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1.
31st ACM International Conference on Information and Knowledge Management, CIKM 2022 ; : 1481-1490, 2022.
Article in English | Scopus | ID: covidwho-2108339

ABSTRACT

The spread of COVID-19 throughout the world has led to cataclysmic consequences on the global community, which poses an urgent need to accurately understand and predict the trajectories of the pandemic. Existing research has relied on graph-structured human mobility data for the task of pandemic forecasting. To perform pandemic forecasting of COVID-19 in the United States, we curate Large-MG, a large-scale mobility dataset that contains 66 dynamic mobility graphs, with each graph having over 3k nodes and an average of 540k edges. One drawback with existing Graph Neural Networks (GNNs) for pandemic forecasting is that they generally perform information propagation in a flat way and thus ignore the inherent community structure in a mobility graph. To bridge this gap, we propose a Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to perform pandemic forecasting, which learns both spatial and temporal information from a sequence of dynamic mobility graphs. HiSTGNN consists of two network architectures. One is a hierarchical graph neural network (HiGNN) that constructs a two-level neural architecture: county-level and region-level, and performs information propagation in a hierarchical way. The other network architecture is a Transformer-based model that captures the temporal dynamics among the sequence of learned node representations from HiGNN. Additionally, we introduce a joint learning objective to further optimize HiSTGNN. Extensive experiments have demonstrated HiSTGNN's superior predictive power of COVID-19 new case/death counts compared with state-of-the-art baselines. © 2022 Owner/Author.

2.
Artificial Neural Networks and Machine Learning - Icann 2022, Pt Iii ; 13531:531-543, 2022.
Article in English | Web of Science | ID: covidwho-2094414

ABSTRACT

Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor their mask-wearing states by suitable automatic detectors. However, existing models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterward, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with alpha-CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.

3.
Journal of Silk ; 59(7):56-63, 2022.
Article in Chinese | Scopus | ID: covidwho-2066727

ABSTRACT

Currently with the changes in living habits and eating habits of China consumers have higher requirements for the wearing comfort and fit of head and face products such as helmets and masks. In addition the outbreak of COVID-19 in 2019 has made suitable masks an important protective equipment for medical staff and the general population. How to improve the safety protection level of masks has also become a hot social issue of concern. The fit of the mask is directly related to the protection effectiveness so it is urgent to measure track and update human head and face data. The research on the characteristics and classification of human head and face is an important basis for the structural design size formulation fit research and plate shape optimization of masks and helmets. Multilayer perceptron is an ANN algorithm. With the development of neural network technology it is gradually applied to prediction and classification. The model with strong nonlinear approximation function simple structure controllable number of input variables and strong operability can be applied to the classification and prediction of human body shape. In order to improve the adaptability of head and face products this paper took 189 female college students aged 18 - 26 as the research subjects and used the Martin measuring instrument to measure the head and face of the subjects. Feature factors affecting head and face shape were extracted by principal component analysis PCA the K-Means method was used to classify the head and face morphology and the index classification method was used to quantify head and face morphology. As a result a head-face shape prediction model based on MLP-ANN was proposed to improve the problem of low production work efficiency caused by too many head and face sizes in classifying or selecting models with too many references. The study found that through the analysis of head and face characteristics of 189 subjects seven important characteristic factors affecting the head and face shape were extracted head contour factor morphological facial factor morphological facial factor eye factor nose factor and mouth & lip factor. The head and face shapes were divided into five sizes according to the clustering center value of each category XS type/morphological index > 93 S type/morphological index 88 93 M type/morphological index 84 88 L type/morphological index 79 84 XL type/morphological index 79 and the M type was the most widely distributed and had a big coverage rate so it can be used as an intermediate type. Then through the MLP neural network seven head-face feature factors were used to predict head-face shape classification. The generated model had a 93. 42% correct prediction result and the research results can provide a reference for the design and production of head and face products. This paper provides an objective method for the study of head and facial features but there are still some limitations. In the future we can continue to improve the classification of head and face shape by expanding the area and age of the experimental subjects for comparative research. We can apply the classification to the head and face product specification system so as to accumulate morphological data for the study of the head and face characteristics of contemporary Chinese people and the design of head and face products such as masks for the Chinese market. © 2022 China Silk Association. All rights reserved.

