Dynamic Properties for a Second-Order Stochastic SEIR Model with Infectivity in Incubation Period and Homestead-Isolation of the Susceptible Population

In this article, we analyze a second-order stochastic SEIR epidemic model with latent infectious and susceptible populations isolated at home. Firstly, by putting forward a novel inequality, we provide a criterion for the presence of an ergodic stationary distribution of the model. Secondly, we establish sufficient conditions for extinction. Thirdly, by solving the corresponding Fokker–Plank equation, we derive the probability density function around the quasi-endemic equilibrium of the stochastic model. Finally, by using the epidemic data of the corresponding deterministic model, two numerical tests are presented to illustrate the validity of the theoretical results. Our conclusions demonstrate that nations should persevere in their quarantine policies to curb viral transmission when the COVID-19 pandemic proceeds to spread internationally. © 2023 by the authors.

Electrostatic Precipitator Design Optimization for the Removal of Aerosol and Airborne Viruses

In the midst of the COVID-19 pandemic, new requirements for clean air supply are introduced for heating, ventilation, and air conditioning (HVAC) systems. One way for HVAC systems to efficiently remove airborne viruses is by filtering them. Unlike disposable filters that require repeated purchases of consumables, the electrostatic precipitator (ESP) is an alternative option without the drawback of reduced dust collection efficiency in high-efficiency particulate air (HEPA) filters due to dust buildup. The majority of viruses have a diameter ranging from 0.1 μm to 5 μm. This study proposed a two-stage ESP, which charged airborne viruses and particles via positive electrode ionization wire and collected them on a collecting plate with high voltage. Numerical simulations were conducted and revealed a continuous decrease in collection efficiencies between 0.1 μm and 0.5 μm, followed by a consistent increase from 0.5 μm to 1 μm. For particles larger than 1 μm, collection efficiencies exceeding 90% were easily achieved with the equipment used in this study. Previous studies have demonstrated that the collection efficiency of suspended particles is influenced by both the ESP voltage and turbulent flow at this stage. To improve the collection efficiency of aerosols ranging from 0.1 μm to 1 μm, this study used a multi-objective genetic algorithm (MOGA) in combination with numerical simulations to obtain the optimal parameter combination of ionization voltage and flow speed. The particle collection performance of the ESP was examined under the Japan Electrical Manufacturers' Association (JEMA) standards and showed consistent collection performance throughout the experiment. Moreover, after its design was optimized, the precipitator collected aerosols ranging from 0.1 μm to 3 μm, demonstrating an efficiency of over 95%. With such high collection efficiency, the proposed ESP can effectively filter airborne particles as efficiently as an N95 respirator, eliminating the need to wear a mask in a building and preventing the spread of droplet infectious diseases such as COVID-19 (0.08 μm–0.16 μm). © 2023 by the authors.

PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model. © 2022 IEEE.

A Bibliographical Analysis of Pragmatic Strategies Responding to the Pandemic Crisis in Aviation

The aviation industry has suffered from the COVID-19 pandemic since early 2020. Airlines, airports, and manufacturers reacted to fight against the disease to protect passengers as well as remain sustainable. The purpose of this study is to analyze existing archives and discover strategic plans implemented by essential actors of the commercial aviation system. Using inductive qualitative analysis in conjunction with VOSviewer bibliographical data visualization, this study unveils the practical strategies of resilience enacted by the airline industry, manufacturers, and commercial airports during the pandemic time. Based on the Crisis Response Matrix from Suk and Kim, airlines' survival strategies during COVID-19 include passenger protection, operational retrenchment, innovation, and long-term managerial plans. Manufacturers' main approaches are expanding business with maintenance, repair, and overhaul (MRO) on top of alternative fuel innovations for emission reduction. Remarkably, airports adopt policies and protocols to screen and protect passengers, share information about infected passengers, and create a contactless airport environment for the prevention and control of pandemic infectious diseases. Synthesis tables containing discoveries are provided for practitioners' future reference. © 2023, University Aviation Association. All rights reserved.

