Simulation Analysis of COVID-19 Epidemic Model in Wuhan Based on SIR Model

Based on SIR model, combined with the mode of COVID-19 epidemic spread in Wuhan, the SIR model of COVID-19 epidemic spread is constructed, which mainly includes three aspects: simulation of the average number of infected people in COVID-19, simulation of cross-infection in COVID-19 and simulation of contact infection in COVID-19. Using the results of these three simulations, we can predict the spread of COVID-19 epidemic in the region, and find out the methods to prevent and control the outbreak or spread of the epidemic. © 2023 SPIE.

Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China

The additional impact of emission-reduction measures in North China (NC) during autumn and winter on the air quality of downwind regions is an interesting but less addressed topic. The mass concentrations of routine air pollutants, the chemical compositions, and sources of fine particles (PM2.5) for January 2018, 2019, and 2020 at a megacity of Central China were identified, and meteorology-isolated by a machine-learning technique. Their variations were classified according to air mass direction. An unexpectedly sharp increase in emission-related PM2.5 by 22.7% (18.0 mug m-3) and 25.7% (19.4 mug m-3) for air masses from local and NC in 2019 was observed compared to those of 2018. Organic materials exhibited the highest increase in PM2.5 compositions by 6.90 mug m-3 and 6.23 mug m-3 for the air masses from local and NC. PM2.5 source contributions related to emission showed an upsurge from 1.39 mug m-3 (biomass burning) to 24.9 mug m-3 (secondary inorganic aerosol) in 2019 except for industrial processes, while all reduced in 2020. From 2018 to 2020, the emission-related contribution of coal combustion to PM2.5 increased from 10.0% to 19.0% for air masses from the local area. To support the priority natural gas quotas in northern China, additional coal in cities of southern China was consumed, raising related emissions from transportation activities and road dust in urban regions, as well as additional biofuel consumption in suburban or rural regions. All these activities could explain the increased primary PM2.5 and related precursor NO2. This study gave substantial evidence of air pollution control measures impacting the downwind regions and promote the necessity of air pollution joint control across the administration.Copyright © 2023 Elsevier Ltd

The growth, adventitious bud formation, bioactive flavonoid production, antioxidant response, and cryptochrome-mediated light signal transduction in Isatis tinctoria L. hairy root cultures exposed to LED lights

The root of Isatis tinctoria L. is highly appreciated as a Traditional Chinese herbal medicine for the prevention and adjuvant treatment of respiratory diseases caused by coronaviruses viruses such as SARS and COVID-19. I. tinctoria hairy root cultures (ITHRCs) provide a better alternative to field cultivation for the production of antiviral flavonoids. For the first time, ITHRCs were exposed to different colors of LED lights i.e., red, green, blue, red/green/blue (1/1/1, RGB), and white, in an attempt to promote the root growth and enhance the production of bioactive flavonoids. Results revealed that the biomass productivity (7.15 ± 0.63 g/L) in ITHRCs with an initial inoculum size of 0.2% cultured for 50 days under blue light increased by 1.86-fold relative to that under dark (control), and yields of rutin (320.49 ± 27.56 μg/g DW), quercetin (388.75 ± 9.17 μg/g DW), kaempferol (787.90 ± 83.43 μg/g DW), and isorhamnetin (269.11 ± 20.08 μg/g DW) increased by 4.15-fold, 9.31-fold, 9.09-fold, and 2.88-fold as compared with control, respectively. Interestingly, the emergence of adventitious buds was noticed in ITHRCs under all light treatments. Additionally, the enhanced densities of chloroplasts and root hairs were found in blue-light grown ITHRCs as against control, which might account for the elevated biomass productivity. Moreover, blue light induced oxidative stress in ITHRCs in terms of the overproduction of oxidation products and the enhancement of antioxidant enzyme activity. Furthermore, blue light significantly activated photoreceptor (CRY1) and key regulator of light signaling (HY5), thus leading to the up-regulated expression of MYB4 and structural genes (such as CHS and FLS) responsible for flavonoid biosynthesis. And, the transcriptional activation of CUC1 was likely related to the formation of adventitious buds in ITHRCs. Overall, the simple supplementation of blue LED light makes ITHRCs more attractive as plant factories for obtaining higher productivity of biomass and medicinally important flavonoids. © 2023 Elsevier B.V.

