Impact of Teachers' Support Strategies on Students' Engagement of Online College English Class Experiences

During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.

A meta-analysis of dynamic change of the SARS-CoV-2 specific antibody IgG positive rate in convalescent patients with COVID-19 in China

Objective To investigate the dynamic changes of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) specific antibody IgG positive rate in coronavirus disease 2019 (COVID-19) survivors in China. Methods the relevant literatures about the positive rate of SARS-COV-2 specific antibody IgG in COVID-19 survivors in China were retrieved from PubMed, Embase, CNKI, Wanfang database and VIP database from December 2019 to February 24, 2022. The quality of the documents were assessed according the revised AHRQ (Agency for Healthcare Research and Quality) statement. Freeman-tukey double arsinusoidal conversion method was used to calculate the positive rate, and StataSE15.0 software was used for statistical analysis. Subgroup analysis was performed according to detection method and fragment, and publication bias was examined by Egger method. Results A total of 12 articles were included, IgG was detected from the first month to the twelfth month after SARS-COV-2 infection, and the total sample size ranged from 74 to 2 907 cases per month. The positive rate was the highest in the second month and the third month, 96.35% (95% CI: 93.98%-98.14%) and 97.23% (95% CI: 94.47%-99.05%) respectively. The positive rate decreased gradually with time, and reached 73.63% (95% CI: 50.31%-91.45%) in the twelfth month. The results of subgroup analysis showed that the heterogeneity between studies with the different detection method and the different detection fragment were significant differences (X2=5.02-39.57, all P < 0.05). Egger method test published bias, and the difference was not statistically significant (t=1.85, P=0.101). Conclusion Most people, one year after infection with SARSCOV- 2, could still detect SARS-COV-2 specific antibody IgG.

Research on Recognition and Analysis of Teacher–Student Behavior Based on a Blended Synchronous Classroom

Due to the impact of the COVID-19 pandemic, many students are unable to attend face-to-face courses, Therefore, in this case, distance education should be promoted to replace face-to-face education. However, because of the imbalance of education in different regions, such as the imbalance of education resources between rural and urban areas, the quality of distance education may not be guaranteed. Therefore, in China and some regions, there have been efforts made to carry out blended synchronous classroom attempts. In hybrid synchronous classroom situations, teachers' workloads have increased, and it is difficult to fully understand students' learning efficiency and class participation. We use deep learning to identify the behaviors of teachers and students in a blended synchronous classroom-based situation, aiming to automate the analysis of classroom videos, which can help teachers in classroom reflection and summary in a blended synchronous classroom or face-to-face classroom. In the behavior recognition of students and teachers, we combine the head, hand, and body posture information of teachers and students and add the feature pyramid (FPN) and convolutional block attention module (CBAM) for comparative experiments. Finally, S–T (student–teacher) analysis and engagement analysis were carried out on the identification results. © 2023 by the authors.

Research on Anti-epidemic Material Delivery Robot

After the occurrence of the COVID-19, preventing cross infection has become a top priority. Therefore, it is proposed to use robots to replace people to distribute anti epidemic materials, so as to reduce human contact. By planning the trajectory of the robot in advance, and using mechanical arms and claws to achieve accurate grasp and delivery of anti epidemic materials, it can carry out material distribution in the isolated inpatient department, and can independently locate and deliver products, goods, etc. in a complex environment. It has strong cargo carrying capacity, and has the dual functions of traditional delivery robots and indoor delivery services. Its use can greatly reduce the infection rate in the epidemic and deliver materials in time. © 2022 IEEE.

Mission-based Academic Tweeting: Analysis of Content Engagement over One Year

Background. The Baylor College of Medicine Infectious Diseases Fellowship program (BCM ID) launched a new academic Twitter account during the COVID-19 pandemic with the mission to promote the achievements of fellows and faculty (promotion-based tweets, or PBT) and disseminate original educational material (education-based tweets, or EBT) during a fellowship recruitment season that became virtual due to the pandemic. Account content was developed by both ID fellows and faculty, with the goal of increasing social media engagement with the fellowship program. Currently, the average Twitter engagement rate per tweet is 0.037% across all industries and 0.7% for higher education accounts. We looked at the engagement rate of missionbased tweets during the inaugural year of the BCM ID Fellowship twitter account. Methods. Weconducted a retrospective reviewof tweets published on the BCMID Fellowship account during the first year of operations (8/1/20-7/31/21). We reviewed 64 mission-based tweets for impressions (number of times a tweet is shown to users), engagement (number of times users interacted with a tweet), and reach (number of followers). We calculated engagement rate (engagement-to-impression ratio, or E:I) for each tweet and compared the engagement rates between EBT and PBT. We also examined the trend of followers over time. Data were collected in October 2021. Results. EBT comprised 41% of total tweets, and PBT comprised the remaining 59%. EBT averaged 3,662 impressions and 458 engagements, for an E:I of 9.6%. PBT averaged 2,449 impressions and 130 engagements, for an E:I of 5.3% (p = 0.007). Despite a decrease in total posts per month over this time period, follower count continued to increase, and monthly engagement rate per tweet remained above 1%. Conclusion. Our experience suggests that user engagement is higher for EBT. Programs planning to launch a new academic Twitter account should consider focusing initial content on education to maximize overall engagement and reach. One limitation of this study is that frequency of tweets also impacts engagement, which may confound data from later in the inaugural year, when tweeting from the account became less frequent.