SERS/photothermal-based dual-modal lateral flow immunoassays for sensitive and simultaneous antigen detection of respiratory viral infections

Lateral flow immunoassay (LFIA) is one of the most common analytical platforms for point-of-care testing (POCT), which is capable of large-scale primary screening and home self-testing of infectious diseases. However, the sensitivity of conventional AuNPs-based LFIA is relatively low and more prone to false negatives. Herein, we report a novel LFIA based on gold-core-silver-shell bimetallic nanoparticles (Au4-ATP@Ag NPs) emitting Surface-enhanced Raman scatting (SERS) and Photothermal (PT) effect, named SERS/PT-based dual-modal LFIA (SERS/PT-dmLFIA), for the antigen detection of infectious diseases pathogens, which displayed an excellent performance. For influenza A virus (IAV), influenza B virus (IBV), and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) N protein detection, the limit of detections (LoD) with Raman as signal were 31.25, 93.75, and 31.25 pg mL-1 respectively, and the LoDs with temperature difference (∆T) as signal were as low as 15.63, 187.5, and 15.63 pg mL-1 respectively, which were over 4-fold more sensitive than visual-based LFIA. The proposed SERS/PT-dmLFIA was used for detecting virus antigen in pharyngeal swabs and showed ideal coincidence rate of over 95% compared to the commercialized assays. In addition, we explored the development of multiplex SERS/PT-dmLFIA that can detect IAV, IBV, and SARS-CoV-2 antigens simultaneously without cross reactivity. Overall, the SERS/PT-dmLFIA for antigen detection not only exhibits high sensitivity, accuracy and specificity, but also have characteristics of rapidity and simplicity, which holds high potential for rapid diagnosis of infectious diseases in laboratory testing, mass screening, and home self-testing. © 2023 Elsevier B.V.

HateNet: A Graph Convolutional Network Approach to Hate Speech Detection

The COVID-19 pandemic has caused hate speech on online social networks to become a growing issue in recent years, affecting millions. Our work aims to improve automatic hate speech detection to prevent escalation to hate crimes. The first c hallenge i n h ate s peech r esearch i s t hat e xisting datasets suffer from quite severe class imbalances. The second challenge is the sparsity of information in textual data. The third challenge is the difficulty i n b alancing t he t radeoff b etween utilizing semantic similarity and noisy network language. To combat these challenges, we establish a framework for automatic short text data augmentation by using a semi-supervised hybrid of Substitution Based Augmentation and Dynamic Query Expansion (DQE), which we refer to as SubDQE, to extract more data points from a specific c lass f rom T witter. W e a lso p ropose the HateNet model, which has two main components, a Graph Convolutional Network and a Weighted Drop-Edge. First, we propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings to provide new insights into how ideas are connected. Second, we propose a weighted Drop-Edge based stochastic regularization technique, which removes edges randomly based on weighted probabilities assigned by the semantic similarities between Tweets. Using 3 different SubDQE-augmented datasets, we compare our HateNet model using eight different tweet embedding methods, six other baseline classification models, and seven other baseline data augmentation techniques previously used in the realm of hate speech detection. Our results show that our proposed HateNet model matches or exceeds the performance of the baseline models, as indicated by the accuracy and F1 score. © 2022 IEEE.

Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties

Ever since the COVID-19 outbreak, various works have focused on using multitude of different static and dynamic features to aid the prediction of disease forecasting models. However, in the absence of historical pandemic data these models will not be able to give any meaningful insight about the areas which are most likely to be affected based on preexisting conditions. Furthermore, the black box nature of neural networks often becomes an impediment for the concerned authorities to derive any meaning from. In this paper, we propose a novel explainable Graph Neural Network (GNN) framework called Graph-COVID-19-Explainer (GC-Explainer) that gives explainable prediction for the severity of the spread during initial outbreak. We utilize a comprehensive set of static population characteristics to use as node features of Graph where each node corresponds to a geographical region. Unlike post-hoc methods of GNN explanations, we propose a framework for learning important features during the training of the model. We further apply our model on real-world early pandemic data to show the validity of our approach. Through GC-Explainer, we show that static features along with spatial dependency among regions can be used to explain the varied degree of severity in outbreak during the early part of the pandemic and provide a framework to identify the at-risk areas for any infectious disease outbreak, especially when historical data is not available. © 2022 IEEE.

Spillover effect of the RMB and Non-USD currencies after the COVID-19 pandemic: Evidence captured from 30-minute high frequency data

Amid the faster- and wider-than-expected spread of COVID-19, which has added new twists to the global economic outlook and profoundly impacted the performance of major currencies around the world, the RMB has been performing well, and thus, its market standing has improved. However, uncertainties about the future pose enormous challenges to the RMB internationalization. By processing 30-min high-frequency data, this paper aims to study changes in the characteristics of the relationship between the RMB and other non-USD currencies at five stages of the pandemic, first by means of auxiliary regression analysis, in which the pandemic is accounted for with a dummy variable, and then with a VAR-BEKK-GARCH model. The research shows that since the latter stages of the global pandemic, significant negative spillover effects among major non-USD currencies can be observed, while the independence of offshore RMB has increased gradually, and there have been weakening trends in the sustainability of the mean spillover and volatility spillover effects among other currencies. As the "regular pandemic prevention and control” begins to take hold in China and the geopolitical uncertainty increasingly outbreaks, the top priority in global currency market should be to resist the pressure of RMB independence with policy changes and increase caution in investing RMB assets. © 2022 Elsevier Inc.

Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing in Healthcare Settings during the COVID-19 Pandemic

Background. COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods. Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coinsized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1 System configuration Results. Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion. We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers.

Transcriptomic Profiling of Ganoderic Acid Me-Mediated Prevention of Sendai Virus Infection

Objectives: Ganoderic acid Me [GA-Me], a major bioactive triterpene extracted from Ganoderma lucidum, is often used to treat immune system diseases caused by viral infections. Although triterpenes have been widely employed in traditional medicine, the comprehensive mechanisms by which GA-Me acts against viral infections have not been reported. Sendai virus [SeV]-infected host cells have been widely employed as an RNA viral model to elucidate the mechanisms of viral infection. Method(s): In this study, SeV-and mock-infected [Control] cells were treated with or without 54.3 muM GA-Me. RNA-Seq was performed to identify differentially expressed mRNAs, followed by qRT-PCR validation for selected genes. GO and KEGG analyses were applied to investigate potential mechanisms and critical pathways associated with these genes. Result(s): GA-Me altered the levels of certain genes' mRNA, these genes revealed are associated pathways related to immune processes, including antigen processing and presentation in SeV-infected cells. Multiple signaling pathways, such as the mTOR pathway, chemokine signaling pathway, and the p53 pathways, significantly correlate with GA-Me activity against the SeV infection process. qRT-PCR results were consistent with the trend of RNA-Seq findings. Moreover, PPI network analysis identified 20 crucial target proteins, including MTOR, CDKN2A, MDM2, RPL4, RPS6, CREBBP, UBC, UBB, and NEDD8. GA-Me significantly changed transcriptome-wide mRNA profiles of RNA polymerase II/III, protein posttranslational and immune signaling pathways. Conclusion(s): These results should be further assessed to determine the innate immune response against SeV infection, which might help in elucidating the functions of these genes affected by GA-Me treatment in virus-infected cells, including cells infected with SARS-CoV-2. Copyright © 2022 Bentham Science Publishers.

Near-Real-Time Carbon Emission Accounting Technology Toward Carbon Neutrality

Climate change is the greatest environmental threat to humans and the planet in the 21st century. Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events. Cumulative carbon dioxide (CO2) emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage, and this accounts for approximately 80% of the total anthropogenic greenhouse gases. Therefore, accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting. Currently, China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060. The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies. Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline. This article reviews existing annual-based carbon accounting methods, with a focus on the newly developed real-time carbon emission technology and its current application trends. We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used. The development of relevant data and methods will provide strong database support to the policymaking for China's "carbon neutrality" strategy. Finally, this article provides an outlook on the future of real-time carbon emission monitoring technology.(c) 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Hand-Hygiene Education and Facilitation System for Children

Hand hygiene has become even more im-portant in light of the COVID-19 pandemic, where hands are one of the high-risk transmission routes. Existing hand-hygiene education is focused on one-time training and does not ensure that correct handwashing procedures are undertaken. Our study, therefore, proposes a hand-hygiene education and facilitation system. Compared to previous systems, through an external RGB camera with our proposed image preprocessing and use the 3-D convo-lution and convolutional long short-term memory (Con-vLSTM) models to detect correctness of handwashing postures, which also facilitates children's ability to wash their hands properly through an on-screen tutorial. It also encourages children to develop good handwashing habits through a positive competition and reward system, and helps teachers to understand children's learning pro-gresses. The experimental results showed that the model was able to identify handwashing postures in real-time with 95.12% accuracy in a realistic and variable environ-ment. © 2022 IEEE.

Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers

This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the median score of 0.925. For Task 5, our best of 0.75 exceeded the median score of 0.745. © 2021 Association for Computational Linguistics.

Exploration of new outdoor leisure activities (glamping) during the post-pandemic era

This study is based on the means-end chain theory and adopts the laddering interview method to understand the customer's perceived value of glamping. The means-end chain theory links attributes, consequences, and values, which can help uncover interviewees' honest opinions and ultimate values they sought in glamping. A total of 40 individuals who had not previously experienced glamping were interviewed. In-depth interviews were conducted following the one-on-one ladder-style format. The results reveal that the interviewees mainly focus on the 'hardware and equipment' aspect of glamping. Moreover, the aspect that interviewees associated closest with glamping was 'comfortable accommodation.' During the COVID-19 pandemic, these interviewees were focused on improving 'their sense of well-being' and 'enjoyment of life.' Hence, the study analysis and its discussion from the management perspective can be applied to camping-related businesses. This study will enhance future academic research by explaining and applying the means-end chain theory and laddering to glamping.