Predicting Study or Work from Home Satisfaction using Data Mining Technique: A Case Study in Malaysia

The COVID-19 pandemic has urged the government of Malaysia to implement Movement Control Order (MCO) which forces working people to work from home while students to study from home. People's satisfaction on work from home is crucial in determining their work productivity and efficiency whereas student's satisfaction on study from home is important for their learning effectiveness. There is no work has been done yet for exploring data mining techniques to build a model for predicting work or study from home satisfaction using Malaysia as a case study. This paper aimed to identify the best data mining model for predicting the work or study from home satisfaction. The prediction model is learned by analyzing the demographic, the personality traits, and the work from home experience collected from a group of Malaysia people. This study attempts to investigate four data mining techniques that are the decision tree, linear kernel support vector machine, polynomial support vector machine, and radial basis support vector machine. Experiment results show that the radial basis support vector machine outperformed other techniques in predicting the work or study from home satisfaction of Malaysia's community. © 2022 IEEE.

Research and Application of Intelligent Community Based on Naive Bayes

Community service has become an indispensable source of service in people's life, but the traditional way of service can't meet people's fast pace of life. Therefore, smart community (smart city) appears in people's vision, which can greatly improve the quality of our life and reduce the inconvenient factors that life may bring. The smart community designed and studied in this paper is divided into five functional modules: user login and registration function, function selection, personnel management, vehicle management, health management module. Among them, the login function, function selection, personnel management module is to interact with the database, to achieve the transmission of information. The health management module is based on the naive Bayes algorithm to classify the safety level of COVID-19, a currently popular influenza virus, to realize the health monitoring of community personnel and prevent the continuous spread of Novel Coronavirus. The intelligent community can ensure the integrity of data and information, simple operation, perfect function and superior performance. At the same time, the intelligent community combined with the naive Bayes algorithm for health management design, embodies the development principle of intelligent community, people-oriented. © 2022 IEEE.

Technology as Treatment: Use of Virtual Complementary and Integrative Therapies by Patients in Neurology

Objective: Given the reductions in in-person visits and the increases in teleneurology visits, we sought to determine whether patients increased their use of virtual complementary and integrative therapies. Background: Patients with neurological disorders may seek treatment options in addition to those recommended by their providers. Prior to the COVID-19 pandemic, about half of patients from populations that sought care in neurology tried complementary and integrative therapies (CITs). Design/Methods: By examining two separate datasets that included cohorts of patients with neurological disorders, we assessed patients' use of virtual (and non-virtual) CITs and determined whether there were clinical characteristics that predicted their use. The two studies that comprised this report included one that examined patient and provider experiences with teleneurology visits, and another that assessed patients with a history of COVID-19 infection who presented for neurologic evaluation. Results: Patients who postponed medical treatment for non-COVID-19-related problems during the pandemic were more likely to seek CITs. Virtual exercise, virtual psychotherapy and relaxation/meditation smartphone applications were the most frequent types of virtual CITs chosen by patients. In both studies, age was a key demographic factor associated with mobile/virtual CIT usage. Conclusions: Data from our investigations demonstrated that, in addition to its other roles in teleneurology, CIT-related technologies may be utilized in the treatment of neurologic conditions.

Analyzing the Current State of Sleep Apps in the App Marketplace and in Literature: A Systematic Review