A Practical Approach to Antimicrobial Stewardship Education: Engaging Infectious Diseases Trainees in Targeted Outpatient Provider Feedback Program

Background. A 2016 needs assessment survey of infectious diseases (ID) training program directors revealed gaps in ID fellows' education including the application of antimicrobial stewardship into clinical practice. Direct involvement of ID trainees in inpatient antimicrobial stewardship (AS) activities has previously been described with favorable outcomes and achievement of program goals. Engagement of ID fellows in outpatient (OP) AS activities including targeted provider feedback has not been well described and may provide opportunity for more hands-on OP AS experience for trainees. Methods. Our OP AS program developed a 4-week virtual rotation experience for ID fellows to meet organizational needs for AS education during the COVID-19 pandemic. Acute respiratory tract infection (ARI) was chosen as a disease state priority. The expected progression of the ID fellow was as follows: Week 1: Review Core Elements of outpatient AS, regulatory requirements, and disease state priorities;review available stewardship tracking data sources;Week 2: Review facility-level antibiotic use data for a single outpatient clinic, and identify top 3 antibiotic prescribers in the clinic;Week 3: Deliver a virtual outpatient AS presentation to all providers at a single clinic, and create antibiotic report cards (See Figure 1) for top antibiotic prescribers;Week 4: Conduct 15-min targeted provider feedback sessions using antibiotic report cards for top 3 providers. Example Antibiotic Report Card Results. Between September 2021-April 2022, three ID fellows completed the OP AS rotation experience. All fellows completed each week of the rotation successfully. Solicitation of feedback from providers receiving targeted feedback sessions (See Figure 2) is ongoing, and a standardized feedback form will be sent to all ID fellows completing the rotation at the end of the academic year to evaluate the program's success in meeting pre-specified learning objectives. The rate of antibiotic prescribing for uncomplicated acute respiratory tract infection will be re-evaluated post-intervention for providers receiving targeted education. Targeted Provider Feedback Survey Conclusion. Direct involvement of ID fellows in OP AS activities may be mutually beneficial for trainees and OP AS programs. A virtual format for AS education and intervention was practical and feasible.

Impact of an Interdisciplinary Outpatient Parenteral Antimicrobial Therapy (OPAT) Clinic During the COVID-19 Pandemic

Background. At the Michael E. DeBakey Veterans Affairs Medical Center, an interdisciplinary outpatient parenteral antimicrobial therapy (OPAT) clinic was developed in August 2020 during the COVID-19 pandemic (Figure 1). Standardized weekly safety monitoring and clinical follow-up was implemented via tele-OPAT visits by an infectious diseases (ID) pharmacist or ID PA with ID physician oversight. This study aimed to describe the practices implemented through initiation of our OPAT clinic, and its impact on clinical outcomes. Figure 1: Description of intervention and clinic model Methods. This retrospective cohort study compared clinical outcomes of veterans receiving home OPAT one year pre- and post-implementation of a dedicated OPAT clinic. OPAT episodes initiated during the intervention period, prescribed for less than 7 days, not completed at time of analysis, administered with hospice services, or at a hemodialysis center, infusion suite, skilled nursing facility or long-term care facility were excluded. The primary endpoint was treatment failure during or within 30 days of OPAT completion defined as requiring repeat OPAT course for the same infection, unplanned admission, unplanned surgical intervention or procedure for additional source control, or death from any cause. Results. A total of 191 OPAT episodes were included in the analysis (preintervention group, n= 76 vs. post-intervention, n=115). The most common indications for OPAT included bone/joint infections (34%), bacteremia (24%), and endocarditis (13%). Treatment failure was lower in the post-intervention vs. preintervention group (29% vs 46%, p = 0.01) with a median time to treatment failure of 24 days. Treatment failures in both groups were primarily driven by unplanned hospital admissions (Table 3). The median time to ID follow-up from OPAT initiation was shorter in the post-intervention vs. pre-intervention arms (6 days vs. 9 days, p< 0.001). The adverse event rate was higher (13% vs. 4%, p= 0.06), and modifications were made more frequently in the post-intervention (49% vs. 29%, p=0.007). Table 1: Demographics and baseline characteristics Table 2: OPAT indications, antimicrobials, and microbiology Table 3: OPAT follow-up, complications, and treatment failure Conclusion. Implementation of an interdisciplinary OPAT clinic led to shorter time to ID follow-up, more frequent OPAT modifications, and reduced treatment failure. The median time to treatment failure suggests the need for ongoing surveillance beyond the initial 2 weeks of OPAT.

