A Real-Time Portable IoT System for Telework Tracking.

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.

Facebook Intervention to Connect Alaska Native People with Resources and Support to Quit Smoking: CAN Quit Pilot Randomized Controlled Trial.

INTRODUCTION: There is some evidence that social media interventions can promote smoking cessation. This randomized controlled pilot study is the first to evaluate the feasibility and potential efficacy of a Facebook smoking cessation intervention among Alaska Native adults. METHODS: Recruitment and data collection occurred December 2019-March 2021. Participants were recruited statewide in Alaska using Facebook advertisements with a targeted sample of 60 enrolled. Participants were stratified by gender, age, and rural/urban residence and randomly assigned to receive referral resources on evidence-based cessation treatments (EBCTs) (control, n=30) or these resources plus a three-month, closed/private, culturally tailored, Facebook group (intervention, n=31) that connected participants to EBCT resources and was moderated by two Alaska Native Trained Tobacco Specialists. Assessments were conducted online post-randomization at one, three, and six months. Outcomes were feasibility (recruitment, retention, intervention engagement), self-reported use of EBCTs, and biochemically confirmed seven-day point-prevalence smoking abstinence. RESULTS: Of intervention participants, 90% engaged (e.g., posted, commented) more than once. Study retention was 57% at six months (no group differences). The proportion utilizing EBCTs was about double for intervention compared with the control group participants at three and six months. Smoking abstinence was higher for intervention than control participants at three months (6.5% vs. 0%, p=0.16) but comparable at six months (6.4% vs. 6.7%, p=0.97). CONCLUSIONS: While additional research is needed to promote long-term cessation, this pilot trial supports recruitment feasibility during the COVID-19 pandemic, consumer uptake, and a signal for intervention efficacy on the uptake of cessation treatment and short-term smoking abstinence. IMPLICATIONS: This study is the first evaluation of a social media intervention for smoking cessation among Indigenous people. We learned that statewide Facebook recruitment of Alaska Native adults who smoke was feasible and there was a signal for the efficacy of a Facebook intervention on the uptake of evidence-based cessation treatment and short-term (three months) biochemically verified smoking abstinence. Clinically, social media platforms may complement current care models by connecting Alaska Native individuals and others living in hard-to-reach communities to cessation treatment resources.

Omicron-specific mRNA vaccine induced cross-protective immunity against ancestral SARS-CoV-2 infection with low neutralizing antibodies.

The major challenge in COVID-19 vaccine effectiveness is immune escape by SARS-CoV-2 variants. To overcome this, an Omicron-specific mRNA vaccine was designed. The extracellular domain of the spike of the Omicron variant was fused with a modified GCN4 trimerization domain with low immunogenicity (TSomi). After immunization with TSomi mRNA in hamsters, animals were challenged with SARS-CoV-2 virus. The raised nonneutralizing antibodies or cytokine secretion responses can recognize both Wuhan S and Omicron S. However, the raised antibodies neutralized SARS-CoV-2 Omicron virus infection but failed to generate Wuhan virus neutralizing antibodies. Surprisingly, TSomi mRNA immunization protected animals from Wuhan virus challenge. These data indicated that nonneutralizing antibodies or cellular immunity may play a more important role in vaccine-induced protection than previously believed. Next-generation COVID-19 vaccines using the Omicron S antigen may provide sufficient protection against ancestral or current SARS-CoV-2 variants. This article is protected by copyright. All rights reserved.

An explainable COVID-19 detection system based on human sounds.

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Traditional Chinese Medicine in Treatment of COVID-19 and Viral Disease: Efficacies and Clinical Evidence.

Coronavirus disease 2019 (COVID-19) remains an uncontained, worldwide pandemic. While battling the disease in China, the Chinese government has actively promoted the use of traditional Chinese medicine, and many studies have been conducted to determine the efficacy of traditional Chinese medicine for treating COVID-19. The present review discusses the effectiveness and safety of traditional Chinese medicine in curing COVID-19 and provides clinical evidence from all confirmed cases in China. Applications of traditional Chinese medicine and specific recipes for treating other viral infections, such as those caused by severe acute respiratory syndrome coronavirus and influenza A viruses (including H1N1), are also discussed. Studies have reported that traditional Chinese medicine treatment plays a significant role in improving clinical symptoms. Therefore, further investigation may be of high translational value in revealing novel targeted therapies for COVID-19.