Objective: To analyze the characteristics of commercially available sleep-exclusive apps using the Mobile Health Index and Navigation (MIND) apps database published by the Division of Digital Psychiatry at BIDMC for app review. Background: Over 70 million Americans endure a chronic sleep disorder during their lifetime. Due to the COVID-19 pandemic, mobile app benefits have been postulated as a means of expanding patient access to scientifically-based interventions in aiding their sleep disorders. With the increasing adoption of mobile health apps, it is critical that information regarding these mobile apps are sufficiently analyzed. Design/Methods: Using pre-specified criteria, we identified 106 sleep related mobile apps in the iOS and Google Playstores, and rated them using a 105 question review that is part of the MIND database. Questions focused on: Accessibility, Privacy & Security, Clinical Foundation, Engagement Style, and Interoperability. The features of the 106 apps were compared to a control group of non-sleep apps to evaluate the features and limitations sleep apps bear relative to the remainder of the marketplace. Results: The most common features of sleep apps were mindfulness (68.9%), deep breathing (54.7%), and psychoeducation (30.2%). Regarding functionality, 39.6% of apps have accessibility features and 49.1% can be used offline. However, sleep apps lack sleep trackers (22.6%), exportability features (21.7%), and opportunities to collaborate with one's provider (7.6%). Only 10.4% of sleep apps have an efficacy study. Conclusions: Sleep apps in the marketplace are primarily used to help the user fall and stay asleep. In its current state, sleep apps can assist only the user as a self-help tool. The lack of sleep tracking, support, exportability, the inability to collaborate with one's provider and efficacy studies limit the potential of implementing sleep apps for clinical use.

Features, Functionalities, and the Future of Commercial Pain Applications

Objective: We aimed to assess the characteristics of the pain application (app) marketplace and determine improvements to mobile health (m Health) that could assist patients with pain disorders in obtaining more accessible care. Background: During the COVID-19 pandemic, complementary and integrative therapies delivered via mHealth technologies have become more popular due to their accessibility. As more users are downloading apps to address health concerns, it is important to analyze the ever growing marketplace's features and limitations. Design/Methods: After searching the iOS and Google Play Stores for commercially available pain apps that met pre-specified search criteria, trained app raters entered 47 apps into the Mobile-Health Index and Navigation Database (MIND) published by the Division of Digital Psychiatry at BIDMC. Through a 105-question objective rating framework, each app was evaluated for its accessibility, privacy, cost, functionalities, features, and clinical foundations. The data was compared with a control group of non-pain apps-matched for platform and cost -to examine the traits of pain apps in contrast with the rest of the marketplace. Results: The top features were 44.7% (21/47) offered symptom tracking, 34.0% (16/47) offered medication tracking, and 42.6% (20/47) offered physical health exercises. Just 2.13% (1/47) of apps offered HIPAA privacy protections or had a crisis management feature. Additionally, only 10.64% (5/47) of pain apps were supported by efficacy studies. Conclusions: Commercially available pain apps are typically used to record pain episodes or document medications. However, the lack of privacy, crisis management, and efficacy studies may be preventing their clinical implementation.

IMPACT OF POSTOPERATIVE TELEMEDICINE VISIT VS. IN-PERSON VISIT ON PATIENT SATISFACTION DURING THE COVID-19 PANDEMIC: A RANDOMIZED CLINICAL TRIAL

Purpose/Background: Although telemedicine utilization has increased dramatically during the COVID-19 pandemic, the impact of telemedicine vs. in-person postoperative visits on patient satisfaction has not been studied. Hypothesis/Aim: We hypothesized that telemedicine visits would be non-inferior to in-person visits in terms of postoperative colorectal surgery patient satisfaction. Methods/Interventions: We conducted a randomized non-inferiority trial from September 2020 to February 2021 comparing postoperative telemedicine visit (Arm T) or in-person clinic visit (Arm I) after trans-abdominal colorectal surgery. Key inclusion criteria were patients age ≥ 18 undergoing trans-abdominal colorectal surgery and patients with a computer and/ or mobile phone with both audio and video capabilities. Patients who required planned physical intervention during their first postoperative visit (e.g. drain removal) and patients undergoing trans-anal or anorectal procedures only were excluded. Patients in the experimental group (Arm T) received their first postoperative visit via telemedicine. Patients in the control group (Arm I) received their first postoperative visit in person. All participants were asked to complete a seven-item patient satisfaction survey electronically within 24 hours after each postoperative visit, which was scored out of a total of 35. The primary endpoint was total patient satisfaction score. Secondary endpoints included patient-reported safety, length of visit, willingness of patients to recommend the practice to their peers, 60-day rate of readmission, and 60-day rate of re-operation. Results/Outcome(s): A total of 46 patients were analyzed with 23 each in Arm T and Arm I. The mean age of our study cohort was 50.6 (SD 17.7) years and 52% were female. No significant differences were found between groups in terms of baseline characteristics. With respect to the primary endpoint of total satisfaction score out of 35, mean difference in total scores between patients in Arm T vs. patients in Arm I was -0.6 (97.5% CI -1.7 - ∞), excluding the non-inferiority limit Δ of -2, demonstrating that patient satisfaction scores in Arm T were non-inferior to those in Arm I. There were no significant differences between groups in terms of secondary endpoints. Limitations: Patients who did not have a computer or mobile device with both video and audio capabilities were excluded, which may have introduced selection bias. The conduction of our trial during the COVID-19 pandemic may have influenced patient desire to participate in telemedicine in order to maintain social distancing, which may have resulted in a more expeditious recruitment process compared to non-pandemic times. Conclusions/Discussion: Postoperative telemedicine visits were a safe and time-efficient option that maintained high patient satisfaction compared to in-person postoperative visits during the COVID-19 pandemic. (Table Presented).