4.
Sci Total Environ ; 2022.
Article in English | PubMed Central | ID: covidwho-2061858

ABSTRACT

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil–Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015–2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at −30.8 %, −27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

5.
Prog Urol ; 32(16): 1431-1439, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2031645

ABSTRACT

BACKGROUND: Impaired semen quality and reproductive hormone levels were observed in patients during and after recovery from coronavirus disease 2019 (COVID-19), which raised concerns about negative effects on male fertility. Therefore, this study systematically reviews available data on semen parameters and sex hormones in patients with COVID-19. METHODS: Systematic search was performed on PubMed and Google Scholar until July 18th, 2022. We identified relevant articles that discussed the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on male fertility. RESULTS: A total number of 1,684 articles were identified by using a suitable keyword search strategy. After screening, 26 articles were considered eligible for inclusion in this study. These articles included a total of 1,960 controls and 2,106 patients. When all studies were considered, the results showed that the semen parameters and sex hormone levels of patients infected with SARS-CoV-2 exhibited some significant differences compared with controls. Fortunately, these differences gradually disappear as patients recover from COVID-19. CONCLUSION: While present data show the negative effects of SARS-CoV-2 infection on male fertility, this does not appear to be long-term. Semen quality and hormone levels will gradually increase to normal as patients recover.


Subject(s)
COVID-19 , Humans , Male , SARS-CoV-2 , Semen , Semen Analysis , Gonadal Steroid Hormones , Hormones
6.
7th International Conference on Distance Education and Learning, ICDEL 2022 ; : 20-28, 2022.
Article in English | Scopus | ID: covidwho-2020430

ABSTRACT

Object: this research aims to organize, evaluate, improve and supervise college students' comprehensive English ability and learning autonomy through mobile and digital learning activities and management. Method: this research applied Task-based English Teaching and Itest System to improve students' learning autonomy and English ability by launching exercise tasks of 4 different English skills every weekday, such as writing, translation, reading and listening. Various learning data was recorded by Itest System everyday as an accordance for guiding and supervising students learning activity, and a means of the mobilization and digitization of the task, feedback and incentive mechanisms. Result:the pass rate of the exercises launched by Itest system is high at the beginning and ending of a semester and every Monday, while it's pretty low in the mid-term of a semester and every Friday. Moreover, the improvement effect of the 4 different English skills is different, and the progress of the 952 students' listening and translation is more obvious. Conclusion: the educational mechanism in this research is effective in improving the English translation and listening skills of the freshmen in college. Digitized management can clarify blind areas in students' learning activities, and locate students' problems and problematic students. Mobile English teaching can minimize close contact, but its disciplinary constriction to a few students needs the assistance of college counselor. Students' learning autonomy is low at the mid-term of a semester and on every Friday, which requires more management force. © 2022 ACM.