Formulation of SARS-CoV-2 Spike Protein with CpG Oligodeoxynucleotides and Squalene Nanoparticles Modulates Immunological Aspects Following Intranasal Delivery.

Nasal spray vaccination is viewed as a promising strategy for inducing both mucosal and systemic protection against respiratory SARS-CoV-2 coronavirus. Toward this goal, a safe and efficacious mucosal adjuvant is necessary for the transportation of the antigen across the mucosal membrane and antigen recognition by the mucosal immune system to generate broad-spectrum immune responses. This study describes the immunological aspects of SARS-CoV-2 spike (S)-protein after being formulated with CpG oligodeoxynucleotides (ODNs) and squalene nanoparticles (termed PELC). Following intranasal delivery in mice, higher expression levels of major histocompatibility complex (MHC) class II and costimulatory molecules CD40 and CD86 on CD11c+ cells were observed at the draining superficial cervical lymph nodes in the CpG-formulated S protein group compared with those vaccinated with S protein alone. Subsequently, the activated antigen-presenting cells downstream modulated the cytokine secretion profiles and expanded the cytotoxic T lymphocyte activity of S protein-restimulated splenocytes. Interestingly, the presence of PELC synergistically enhanced cell-mediated immunity and diminished individual differences in S protein-specific immunogenicity. Regarding humoral responses, the mice vaccinated with the PELC:CpG-formulated S protein promoted the production of S protein-specific IgG in serum samples and IgA in nasal and bronchoalveolar lavage fluids. These results indicate that PELC:CpG is a potential mucosal adjuvant that promotes mucosal/systemic immune responses and cell-mediated immunity, a feature that has implications for the development of a nasal spray vaccine against COVID-19.

An explainable COVID-19 detection system based on human sounds

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Midwest rural-urban disparities in use of patient online services for COVID-19.

PURPOSE: Rural populations are disproportionately affected by the COVID-19 pandemic. We characterized urban-rural disparities in patient portal messaging utilization for COVID-19, and, of those who used the portal during its early stage in the Midwest. METHODS: We collected over 1 million portal messages generated by midwestern Mayo Clinic patients from February to August 2020. We analyzed patient-generated messages (PGMs) on COVID-19 by urban-rural locality and incorporated patients' sociodemographic factors into the analysis. FINDINGS: The urban-rural ratio of portal users, message senders, and COVID-19 message senders was 1.18, 1.31, and 1.79, indicating greater use among urban patients. The urban-rural ratio (1.69) of PGMs on COVID-19 was higher than that (1.43) of general PGMs. The urban-rural ratios of messaging were 1.72-1.85 for COVID-19-related care and 1.43-1.66 for other health care issues on COVID-19. Compared with urban patients, rural patients sent fewer messages for COVID-19 diagnosis and treatment but more messages for other reasons related to COVID-19-related health care (eg, isolation and anxiety). The frequent senders of COVID-19-related messages among rural patients were 40+ years old, women, married, and White. CONCLUSIONS: In this Midwest health system, rural patients were less likely to use patient online services during a pandemic and their reasons for its use differ from urban patients. Results suggest opportunities for increasing equity in rural patient engagement in patient portals (in particular, minority populations) for COVID-19. Public health intervention strategies could target reasons why rural patients might seek health care in a pandemic, such as social isolation and anxiety.

Anti-fatigue effect from Ginseng Radix et Rhizoma: a suggestive and promising treatment for long COVID

Two years after the coronavirus disease 2019 (COVID-19) outbreak, an increasing number of patients continue to suffer from long COVID (LC), persistent symptoms, and/or delayed or long-term complications beyond the initial 4 weeks from the onset of symptoms. Constant fatigue is one of the most common LC symptoms, leading to severely reduced quality of life among patients. Ginseng Radix et Rhizoma—known as the King of Herbs in traditional Chinese medicine—has shown clinical anti-fatigue effects. In this review, we summarize the underlying anti-fatigue mechanisms of Ginseng Radix et Rhizoma extracts and their bioactive compounds, with a special focus on anti-viral, immune remodeling, endocrine system regulation, and metabolism, suggesting that Ginseng Radix et Rhizoma is a potentially promising treatment for LC, especially in regard to targeting fatigue.

Recovery of functional fitness, lung function, and immune function in healthcare workers with nonsevere and severe COVID-19 at 13 months after discharge from the hospital: a prospective cohort study.