SEROLOGICAL RESPONSES TO SARS-COV-2 VACCINATION IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE: A PROSPECTIVE COHORT STUDY

Background: The immune response of SARS-CoV-2 vaccines is uncertain in those with Inflammatory Bowel Disease (IBD) due to a diverse array of immune-modifying therapies that vary in the mechanism of immunosuppression. Aim: We aimed to quantify the serological response to SARS-CoV-2 vaccines in those with IBD and determine antibody levels across varying therapeutic options. Methods: Individuals with IBD who received a first and/or second dose of a COVID-19 vaccine (Pfizer-BioNTech, Moderna, and/or AstraZeneca) were assessed for serological response (1–8 weeks after first dose;1–8 weeks after second dose, 8–18 weeks after second dose, 18+ weeks after second dose) using the SARS-CoV-2 IgG II Quant assay to the receptor-binding domain of the SARS-CoV-2 spike protein. The cohort was stratified based on age, sex, vaccine received, IBD type, IBD therapeutic, and prior confirmed diagnosis of COVID-19. The primary outcome was seroconversion defined as IgG levels of ³50 AU/mL. Secondarily, we evaluated the geometric mean titer (GMT) with 95% confidence intervals (CI). Results: Table 1 describes the characteristics of individuals with IBD (n=466) with serological data following the first dose (n=247) and/or second dose (n=413) of a COVID-19 vaccine. After 1–8 weeks following first dose of the vaccine, 81.4% seroconverted, with the lowest first-dose conversion rates in patients taking anti- TNF monotherapy (80.3%), anti-TNF combination therapy (51.5%), and corticosteroids (50.0%) (Table 1). Overall, 98.4% of the cohort seroconverted within 1–8 weeks of the second dose. Over time, seropositive rates decreased with 95.8% seroconversion within 8– 18 weeks of the second dose and 90.5% after 18 weeks. Seroconversion after second dose was consistently high across all medication classes (range: 94.6%–100.0%), except for oral corticosteroids (62.5%). GMT levels significantly increased (p<0.0001) from first dose (1825 AU/mL [95% CI: 981, 2668 AU/mL]) to second dose at 1–8 week (9059 AU/mL [7698, 10420 AU/mL]) but fell significantly (p<0.0001) to 3649 AU/mL (95% CI: 2562, 4736 AU/ mL) 8–18 weeks from second dose and 2527 AU/mL (95% CI: 883, 4172 AU/mL) 18+ weeks after second dose (Table 1, Figure 1). GMT levels 1–8 weeks after second dose were higher in those with prior COVID-19 (16,770 AU/mL), but lower in those receiving anti- TNF combination therapy (4231 AU/mL) and oral corticosteroids (5996 AU/mL) (Table 1). Conclusion: Seroconversion rates following full-regimen vaccination are high in patients with inflammatory bowel disease across all medication classes except for anti-TNF combination therapy and oral corticosteroids. Antibody titres and seroconversion rates tend to decrease after eight weeks post-full vaccination, which is consistent across medication classes. (Table Presented) Table 1. Patient and vaccine characteristics, seroconversion rates, and geometric mean titres by prior PCR-confirmed COVID-19 status for each medication class. (Figure Presented) Figure 1. Log-transformed anti-SARS-CoV-2 spike antibody concentration per vaccine category. Black points represent GMTs while narrow black bars represent bounds of 95% CI associated with each GMT. Solid blue line represents threshold for positive seroconversion [ln (50 AU/mL)].