7.
Kexue Tongbao/Chinese Science Bulletin ; 67(21):2509-2521, 2022.
Article in Chinese | Scopus | ID: covidwho-1993426

ABSTRACT

The novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a public health emergency of international concern. Exposure to droplets produced in the coughs and sneezes of infected individuals has been perceived as the dominant transmission mode for COVID-19. However, increasingly more evidence supports claims of COVID-19 having airborne transmission. An in-depth understanding of the transmission pathways and influencing factors of SARS-CoV-2 is of great significance for formulating more effective intervention strategies. A large number of epidemiological investigations into the influence of atmospheric environmental conditions on virus transmission have been conducted. In this paper, we review current understandings of the association between COVID-19 and atmospheric environmental conditions. We first summarize the epidemiological investigations on the impact of atmospheric environmental factors (including solar radiation, temperature and humidity, wind speed, particulate matters, and gaseous pollutants) on the spread of COVID-19, and 164 epidemiological investigations are included, in which air temperature and relative humidity received the greatest attention. However, the impact of these factors on the prevalence of COVID-19 remains largely uncertain. 56% and 41% of investigations of temperature and humidity, respectively, show that cold and dry weather promotes COVID-19 transmission, while some studies come to the opposite conclusion, and still others do not show a significant relationship between them. Investigations of solar radiation are limited, but have come to the consistent conclusion that weak solar radiation is linked to increased severity of COVID-19 infection. Investigation of the impact of air pollution mainly focuses on particulate matters, and more than 70% of investigations indicate that PM2.5 likely contributes to the spread of COVID-19. Similarly, 62%, 51%, and 31% of investigations of NOx, O3, and SO2, respectively, indicate that the exposure to severe pollution can aggravate COVID-19 transmission. Therefore, the available findings reveal the complexity of the impact of atmospheric environmental conditions on the spread of COVID-19. We further discuss their mechanisms from three perspectives: (1) Atmospheric environmental conditions influence the generation of virus-laden aerosols and the occurrence of SARS-CoV-2 in the atmosphere. Relative humidity can affect the evaporation process of water on virus-laden aerosol, and thus affect its atmospheric life and probability of being inhaled by human body. (2) Atmospheric environmental conditions directly affect the stability of infection activity of SARS-CoV-2. Generally, high temperature, medium relative humidity, and intense solar radiation promote the inactivation of SARS-CoV-2. (3) Atmospheric environmental conditions indirectly affect the infection ability of SARS-CoV-2 by changing the defense ability of host cells. Air pollutants, especially PM2.5, can affect human susceptibility to the virus by increasing the expression of the SARS-CoV-2 receptor (angiotensin converting enzyme 2) in host cells. Meanwhile, meteorological conditions and air pollution can lead to respiratory system and other diseases in the human body, thus reducing human immunity and increasing the risk of virus infection, as well as the numbers of severely infected and fatal cases. All three mechanisms may contribute to the prevalence of COVID-19, but the dominant mechanism remains unclear. Finally, future directions of in-depth studies regarding the association between the epidemic and atmospheric conditions are proposed. © 2022 Chinese Academy of Sciences. All rights reserved.

8.
19th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13307 LNAI:179-188, 2022.
Article in English | Scopus | ID: covidwho-1919674

ABSTRACT

Besides being a threat to public physical health, COVID-19 may also bring harm to peoples’ mental health as well. This preliminary study aimed to explore how different levels of social exposure might result in different mental health outcomes (e.g., burnout) on frontline metro staff, who guarantee the efficiency and safety of urban transportation. Three positions of frontline metro staff with different levels of social exposure, namely station attendants, train drivers, and maintenance workers. Two waves of cross-sectional studies were conducted at two time points, one was shortly after the lockdown in 2020, the other was 5 months later in July 2020. Results showed that there is no significant difference between stress levels after the lockdown. However, a significant difference was observed in the burnout levels after several months of operation. Staff with more contact with passengers (i.e., station attendants) reported the highest level of burnout. Staff with less contact with passengers (i.e., maintenance workers) reported the lowest level of burnout. A possible explanation of such phenomenon was that higher social exposure during the pandemic may cause more anxiety and fear to be infected as well as more emotional labor to deal with people wearing masks. We also discussed possible methods to improve the well-being of metro staff. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

9.
14th International Conference on Cross-Cultural Design, CCD 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13313 LNCS:230-240, 2022.
Article in English | Scopus | ID: covidwho-1919665