OBJECTIVES: This study aimed to evaluate the recovery of functional fitness, lung function, and immune function in healthcare workers (HCWs) with nonsevere and severe COVID-19 at 13 months after discharge from the hospital. METHODS: The participants of "Rehabilitation Care Project for Medical Staff Infected with COVID-19" underwent a functional fitness test (muscle strength, flexibility, and agility/dynamic balance), lung function test, and immune function test (including cytokines and lymphocyte subsets) at 13 months after discharge. RESULTS: The project included 779 HCWs (316 nonsevere COVID-19 and 463 severe COVID-19). This study found that 29.1% (130/446) of the HCWs have not yet recovered their functional fitness. The most affected lung function indicator was lung perfusion capacity (34% with diffusion capacity for carbon monoxide-single breath <80%). The increase of interleukin-6 (64/534, 12.0%) and natural killer cells (44/534, 8.2%) and the decrease of CD3+ T cells (58/534, 10.9%) and CD4+ T cells (26/534, 4.9%) still existed at 13 months after discharge. No significant difference was found in the HCWs with nonsevere and severe COVID-19 regarding recovery of functional fitness, lung function, and immune function at 13 months after discharge. CONCLUSION: The majority of Chinese HCWs with COVID-19 had recovered their functional fitness, lung function, and immune function, and the recovery status in HCWs with severe COVID-19 is no worse than that in HCWs with nonsevere COVID-19 at 13 months after discharge from the hospital.

[Clinical observation of Xuanfei Baidu Decoction in treatment of severe coronavirus disease 2019 (COVID-19)].

The present study evaluated the effectiveness and safety of Xuanfei Baidu Decoction(XFBD) for severe cases with coronavirus disease 2019(COVID-19).Forty-one patients(diagnosed as severe or critical type) admitted to Hubei Provincial Hospital of Integrated Chinese and Western Medicine and Wuhan Hospital of Traditional Chinese Medicine from February 1 to March 1, 2020, were included.All patients were treated with XFBD based on conventional therapies.Clinical outcomes, length of hospital stay, and lung CT images of patients were observed.Laboratory indicators were compared between admission and the 14 th day of treatment.Traditional Chinese medicine(TCM) symptoms and signs on the 7 th and 14 th days of treatment were also compared with baseline.The differences in clinical characteristics and clinical outcomes between XFBD and western medicine or conventional therapies were analyzed with the published trials on severe COVID-19 cases during the same period as external controls.According to the results, among the 41 cases, 40 were cured and discharged, and 1 died; the median length of hospital stay was 22 days, and the improvement rate of lung CT was 87.2%(34/39).Compared with the conditions on admission, the levels of white blood cells(WBC), C-reactive protein(CRP), fibrinogen(FIB), and lactate dehydrogenase(LDH) were reduced(P<0.05, P<0.01), and levels of procalcitonin(PCT), prothrombin time(PT), creatine kinase(CK), alanine aminotransferase(AST), total bilirubin(TBiL), and other indicators showed a downward trend.Moreover, symptoms like fever, cough, chest tightness/shortness of breath, dyspnea, head and body pain, anorexia, and greasy tongue coating were significantly improved on the 7 th and 14 th days of treatment(P<0.05, P<0.01), and fatigue was improved on the 14 th day of treatment(P<0.01).The mortality, adverse reactions, and major events of the XFBD group were significantly lower than those of the western medicine and conventional treatment groups in the same period, and the usage of antibiotics, hormones, vasopressin, and invasive mechanical ventilation during treatment were generally less than other groups.In conclusion, XFBD has good efficacy and safety in the treatment of severe COVID-19 cases by improving inflammation and clinical symptoms, promoting the absorption of lung inflammation, and reducing mortality.

Computational drug repurposing based on electronic health records: a scoping review.

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

Patient Portal Messaging for Asynchronous Virtual Care During the COVID-19 Pandemic: Retrospective Analysis.