Global Convolutional Neural Processes

The ability to deal with uncertainty in machine learning models has become equally, if not more, crucial to their predictive ability itself. For instance, during the pandemic, governmental policies and personal decisions are constantly made around uncertainties. Targeting this, Neural Process Families (NPFs) have recently shone a light on prediction with uncertainties by bridging Gaussian processes and neural networks. Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties). Nonetheless, some critical questions remain unresolved, such as a formal definition of global uncertainties, the causality behind global uncertainties, and the manipulation of global uncertainties for generative models. Regarding this, we build a member GloBal Convolutional Neural Process(GBCoNP) that achieves the SOTA log-likelihood in latent NPFs. It designs a global uncertainty representation p(z), which is an aggregation on a discretized input space. The causal effect between the degree of global uncertainty and the intra-task diversity is discussed. The learnt prior is analyzed on a variety of scenarios, including 1D, 2D, and a newly proposed spatial-temporal COVID dataset. Our manipulation of the global uncertainty not only achieves generating the desired samples to tackle few-shot learning, but also enables the probability evaluation on the functional priors. © 2021 IEEE.

Energy Consumption in Commercial Buildings in a Post-COVID-19 World

The ripple effects of the pandemic have resulted in an unprecedented shift in sectoral energy consumption as the workforce predominantly stays and works from home. Quantifying the impact of these restrictions on energy consumption offers a new direction towards intelligent energy services in a post coronavirus (post-COVID-19) world, especially for commercial buildings. Thus, utilising actual power consumption data, the study evaluates how energy usage in commercial buildings can change in a post-COVID-19 world, whilst examining the impact of digitalisation to identifying potential new opportunities. The paper analyses the changes in energy demand with occupancy rate based on data from 126 commercial businesses with varied classes across Manchester, United Kingdom. The results show that the reduction in energy demand is not proportionate to the occupancy level, resulting in high energy costs. For instance, an average footfall for February 2021 is 10% of 2020, while the costs of electricity only fall to 80% of 2020. Although most of the energy demand is from appliances, the absence of energy efficiency increases energy consumption, highlighting the urgent need for optimised energy efficiency measures to include the time of use and scheduled use of energy across people and processes. IEEE

RAU: An Interpretable Automatic Infection Diagnosis of COVID-19 Pneumonia with Residual Attention U-Net

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused a significant impact on public health and economy. However, there is still lack of studies on effectively quantifying the different lung infection areas caused by COVID-19. As a basic but challenging task of the diagnostic framework, distinguish infection areas in computed tomography (CT) images and help radiologists to determine the severity of the infection rapidly. To this end, we proposed a novel deep learning algorithm for automated infection diagnosis of multiple COVID-19 Pneumonia. Specifically, we use the aggregated residual network to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, the proposed method achieves 0.91 DSC which is 14.6% higher than selected baselines. Experimental results demonstrate the outstanding performance of our proposed model for the automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep learning-based segmentation tool to lay a foundation to facilitate the quantitative diagnosis of COVID-19 lung infection in CT images. © 2021, Springer Nature Switzerland AG.

