for one week) and fast response (just 5 s) for residual H2O2 detection. We validated the accuracy and practicability of the CoSA-N/C@rGO sensor in the actual H2O2 disinfection process of personal protective equipment. Further characterization verifies that the electrocatalytic activity and selective reduction of H2O2 is determined by the atomically dispersed Co-N-x-C sites and the high oxygen content of CoSA-N/C@rGO, where the response time and reliability of H2O2 detection is determined by the neuronal-like structure with high nitrogen content. Our findings pave the way for developing a sensor with superior sensitivity, selectivity and stability, rendering promising applications such as medical care and environmental treatment.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic is accelerating in the world. Although the epidemic has been basically controlled across China at the present stage, the domestic epidemic prevention and control situation is still complex and grim. Field epidemiology played an important role in the prevention and control of infectious diseases during this epidemic and the past epidemics. In order to improve the teaching content of field epidemiology in undergraduate medical education, and to explore the practice of training in epidemiology with the present situation, this paper explored the teaching practice of field epidemiology investigation based on COVID-19. This study will help to enhance both the ability of students from theory to practice, and the adaption of high-level talents in field epidemic under the new situation. © 2021, Publication Centre of Anhui Medical University. All rights reserved.
ABSTRACT
Objective: To retrospective analyze the epidemiology, clinical characteristics, treatment and prognosis in patients with coronavirus disease 2019 (COVID-19). Methods: A total of 278 patients with COVID-19 admitted to Guangzhou Eighth People's Hospital from January 20 to February 10, 2020 were selected. The general demographic data, epidemiological data, clinical symptoms, laboratory examinations, lung CT imaging, treatment and prognosis were analyzed. Results: There were 130 male patients (46.8%) and 148 females (53.2%) with age (48.1±17.0) years and 88.8% patients between 20-69 years. Two hundred and thirty-six (84.9%) patients had comorbidities. Two hundred and eleven cases (75.9%) were common type. The in-hospital mortality was 0.4% (1/278). The majority (201, 72.3%) were imported cases mainly from Wuhan (89, 44.3%). The most common clinical manifestations were fever (70.9%) and dry cough (61.5%). In some patients, hemoglobin (10.4%), platelets (12.6%) and albumin (55.4%) were lower than the normal range. Other biochemical tests according to liver and function were normal, while lactic dehydrogenase (LDH) was elevated in 61 patients (21.9%), creatine kinase increased in 26 patients (9.4%). Prolonged activated partial thromboplastin time (APTT) was seen in 52 patients (18.7%), D-dimer higher than normal in 140 patients (50.4%), while 117 patients (42.1%) had elevated high-sensitivity C-reactive protein. Typical CT manifestations included single or multiple ground glass shadows especially in lung periphery in early disease which infiltrated and enlarged during progressive stage. Diffuse consolidation with multiple patchy density in severe/critical cases and even "white lung" presented in a few patients. Two hundred and forty-two patients (87.1%) received one or more antiviral agents, 242 (87.1%) combined with antibacterials, 191 (68.7%) with oxygen therapy. There were 198 patients (71.2%) treated with traditional Chinese medicine. Conclusions: COVID-19 could attack patients in all ages with majority of common type and low mortality rate. Clinical manifestations involve multiple organs or systems. Progression of the disease results in critical status which should be paid much attention.
Subject(s)
COVID-19 , Adult , Aged , Female , Fever , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , SARS-CoV-2ABSTRACT
More and more studies have evaluated the associations between ambient temperature and coronavirus disease 2019 (COVID-19). However, most of these studies were rushed to completion, rendering the quality of their findings questionable. We systematically evaluated 70 relevant peer-reviewed studies published on or before 21 September 2020 that had been implemented from community to global level. Approximately 35 of these reports indicated that temperature was significantly and negatively associated with COVID-19 spread, whereas 12 reports demonstrated a significantly positive association. The remaining studies found no association or merely a piecewise association. Correlation and regression analyses were the most commonly utilized statistical models. The main shortcomings of these studies included uncertainties in COVID-19 infection rate, problems with data processing for temperature, inappropriate controlling for confounding parameters, weaknesses in evaluation of effect modification, inadequate statistical models, short research periods, and the choices of research areal units. It is our viewpoint that most studies of the identified 70 publications have had significant flaws that have prevented them from providing a robust scientific basis for the association between temperature and COVID-19.
ABSTRACT
Spatio-temporal data analysis is very important in many time-critical applications. We take Coronavirus disease (COVID-19) as an example, and the key questions that everyone will ask every day are: how does Coronavirus spread? where are the high-risk areas? where have confirmed cases around me? Interactive data analytics, which allows general users to easily monitor and explore such events, plays a key role. However, some emerging cases, such as COVID-19, bring many new challenges: (C1) New information may come with different formats: basic structured data such as confirmed/suspected/serious/death/recovered cases, unstructured data from newspapers for travel history of confirmed cases, and so on. (C2) Discovering new insights: data visualization is widely used for storytelling;however, the challenge here is how to automatically find "interesting stories", which might be different from day to day. We propose DEEPTRACK, a system that monitors spatio-temporal data, using the case of COVID-19. For (C1), we describe (a) how we integrate and clean data from different sources by existing modules. For (C2), we discuss (b) how to build new modules for ad-hoc data sources and requirements, (c) what are the basic (or static) charts used;and (d) how to generate recommended (or dynamic) charts that are based on new incoming data. The attendees can use DeepTrack to interactively explore various COVID-19 cases.
ABSTRACT
OBJECTIVES: Asymptomatic patients, together with those with mild symptoms of coronavirus disease 2019 (COVID-19), may play an important role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. However, the dynamics of virus shedding during the various phases of the clinical course of COVID-19 remains unclear at this stage. METHODS: A total of 18 patients found to be positive for SARS-CoV-2 infection by real-time reverse transcription PCR (RT-PCR) assay and admitted to Chongqing University Central Hospital between 29 January and 5 February 2020 were enrolled into this study. Medical data, pulmonary computed tomographic (CT) scan images and RT-PCR results were periodically collected during the patients' hospital stay. All participants were actively followed up for 2 weeks after discharge. RESULTS: A total of nine (50%) asymptomatic patients and nine (50%) patients with mild symptoms of COVID-19 were identified at admission. Six patients (66.7%) who were asymptomatic at admission developed subjective symptoms during hospitalization and were recategorized as being presymptomatic. The median duration of virus shedding was 11.5, 28 and 31 days for presymptomatic, asymptomatic and mildly symptomatic patients, separately. Seven patients (38.9%) continued to shed virus after hospital discharge. During the convalescent phase, detectable antibodies to SARS-CoV-2 and RNA were simultaneously observed in five patients (27.8%). CONCLUSIONS: Long-term virus shedding was documented in patients with mild symptoms and in asymptomatic patients. Specific antibody production to SARS-CoV-2 may not guarantee virus clearance after discharge. These observations should be considered when making decisions regarding clinical and public health, and when considering strategies for the prevention and control of SARS-CoV-2 infection.