ABSTRACT

Social media is one of the most significant sources of information in modern people’s life. Due to the large quantity of user base and public opinions, when people read a blog post, the different tendencies of comments may affect their views on the event to a certain extent. This paper, taking the COVID-19 epidemic as an example, investigated the impact of Weibo (a popular social software in China) comments on readers’ sentiments. In this paper, text mining technology was adopted to collect data including the blogs and the comments under each blog, and the NLPIR-Parser platform was used to analyze the sentiment of the comments. Finally, the conclusion that the sentiments of other comments tend to follow the sentiments of the first comments was drawn. Based on the research results, this paper also gave some enlightenment on social media management and suggestions of public opinions oversight. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

10.
Chung-kuo Tsao Chih/China Pulp and Paper ; 41(1):123-125, 2022.
Article in Chinese | Scopus | ID: covidwho-1893358

ABSTRACT

The influence of COVID-19 on the world economy and the new risks and opportunities faced by the forest, pulp and paper industry were introduced in this paper. The changes that would occur in the world's political, economic and industrial patterns in the post-epidemic era had been analyzed. It was recommended that Chinese forestry, pulp and paper enterprises should work abroad together to expand investment in raw material production areas, and improve the industrial supply chain. © 2022, China National Pulp and Paper Research Institute Co., Ltd.(CNPPRI). All right reserved.

11.
Technology and Innovation ; 22(2):219-224, 2022.
Article in English | Web of Science | ID: covidwho-1856507

ABSTRACT

The pandemic caused by the spread of the virus SARS-CoV-2 threatened to severely disrupt the activities of student-athletes. In order to provide a safe environment for athletic competition, the National Collegiate Athletic Association (NCAA) mandated testing of student-athletes. The goal was to rapidly identify student-athletes and the athletic staff member who either tested positive for SARS-CoV-2 or were in contact with individuals who tested positive. Rapid identification of infected individuals and their contacts allowed the University to implement quarantine standards and quarantine facilities quickly as needed. The University of Texas at Arlington (UTA) developed an in-house testing program and was quickly able to meet the NCAA requirements, allowing UTA to continue its athletic participation with minimal forfeiture of scheduled games. The purpose of this paper is to report the implementation UTRs COVID prevention program for the university's athletic program. This program may provide valuable information to other universities' planning for the management of COVID prevention in their athletic programs. Challenges and solutions are identified.

13.
Academia-Industry Consortium for Data Science (AICDS) ; 1411:323-330, 2020.
Article in English | Web of Science | ID: covidwho-1777669

ABSTRACT

Stock market return analysis and forecasting are an important topic in econometric finance research. Since the traditional ARIMA models do not consider the variation of volatility, their prediction accuracy is not satisfactory to represent highly volatile periods of any stock market. The GARCH model family resolves the heteroskedasticity of a time series, and hence, it performs better in periods of high volatility. This paper explores the impact of the COVID-19 epidemic on Chinese small- and medium-sized enterprises (SMEs) using a GARCH model for Business as usual (BAU) simulation. We use the Chinese Growth Enterprise Market (GEM) stock index to represent the economic situation of SMEs during the COVID-19 period. Then, we extract, analyze, and predict changes in GEM stock volatility, explore the impact on and recovery status of SMEs, and predict their future trends. For BAU simulation, we first preprocess the GEM stock index between 2018 and 2020 and determine the order of autocorrelation and lags of the data to build the mean model. An ARCH effect test on the residual term of the mean equation was found to be significant and help to decide the order of the GARCH framework. Using the model, a BAU simulation was created and compared statistically with the actual GEM index during 2020. The comparison successfully demonstrated that the GEM index has increased volatility during the pandemic, which is in line with our prior hypothesis.