Clinical characteristics and prognosis of COVID-19 patients with tracheal intubation

This study aimed to describe the clinical characteristics and prognosis of COVID-19 patients who received tracheal intubation and mechanical ventilation. A total of 52 critical COVID-19 patients who received tracheal intubation were retrospectively included. The primary data including clinical features, laboratory results, and the outcomes were collected and analyzed. Among the 52 patients who received tracheal intubation, 14 were successfully extubated within two weeks and 38 failed extubation. The patients in the extubation failure group were significantly older than the patients in the successful extubation group (median age, 67.50 years vs 55.50 years). The median values of SpO2 and the PaO2/FiO2 (P/F) before tracheal intubation were significantly lower in extubation failure group than the those in successful extubation group (SpO2: 78.50% vs 85%, P/F: 71.50 mmHg vs 84.50 mmHg). Compared with the successful extubation group, the extubation failure group was found to have a significantly lower 28-day survival rate (21.05% vs 100%). Patients with extubation failure had more severe multi-organ injuries. Besides, a more severe hypoxia level was found to be associated with the failure of extubation and subsequent poor prognosis. Therefore, tracheal intubation and timely invasive mechanical ventilation should be administered in COVID-19 patients with refractory hypoxemia.

Effectiveness of personal protective health behaviour against COVID-19

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) has become a pandemic, and over 80 million cases and over 1.8 million deaths were reported in 2020. This highly contagious virus is spread primarily via respiratory droplets from face-to-face contact and contaminated surfaces as well as potential aerosol spread. Over half of transmissions occur from presymptomatic and asymptomatic carriers. Although several vaccines are currently available for emergency use, there are uncertainties regarding the duration of protection and the efficacy of preventing asymptomatic spread. Thus, personal protective health behaviour and measures against COVID-19 are still widely recommended after immunization. This study aimed to clarify the efficacy of these measures, and the results may provide valuable guidance to policymakers to educate the general public about how to reduce the individual-level risk of COVID-19 infection. METHODS: This case-control study enrolled 24 laboratory-confirmed COVID-19 patients from Centro Hospitalar Conde de Sao Januario (C.H.C.S.J.), which was the only hospital designated to manage COVID-19 patients in Macao SAR, China, and 1113 control participants who completed a 14-day mandatory quarantine in 12 designated hotels due to returning from high-risk countries between 17 March and 15 April 2020. A questionnaire was developed to extract demographic information, contact history, and personal health behaviour. RESULTS: Participants primarily came from the United Kingdom (33.2%), followed by the United States (10.5%) and Portugal (10.2%). Independent factors for COVID-19 infection were having physical contact with confirmed/suspected COVID-19 patients (adjusted OR, 12.108 [95% CI, 3.380-43.376], P < 0.005), participating in high-risk gathering activities (adjusted OR, 1.129 [95% CI, 1.048-1.216], P < 0.005), handwashing after outdoor activity (adjusted OR, 0.021 [95% CI, 0.003-0.134], P < 0.005), handwashing before touching the mouth and nose area (adjusted OR, 0.303 [95% CI, 0.114-0.808], P < 0.05), and wearing a mask whenever outdoors (adjusted OR, 0.307 [95% CI, 0.109-0.867], P < 0.05). The daily count of handwashing remained similar between groups. Only 31.6% of participants had a sufficient 20-s handwashing duration. CONCLUSIONS: Participating in high-risk gatherings, wearing a mask whenever outdoors, and practising hand hygiene at key times should be advocated to the public to mitigate COVID-19 infection.

Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19: An evidence-based clinical practice guideline (updated version)

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)";now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis [including agents and Traditional Chinese Medicine (TCM) agents], diagnosis [including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest X-ray, and CT features of asymptomatic infections], treatments [including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, Qingfei Paidu decoction, Lianhua Qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)], and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients. © 2020 People's Military Medical Press. All rights reserved.

Factors influencing health behaviours during the coronavirus disease 2019 outbreak in China: an extended information-motivation-behaviour skills model.

