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1.
Neurocomputing ; 2022.
Article Dans Anglais | ScienceDirect | ID: covidwho-2086591

Résumé

With the global outbreak of COVID-19, wearing face masks has been actively introduced as an effective public measure to reduce the risk of virus infection. This measure leads to the failure of face recognition in many cases. Therefore, it is very necessary to improve the recognition performance of masked face recognition (MFR). Inspired by the successful application of self-attention in computer vision, we propose a Convolutional Visual Self-Attention Network (CVSAN), which uses self-attention to augment the convolution operator. Specifically, this is achieved by connecting a convolutional feature map, which enforces local features, to a self-attention feature map that is capable of modeling long-range dependencies. Since there is currently no publicly available large-scale masked face data, we generate a Masked VGGFace2 dataset based on the face detection algorithm to train the CVSAN model. Experiments show that the CVSAN algorithm significantly improves the performance of MFR compared to other algorithms.

2.
Journal of Shandong University ; 58(4):17-22, 2020.
Article Dans Anglais, Chinois | GIM | ID: covidwho-1812956

Résumé

During the epidemic of coronavirus disease 2019(COVID-19), the local Centers for Disease Control were bombarded with large amounts of questions from the public and the human hotline system was unable to meet the demands. As a result, Jinan Centers for Disease Coatrd developed an "intelligent question answering robot system" to cope with this situation. This paper introduces the design of the robot system and construction and classification of the knowledge base, and evaluates its application effects. The robot system can greatly reduce pressure on the human hotline, actively record and analyze users' demands, and improve the quality and efficiency of Centers for Disease Coatrd consultation service. It is a valuable and growable operating mode of consultation service, which can provide reference for the information service in future public health events.

3.
medrxiv; 2022.
Preprint Dans Anglais | medRxiv | ID: ppzbmed-10.1101.2022.03.28.22273021

Résumé

A global sentiment in early 2022 is that the COVID-19 virus could become endemic just like common cold flu viruses soon. The most optimistic view is that, with minimal precautions, such as vaccination, boosters and optional masking, life for most people will proceed as normal soon. However, as warned by A. Katzourakis of Oxford University recently [1], we must set aside lazy optimism, and must be realistic about the likely levels of death, disability and sickness that will be brought on by a COVID-19 endemic. Moreover, the world must also consider that continual circulation of the virus could give rise to new variants such as the new BA.2 variant (a subvariant of Omicron) continues to spread across the US and parts of Europe. Data from the CDC is already showing that BA.2 has been tripling in prevalence every two weeks [2]. Hence, globally, we must use available and proven weapons to continue to fight the COVID-19 viruses, i.e., effective vaccines, antiviral medications, diagnostic tests and stop an airborne virus transmission through social distancing, and mask wearing. For this work, we have demonstrated a smart mask with an optimally-coupled ultra-thin flexible soundwave sensors for tracking, classifying, and recognizing different respiratory activities, including breathing, speaking, and two-/tri-phase coughing; the masks functionality can also be augmented in the future to monitor other human physiological signals. Although researchers have integrated sensors into masks to detect respiratory activities in the past, they only based on measuring temperature and air flow during coughing, i.e., counting only the number of coughs. However, coughing is a process consisting of several phases, including an explosion of the air with glottal opening producing some noise-like waveform, a decrease of airflow to decrease sound amplitude, and a voiced stage which is the interruption of the air flow due to the closure of glottal and periodical vibration of partly glottis, which is not always present. Therefore, sensors used for cough detection should not be only sensitive to subtle air pressure but also the high-frequency vibrations, i.e., a pressure sensor that needs to be responsive to a wide input amplitude and bandwidth range, in order to detect air flows between hundreds of hertz from breath, and acoustic signals from voice that could reach [~] 8000 Hz. Respiratory activities data from thirty-one (31) human subjects were collected. Machine learning methods such as Support Vector Machines and Convolutional Neural Networks were used to classify the collected sensor data from the smart mask, which show an overall macro-recall of about 93.88% for the three respiratory sounds among all 31 subjects. For individual subjects, the 31 human subjects have the average macro-recall of 95.23% (ranging from 90% to 100%) for these 3 respiratory activities. Our work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and conditioning, and applying machining learning algorithms to demonstrate a reliable wearable device for potential applications in continual healthy monitoring of subjects with cough symptoms during the eventual COVID-19 endemic. The monitoring and analysis of cough sound should be highly beneficial for human health management. These health monitoring data could then be shared with doctors via cloud storage and transmission technique to help disease diagnosis more effectively. Also, communication barriers caused by wearing masks can be alleviated by combining with the speech recognition techniques. In general, this research helps to advance the wearable device technology for tracking respiratory activities, similar to an Apple Watch or a Fitbit smartwatch in tracking physical and physiological activities.