BACKGROUND: During the COVID-19 pandemic, patient portals and their message platforms allowed remote access to health care. Utilization patterns in patient messaging during the COVID-19 crisis have not been studied thoroughly. In this work, we propose characterizing patients and their use of asynchronous virtual care for COVID-19 via a retrospective analysis of patient portal messages. OBJECTIVE: This study aimed to perform a retrospective analysis of portal messages to probe asynchronous patient responses to the COVID-19 crisis. METHODS: We collected over 2 million patient-generated messages (PGMs) at Mayo Clinic during February 1 to August 31, 2020. We analyzed descriptive statistics on PGMs related to COVID-19 and incorporated patients' sociodemographic factors into the analysis. We analyzed the PGMs on COVID-19 in terms of COVID-19-related care (eg, COVID-19 symptom self-assessment and COVID-19 tests and results) and other health issues (eg, appointment cancellation, anxiety, and depression). RESULTS: The majority of PGMs on COVID-19 pertained to COVID-19 symptom self-assessment (42.50%) and COVID-19 tests and results (30.84%). The PGMs related to COVID-19 symptom self-assessment and COVID-19 test results had dynamic patterns and peaks similar to the newly confirmed cases in the United States and in Minnesota. The trend of PGMs related to COVID-19 care plans paralleled trends in newly hospitalized cases and deaths. After an initial peak in March, the PGMs on issues such as appointment cancellations and anxiety regarding COVID-19 displayed a declining trend. The majority of message senders were 30-64 years old, married, female, White, or urban residents. This majority was an even higher proportion among patients who sent portal messages on COVID-19. CONCLUSIONS: During the COVID-19 pandemic, patients increased portal messaging utilization to address health care issues about COVID-19 (in particular, symptom self-assessment and tests and results). Trends in message usage closely followed national trends in new cases and hospitalizations. There is a wide disparity for minority and rural populations in the use of PGMs for addressing the COVID-19 crisis.

Qingjin Yiqi granules for post-COVID-19 condition: A randomized clinical trial.

OBJECTIVE: To evaluate the effectiveness and safety of Qingjin Yiqi granules (QJYQ) on post-COVID-19 condition (PCC). METHOD: Patients who met the inclusion criteria were randomly assigned to two groups, the QJYQ group received QJYQ combined with standard rehabilitation treatments (SRTs) and the control group only received SRTs. The treatment course was 14 days. The primary outcomes were modified Medical Research Council (mMRC) scale and Borg scale, while the secondary outcomes included symptoms score and 6-minute walking distance (6MWD). The safety outcome was the incidence of adverse events. RESULTS: A total of 388 patients with PCC were enrolled and randomly assigned to the QJYQ group (n = 194) and the control group (n = 194). Compared to the controls, the mMRC scale was improved in the QJYQ group, which was better than that of the control group [ß (95%CI): -0.626 (-1.101, -0.151), p = 0.010]. A significant improvement in Borg scale was also observed in the QJYQ group compared to the control group [ß (95%CI): -0.395(-0.744, -0.046), p = 0.026]. There was no statistically significant difference in symptoms score and 6MWD between the two groups (p = 0.293, p = 0.724). No treatment-related adverse events were observed in either group. CONCLUSIONS: QJYQ can bring benefits to patients with PCC, mainly in the improvement of breathlessness and fatigue.

Characterizing Patient-Clinician Communication in Secure Medical Messages: Retrospective Study.

BACKGROUND: Patient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care. OBJECTIVE: The aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester. METHODS: We performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity. RESULTS: During 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely "physician-primary care," "registered nurse-specialty," and "physician-specialty," bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The "nurse practitioner or physician assistant-primary care" and "physician-primary care" categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds. CONCLUSIONS: Our study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients' needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

The correlation between professional quality of life and mental health outcomes among hospital personnel during the Covid-19 pandemic in Taiwan

Purpose This study investigated the association between professional quality of life, working context, and mental health outcomes among hospital personnel in Taiwan during the worldwide upsurge in COVID-19 cases. Patients and Methods We recruited 503 hospital personnel to whom we administered online questionnaires containing items from the Professional Quality of Life (ProQoL) scale, which covers compassion satisfaction (CS), burnout (BO) and compassion fatigue (CF), the Depression, Anxiety and Stress Scale (DASS-21) and questions on work-related variables. Data were collected from 13 July to 19 August 2020. Results The participants generally reported moderate CS and BO and low CF. Overall prevalence of mild-to-extremely-severe stress, anxiety and depression was 24.5%, 39.6% and 31.2%, respectively. Multiple logistic regression revealed that moderate-to-high BO and CF correlated with increased risks of mild-to-extremely-severe stress (OR = 4.17 and 2.23, respectively), anxiety (OR = 4.86 and 2.81, respectively) and depression (OR = 5.83 and 3.01, respectively), while moderate-to-high CS correlated with reduced risks of stress (OR = 0.53) and depression (OR = 0.45) only. There were CS and BO differences in groups categorized by marital status and profession. Anxiety increased linearly by seniority <10, 10–19 and ≥20 years (p for trend <0.05). Conclusion In conclusion, the subscales of ProQOL, BO and CF appeared to be associated with increased risks of stress, anxiety and depression among hospital personnel during the COVID-19 epidemic. A long-term contingency program may be needed to adjust work context variables and support emotional well-being of these workers.