14.
Public Health ; 205: 169-181, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1751169

ABSTRACT

OBJECTIVE: This study aimed to systematically clarify attitudes and influencing factors of the public toward COVID-19 vaccination for children or adolescents. STUDY DESIGN: This was a scoping review. METHODS: This scoping review screened, included, sorted, and analyzed relevant studies on COVID-19 vaccination for children or adolescents before December 31, 2021, in databases, including PubMed, Elsevier, Web of Science, Cochrane Library, and Wiley. RESULTS: A total of 34 studies were included. The results showed that the public's acceptance rate toward COVID-19 vaccination for children or adolescents ranged from 4.9% (southeast Nigerian mothers) to 91% (Brazilian parents). Parents' or adolescents' age, gender, education level, and cognition and behavior characteristics for the vaccines were the central factors affecting vaccination. The vaccine's safety, effectiveness, and potential side-effects were the main reasons affecting vaccination. CONCLUSIONS: Realizing current public attitudes of COVID-19 vaccination for adolescents or children can effectively develop intervention measures and control the pandemic as soon as possible through herd immunity.


Subject(s)
COVID-19 , Vaccines , Adolescent , Attitude , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Child , Female , Humans , Parents , Vaccination
15.
Geophysical Research Letters ; 49(2):10, 2022.
Article in English | Web of Science | ID: covidwho-1692656

ABSTRACT

The significant reduction in human activities during COVID-19 lockdown is anticipated to substantially influence urban climates, especially urban heat islands (UHIs). However, the UHI variations during lockdown periods remain to be quantified. Based on the MODIS daily land surface temperature and the in-situ surface air temperature observations, we reveal a substantial decline in both surface and canopy UHIs over 300-plus megacities in China during lockdown periods compared with reference periods. The surface UHI intensity (UHII) is reduced by 0.25 (one S.D. = 0.22) K in the daytime and by 0.23 (0.20) K at night during lockdown periods. The reductions in canopy UHII reach 0.42 (one S.D. = 0.26) K in the daytime and 0.39 (0.29) K at night. These reductions are mainly due to the near-unprecedented drop in human activities induced by strict lockdown measures. Our results provide an improved understanding of the urban climate variations during the global pandemic.

16.
Journal of Biomaterials and Tissue Engineering ; 12(4):778-787, 2022.
Article in English | Web of Science | ID: covidwho-1666523

ABSTRACT

Background and purpose: Coronavirus disease 2019 (COVID-19) was spreading all over the world. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) primarily invades and infects the lungs of humans leading to COVID-19. Mild to severe clinical symptoms such as fever, cough, and shortness of breath were existed in those patients. One of the most common changes in these patients was abnormal blood routine. However, uncertainty remains regarding the dynamic characteristics of platelet in COVID-19 patients due to limited data. Therefore, we aimed to analyze the association between dynamic characteristics of blood platelet and disease severity, and to identify new monitoring indicators to treat the COVID-19 patients.Methods:In this cohort study, 398 COVID19 patients treated in the Shenzhen Third People's hospital from December 16, 2019 to March 26, IP: 182.75.148.10 On: Thu, 20 Jan 2022 08:58:32 Copyright: American Scientific Publishers 2020 were collected and participated. All data of participants including the clinical characteristics, Delivered by Ingenta imaging and laboratory information were collected. All patients included in our study were classified as four groups (mild, common, severe, and critical types) regarding clinical symptoms and relevant severe failures based on the Diagnosis Criteria. Platelet count was examined at the baseline and every 3-5 days during hospitalization. Results: The platelet count varied with clinical classifications. The platelet count in mild type was normal without significant fluctuation. While the blood platelet count of most common and severe patients had obvious fluctuations, showing as a dynamic change that first rose and then fell to the level at admission, which was consistent with the trend of lung inflammation. Bone marrow smears further showed that bone marrow hyperplasia was normal in mild, common and severe type patients, and megakaryocytes and their platelet-producing functions were not abnormal. Conclusions: Our results suggested that the dynamic changes of platelet count might be a predictor of lung inflammation alteration for COVID-19 patients. The changes in platelet count might be a responsive pattern secondary to lung inflammation. The function of bone marrow may be slightly affected by SARS-CoV-2 infection.