Sujets)
4.
medrxiv; 2021.
Preprint Dans Anglais | medRxiv | ID: ppzbmed-10.1101.2021.06.24.21259370

Résumé

Background The SARS-CoV-2 Alpha variant B.1.1.7 became prevalent in the United States (US). We aimed to evaluate the impact of vaccination scale-up and potential reduction in the vaccination effectiveness on the COVID-19 epidemic and social restoration in the US. Methods We extended a published compartmental model and calibrated the model to the latest US COVID-19 data. We estimated the vaccine effectiveness against B.1.1.7 and evaluated the impact of a potential reduction in vaccine effectiveness on future epidemics. We projected the epidemic trends under different levels of social restoration. Results We estimated the overall existing vaccine effectiveness against B.1.1.7 to be 88.5% (95%CI: 87.4-89.5%) and vaccination coverage would reach 70% by the end of August, 2021. With this vaccine effectiveness and coverage, we anticipated 498,972 (109,998-885,947) cumulative infections and 15,443 (3,828-27,057) deaths nationwide over the next 12 months, of which 95.0% infections and 93.3% deaths were caused by B.1.1.7. Complete social restoration at 70% vaccination coverage would only slightly increase cumulative infections and deaths to 511,159 (110,578-911,740) and 15,739 (3,841-27,638), respectively. However, if the vaccine effectiveness were reduced to 75%, 50% or 25% due to new SARS-CoV-2 variants, we predicted 667,075 (130,682-1,203,468), 1.7m (0.2-3.2m), 19.0m (5.3-32.7m) new infections and 19,249 (4,281-34,217), 42,265 (5,081-79,448), 426,860 (117,229-736,490) cumulative deaths to occur over the next 12 months. Further, social restoration at a lower vaccination coverage would lead to even greater future outbreaks. Conclusion Current COVID-19 vaccines remain effective against the B.1.1.7 variant, and 70% vaccination coverage would be sufficient to restore social activities to a pre-pandemic level. Further reduction in vaccine effectiveness against SARS-CoV-2 variants would result in a potential surge of the epidemic in the future.


Sujets)
5.
Earth System Science Data Discussions ; : 1-26, 2021.
Article Dans Anglais | Academic Search Complete | ID: covidwho-1031311

Résumé

In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January-August 2020 period, gridded AFs at a 0.1×0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20-30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20-30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30-50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15-30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/). [ABSTRACT FROM AUTHOR] Copyright of Earth System Science Data Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

6.
arxiv; 2020.
Preprint Dans Anglais | PREPRINT-ARXIV | ID: ppzbmed-2008.10851v4

Résumé

Ozone (O$_{3}$) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O$_{3}$ have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O$_{3}$ across China for periods before and during the lockdown. We find that daytime O$_{3}$ decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O$_{3}$ changes, with a larger impact from the former especially in central China. The plunge in nitrogen oxide (NO$_{x}$) emission contributed to O$_{3}$ increases in populated regions, whereas the reduction in volatile organic compounds (VOC) contributed to O$_{3}$ decreases across the country. Due to a decreasing level of NO$_{x}$ saturation from north to south, the emission reduction in NO$_{x}$ (46%) and VOC (32%) contributed to net O$_{3}$ increases in north China; the opposite effects of NO$_{x}$ decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in these two precursors contributed to net O$_{3}$ declines in south China. Our study highlights the complex dependence of O$_{3}$ on its precursors and the importance of meteorology in the short-term O$_{3}$ variability.


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