Hyaluronic Acid-Glycine-Cholesterol Conjugate-Based Nanoemulsion as a Potent Vaccine Adjuvant for T Cell-Mediated Immunity.

Clinical cases of allergic reaction that are due to excipients containing polyethylene glycol (PEG), a hydrophilic molecule commonly used in drug/vaccine formulations, has attracted much attention in recent years. In order to develop PEG-free adjuvants, we investigated the feasibility of natural ingredients in the human body such as hyaluronic acid in the form of hyaluronic acid-glycine cholesterol (HACH) conjugate as an excipient for vaccine formulation. Interestingly, HACH grafted with ~13 wt.% cholesterol has good water dispersity and can serve as an emulsifier to stabilize the squalene/water interfaces, yielding a milky white and isotropic emulsion (SQ@HACH) after being passed through a high-shear microfluidizer. Our results show that SQ@HACH particles possessed a unimodal average hydrodynamic diameter of approximately 190 nm measured by dynamic light scattering and exhibited good stability upon storage at 4 °C and 37 °C for over 20 weeks. The results of immunogenicity using a mouse model with ovalbumin (OVA) as the antigen revealed that SQ@HACH significantly enhanced antigen-specific immune responses, including the polarization of IgG antibodies, the cytokine secretions of T cells, and enhancement of cytotoxic T lymphocyte (CTL) activation. Moreover, SQ@HACH revealed lower local inflammation and rapidly absorbing properties compared with AlPO4 after intramuscular injection in vivo, indicating the potential functions of the HA-derived conjugate as an excipient in vaccine formulations for enhancement of T cell-mediated immunity.

A Retrospective Study of Oral Emergency Services During COVID-19.

OBJECTIVES: This study was performed to examine changes in the number of patient visits and types of oral services in an oral emergency department from the beginning to the control stage of the coronavirus disease 2019 (COVID-19) outbreak in Beijing. METHODS: The numbers of daily oral emergency visits from January 20 to March 24, 2020, at a dental university hospital in Beijing and daily newly confirmed COVID-19 cases in Beijing during the same period were collected and analysed. All oral emergency patient information (including sex, age, and oral diagnosis) was also collected and analysed. Patients with incomplete medical data were excluded. RESULTS: In total, 12,416 patients were included in this study. The number of daily emergency visits was negatively correlated with the number of newly confirmed local COVID-19 cases in Beijing (P < .001). The number of daily emergency visits during the COVID-19 stable period in Beijing was greater than that during the outbreak period (P < .001). Compared to those in the COVID-19 outbreak period, the percentages of females, children and adolescents, patients with acute toothache, and patients with nonurgent cases were higher in the stable period, and the numbers of patients with toothache, trauma, infection, and nonemergency conditions increased in the COVID-19 stable period (P < .001). CONCLUSIONS: COVID-19 significantly influenced the number of patient visits and the percentages of patients with oral emergency situations in the oral emergency department. There were obvious differences in treatment seeking for oral emergencies between the COVID-19 periods in Beijing. There was an inverse relationship between daily oral emergency visits and daily confirmed COVID-19 cases in Beijing.

Application of Pandemic Intelligence in Dynamic Data in Taiwan.

Taiwan was successful in containing the spread of the novel coronavirus (COVID-19) in 2020. One major factor in this success was the compilation and provision of comprehensive information about the pandemic. The present study proposes a pandemic intelligence system that provides data on the number of epidemic prevention professionals in each county and city, as well as daily confirmed cases, the demographics of the confirmed cases, and available resources (negative-pressure room beds and artificial ventilation apparatuses) in hospitals. Furthermore, the system provides the location of pharmacies selling masks and their current inventories, as well as the distribution of crowds at popular tourist destinations and social-distance monitoring. The most frequently used map layer in the thematic map of the pandemic is that of crowd distribution during the study period from March 2020 until the end of the same year. The case study used in this investigation for applying the system is represented by the 4-day weekend for Tomb-Sweeping Day of 2020. Through the real-time analysis of dynamic data and the integration of intelligence, the system offers a clear insight into changes in relevant information and, thus, enables the preemptive deployment of control measures by the county/city governments regarding pandemic management.