OBJECTIVES: This study explored the factors influencing health behaviours during the coronavirus disease 2019 (COVID-19) outbreak in China. The impact of perceived stress and positive perception of interventions on health behaviours in China were assessed using the extended information-motivation-behaviour skills (IMB) model. STUDY DESIGN: Cross-sectional survey. METHODS: The Questionstar online survey tool was used to construct a structured questionnaire based on the IMB model. Between 14 and 22 February 2020, during the peak of COVID-19 epidemic in China, 2449 participants were recruited by snowball sampling on WeChat and Tencent QQ social media platforms in China. Data were collected through an online questionnaire, and structural equation modelling was performed to evaluate the extended IMB model. RESULTS: Health behaviours were assessed using a scoring system (total score range: 8-40); the average health behaviour score in this study was 34.62 ± 4.44. The term 'health risk stress' refers to the impact that perceived stress has on health, and this was experienced by 39.9% of participants. Only 35.9% of participants answered all seven questions on COVID-19 information correctly. The final model showed that information, motivation, behavioural skills, heath risk stress and positive perception of interventions had significant direct effects on health behaviours. Health behaviours were positively associated with the positive perception of interventions but negatively associated with health risk stress. Behavioural skills had the greatest impact on health behaviours. CONCLUSIONS: In the face of public health emergencies, the extended IMB model has been used as a theoretical framework to construct more effective interventions. The government should pay attention to publicity and guidance, strengthen positive interactions with the public and disclose relevant information in a timely manner to gain trust and to maintain the positive public perception of the interventions. In terms of health education, the government should focus on behavioural skills, promptly rectify ineffective prevention information and raise awareness about the disease to relieve stress and anxiety in the population.

[Analysis of selective endoscopy results during the epidemic of coronavirus disease 2019 (COVID-19)].

Objective: To explore the necessity and safety of selective endoscopy to detect gastrointestinal (GI) malignancy during the outbreak of coronavirus disease 2019 (COVID-19). Methods: A retrospective cohort study was carried out to analyze the clinical data of selective endoscopy performed at the Endoscopic Center, Zhongshan Hospital of Fudan University from February 20 to March 6, 2020. Clinical data included epidemiological questionnaire, chief complaints, endoscopic findings and biopsy pathology results, etc. All medical staff had blood test for IgM/IgG antibodies of COVID-19. Patients and their families were followed up by phone to determine whether they were infected with COVID-19. Meanwhile, the clinical data of selective endoscopy during the same period from February 20 to March 6, 2019 were collected as the control group to compare the overall results of endoscopy examinations during the epidemic and the detection rate of GI malignancy. Results: A total of 911 patients underwent endoscopy in the epidemic period group, and a total of 5746 cases in the control group, which was 6.3 times over the epidemic period group. In the epidemic period group, 544 cases received gastroscopy and 367 cases received colonoscopy, while 3433 cases received gastroscopy and 2313 cases received colonoscopy in the control group, which were both 6.3 times of epidemic period group. Gastroscopy revealed that 39 patients (7.2%) were diagnosed with upper GI malignancies in the epidemic period group and 77 patients (2.2%) in the control group with significant difference (χ(2)=40.243, P<0.001). The detection rate of gastric cancer in these two groups was 3.3% (n=18) and 1.7% (n=59) respectively with significant difference (χ(2)=6.254,P=0.012). The detection rate of esophageal cancer was 3.7% (n=20) and 0.5% (n=18) respectively with significant difference (χ(2)=49.303,P<0.001). Colonoscopy revealed that colorectal cancer was found in 32 cases (8.7%) of the epidemic period group and 88 cases (3.8%) of the control group with significant difference (χ(2)=17.888, P<0.001). During the epidemic period, no infection of medical staff was found through the blood test of IgM/IgG antibodies on COVID-19. No patient and family members were infected with COVID-19 by phone follow-up. Conclusion: Compared with the same period in 2019, the number of selective endoscopy decreases sharply during the epidemic period, while the detection rate of various GI malignant tumors increases significantly, which indicates that patients with high-risk symptoms of GI malignancies should still receive endoscopy as soon as possible. Provided strict adherence to the epidemic prevention standards formulated by the state and professional societies, it is necessary to carry out clinical diagnosis and treatment as soon as possible.