A THIRD DOSE OF SARS-COV-2 VACCINE YIELDS HIGH ANTIBODY LEVELS COMPARED TO TWO DOSE VACCINATION SCHEDULE IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Background: The immune response to a two-dose regimen of SARS-CoV-2 vaccination in those with Inflammatory Bowel Disease (IBD) has been consistently high in emerging research. Serological responses following a third dose have yet to be established. Aim: We aimed to quantify the serological response to a third dose of SARS-CoV-2 vaccines in those with IBD and compare to responses after a two-dose regimen. Methods: Individuals with IBD who have received at least two doses of a COVID-19 vaccine were assessed for serological response using the SARS-CoV-2 IgG II Quant assay to the receptor-binding domain of the SARSCoV- 2 spike protein at least eight weeks after second dose and then after third dose. The primary outcome was seroconversion defined as IgG levels of ≥50 AU/mL. Secondarily, we evaluated the geometric mean titer (GMT) with 95% confidence intervals (CI). Outcomes were stratified by prior COVID-19 history. A Wilcoxon rank sum test was used to compare antibody titres following 3rd dose vaccination and titres following 2nd dose vaccination. For patients with both post-2nd and post-3rd vaccination serology, the difference in antibody titres between doses was determined and the mean difference was tested using one-sample Student's t-tests. Results: Table 1 describes the characteristics of individuals with IBD (n = 271) with serological data following the corresponding dose for those with 2nd dose vaccination (n = 175) compared to those with a 3rd dose of vaccine (n = 96). Seroconversion following 3rd dose vaccination occurred for all individuals (100.0%), compared to a 94.4% seroconversion rate at least eight weeks following 2nd dose vaccination (range: 8 to 35 weeks post-2nd dose). GMT for the post-3rd dose cohort (16424 AU/mL [13437, 19411 AU/mL]) was significantly higher (p<0.0001) than the post-2nd dose cohort (3261 AU/mL [2356, 4165 AU/mL] (Table 1, Figure 1b). Individual titres as a function of time following 2nd dose vaccination are seen in Figure 1a for both 3rd dose and 2nd dose cohorts. For individuals with serology following both 2nd dose and 3rd dose vaccination (n = 82), seroconversion rates increased from 97.6% to 100.0% after the 3rd dose. GMT following post-3rd dose vaccination also increased with a mean difference in antibody titres between post-3rd dose and post-2nd dose vaccination of 11384 AU/mL (8541, 14228 AU/mL, p < 0.0001). This difference was significant for both individuals with prior COVID-19 history (11682 AU/mL [95% CI: 8618, 14746 AU/mL, p<0.0001]) and individuals without (8194 AU/mL [95% CI: 988, 15400 AU/mL]). Conclusion: Seroconversion rates and antibody response following third dose vaccination are substantially increased as compared to second dose in patients with IBD. Third dose vaccination can counter the decrease in antibody concentration over time following a two-dose regimen. (Table Presented) Table 1. Patient characteristics, vaccine type, seroconversion rates, and geometric mean titres by prior COVID-19 status for post-3rd dose and post-2nd dose cohorts

Traditional Chinese Medicine for COVID-19 Pandemic and Emerging Challenges: an Online Cross-sectional Survey in China

Methods: The survey was designed by an international team, translated and adapted to simplified Chinese, including 132 kinds of traditional Chinese medicine (TCM) preparation recommended by guidelines. It was distributed and collected from February to May 2021, with data analysed by WPS spreadsheet and wjx.cn. Descriptive statistics were used to describe demographics and clinical characteristics, diagnosis, treatments, preventative behaviours and interventions, and their associated outcomes. Results: The survey was accessed 503 times with 341 (67.8%) completions covering 23 provinces and four municipalities in China. Most (282/341, 82.7%) respondents reported no symptoms during the pandemic and the majority (290/341, 85.0%) reported having a SARS-CoV-2 PCR test at some point. Forty-five (13.2%) reported having a respiratory infection, among which 19 (42.2%) took one or more categories of modern medicine, e.g. painkillers, antibiotics;16 (35.6%) used TCM interventions(s);while seven respondents combined TCM with modern medicine. All respondents reported using at least one behavioural or medical approach to prevention, with 22.3% taking TCM and 5.3% taking modern medicines. No respondents reported having a critical condition related to COVID-19. Background: We aimed to investigate use of infection control behaviours, preventative and therapeutic interventions, and outcomes among respondents to an online survey during the COVID-19 pandemic in China. Conclusion: We found evidence of widespread use of infection control behaviours, modern medicines and TCM for treatment and prevention of COVID-19 and other respiratory symptoms. Larger scale studies are warranted, including a more representative sample exploring TCM preparations recommended in clinical guidelines.

A Survey on Contact Tracing: The Latest Advancements and Challenges

Infectious diseases are caused by pathogenic microorganisms, such as bacteria, viruses, parasites or fungi, which can be spread, directly or indirectly, from one person to another. Infectious diseases pose a serious threat to human health, especially COVID-19 that has became a serious worldwide health concern since the end of 2019. Contact tracing is the process of identifying, assessing, and managing people who have been exposed to a disease to prevent its onward transmission. Contact tracing can help us better understand the transmission link of the virus, whereby better interrupting its transmission. Given the worldwide pandemic of COVID-19, contact tracing has become one of the most critical measures to effectively curb the spread of the virus. This paper presents a comprehensive survey on contact tracing, with a detailed coverage of the recent advancements the models, digital technologies, protocols and issues involved in contact tracing. The current challenges as well as future directions of contact tracing technologies are also presented.