17.
Journal of Engineering-Joe ; : 6, 2022.
Article in English | Web of Science | ID: covidwho-1655689

ABSTRACT

During the COVID-19 outbreak, service robots have provided good service for teachers and students in universities. In order to improve the safety and convenience of pharmaceutical undergraduate laboratory, a service robot for pharmaceutical undergraduate teaching laboratory was designed from the aspects of path planning, positioning and navigation, human-computer interaction etc., which can solve practical problems such as epidemic prevention and control, laboratory safety etc.

18.
2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021 ; : 454-459, 2021.
Article in English | Scopus | ID: covidwho-1613110

ABSTRACT

This paper is mainly to analyze and predict some situations of COVID-19 in the United States. The first part of this paper mainly analyzes the relationship between the mortality rate of COVID-19 disease and population structure and density by analyzing the publicly reported COVID-19 data from various counties in the United States. We found that there is a negative correlation between population density and death rate. Secondly, through a software called Shiny we introduced, it can predict the future development trend of the epidemic in the United States based on the existing data. The development trend of the past data presented by the shiny application matches with the actual trend, which has a certain credibility. In this work, the result can help us to have a better understanding of COVID-19. Although the analysis object is the United States, it can be used as a reference for many countries. © 2021 ACM.

19.
Shengtai Xuebao ; 41(19):7493-7508, 2021.
Article in Chinese | Scopus | ID: covidwho-1497775

ABSTRACT

The severe outbreak of Coronavirus disease 2019 (COVID- 19) demonstrates the importance of disease risk assessment. The existing risk assessment methods are limited by the real time and accuracy of data. Most of them take the administrative statistical unit as the analysis scale, which has modifiable areal unit problem (MAUP) . First, based on a random forest method, we integrated COVID-19 transmission data at community scale and multisource geospatial data to map COVID-19 disease outbreak risks at fine scale. The experimental results (overall accuracy = 0.85, Kappa = 0.70) indicated the feasibility of the model. Second, we built a spatial variable-infection risk model at community and place scale to assess the risk degree of epidemic spread in different places and facilities. Last, we analyzed the possibly spatial drivers of disease transmission. The results show that (1) the central area of Wuhan city has the highest risk of infection and the risk map presents a trend of decreasing from the center to the periphery;(2) The top five facilities with the highest risk of COVID- 19 infection are shopping, medical, financial, transportation and public facilities;(3) The transmission risk of the epidemic is low in primary and middle schools, but high in colleges and universities;(4) The model determines the degree of epidemic risk at the community scale and predicts that shopping and traffic places are two most significant driving factors with the epidemic outbreak. In conclusion, this study suggests a new method of disease risk assessment based on a fine scale, which can pave the way for future disease risk assessment. © 2021 Science Press. All rights reserved.

20.
Journal of the Optical Society of America B: Optical Physics ; 38(10):2855-2862, 2021.
Article in English | Scopus | ID: covidwho-1430514

ABSTRACT

Surface plasmonic resonance (SPR) is integrated into a whispering-gallery mode (WGM) optical microsensor to augment sensitivity in this study. The performance of such WGM silica ring sensors of 20 µm in size with an Ag or Au metal core was evaluated for detection of small respiratory viruses such as COVID-19 via the finite-element modeling. Compared with pure WGM sensors, the integration with SPR enhances sensitivity by 3–5 times and facilitates combination with the polymerase chain reaction method to achieve fast, accurate, and specific virus detection. The presence of a single respiratory virus of 70 to 100 nm in diameter in air environment could shift the sensor resonance wavelength by 36 to 64 pm in the case of an Au metal core or by 34 to 63 pm of an Ag metal core. With use of a general-purpose optical analyzer of 10 pm resolution, a single airborne virus of 20 nm in size is detectable using the proposed hybrid sensor. This corresponds to about 0.005 vol. %. For viruses in aqueous solution, the detection limit rises to about 0.2 vol. %. A fundamental enhancement factor based on relative electric energy ratio is introduced and defined to analyze and quantify sensitivity enhancement for the first time, to the best of our knowledge. © 2021 OSA - The Optical Society. All rights reserved.

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