Natural Language Processing for Scientific Paper Evaluation: Comparing Human and Machine Judgements

A huge number of papers have been published about COVID-19. So much it's overwhelming. Many papers appear on preprint servers such as arXiv before publication. Researchers and clinicians can get ahead of the curve by making use of these preprint papers, but how to tell what is worth reading? Could there be an automated recommendation mechanism? In this paper we address the question by experimenting with SPECTER document-level vector embedding which establishes the representations by incorporating state-of-the-art Transformer models, such as SciBERT, a BERT variant tailored to scientific text. Meanwhile, the dataset we choose to apply SPECTER embedding is the CORD-19 dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Production Capacity Reserve Strategy of Emergency Medical Supplies: Incentive Model for Nonprofit Organizations

In 2020, COVID-19 swept across the globe. To reduce the social harms caused by this public health event, nonprofit organizations (NPOs) cooperated with medical enterprises to produce reserves of emergency medical supplies. In practice, this cooperation was challenged by the different goals of NPOs and medical enterprises and the asymmetry of information between these parties. Enterprises are prone to irregularities or speculative behaviors that can result in insufficient production capacity during public health events, which increase disaster risks. Based on the principal–agent relationship of NPOs and enterprises, this study analyzed a game model between NPOs and enterprises under information asymmetry;constructed an incentive model for reserve emergency medical supply production capacity;and solved the optimal reward and punishment coefficients of NPOs, optimal effort level of enterprises, and benefits of disaster reduction. The study also verified the validity of the model using numerical examples and a sensitivity analysis. In taking up the findings of the study, this paper discusses the effects of several important exogenous variables on the optimal decision strategies of NPOs and enterprises and offers management-related insights for NPOs. © 2022 by the authors.

Alternative role of motif B in template dependent polymerase inhibition

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on the central molecular machine RNA-dependent RNA polymerase (RdRp) for the viral replication and transcription. Remdesivir at the template strand has been shown to effectively inhibit the RNA synthesis in SARS-CoV-2 RdRp by deactivating not only the complementary UTP incorporation but also the next nucleotide addition. However, the underlying molecular mechanism of the second inhibitory point remains unclear. In this work, we have performed molecular dynamics simulations and demonstrated that such inhibition has not directly acted on the nucleotide addition at the active site. Instead, the translocation of Remdesivir from +1 to-1 site is hindered thermodynamically as the post-Translocation state is less stable than the pre-Translocation state due to the motif B residue G683. Moreover, another conserved residue S682 on motif B further hinders the dynamic translocation of Remdesivir due to the steric clash with the 1′-cyano substitution. Overall, our study has unveiled an alternative role of motif B in mediating the translocation when Remdesivir is present in the template strand and complemented our understanding about the inhibitory mechanisms exerted by Remdesivir on the RNA synthesis in SARS-CoV-2 RdRp. © 2022 Chinese Physical Society.

Global COVID-19 development trend forecast based on machine learning

The outbreak of COVID-19 not only affects people's health, but also hinders the pace of economic progress of various countries. Our goal was to develop a prediction model based on machine learning, which could be used to predict development trend of COVID-19 in the future. It can provide governments and health authorities with useful information conducive to decision-making. Considering that the propagation of COVID-19 is affected by many factors and a single prediction model lacks all-round monitoring of the data set, the ARIMA-SVM integration model was established by using the global cumulative number of confirmed cases. The individual models of ARIMA and SVM were used to predict the COVID-19 trend. Based on the prediction results of the above prediction model, a new integration forecast model was formed through a combination of weighted weights. Finally, the forecast results of the combined model and the individual model were compared. The prediction performance of models were compared according to Mean Absolute Percentage Error (MAPE). The prediction results showed that the MAPE values of ARIMA model, SVM model and ARIMA-SVM integration model were 15.843%, 1.251%, 1.132% respectively. Compared with the traditional machine learning models ARIMA and SVM, the combined model has reduced the average absolute error percentage by 92.103% and 9.51%, respectively, and can achieve more accurate and reliable COVID-19 trend prediction. It used two single models to complement each other, reduced the systematic error of the prediction model, and significantly improved the prediction effect. © 2022 IEEE.

Rural Physicians' Duties and Responsibilities in COVID-19 Pandemic Containment: an Empirical Study from the Perspective of Governance in Primary Care

Background: COVID-19 pandemic containment in rural areas is the frontline for containing COVID-19 and a key part of response system for public health emergencies in China, during which rural physicians play an important role as the "gatekeeper" of rural residents' health and rural pandemic prevention and control. However, rural physicians have demonstrated some work-related problems during the COVID-19 pandemic containment, which have affected the implementation effectiveness of their duties and responsibilities. Objective: To investigate the duties and responsibilities of rural physicians during COVID-19 pandemic containment in rural areas, and to identify the problems, then put forward relevant suggestions. Methods: An on-site semi-structured interview using non-participant observation approach was carried out in Beijing's Huairou District from April to July, 2021. Eighteen rural physicians were selected to attend the interview as stakeholders. The interview was guided by an outline developed based on a literature review and an expert consultation, including three parts: (1)demographic characteristics(practice location, sex, age), (2)practicing qualifications(education level, starting time of practicing, professional qualifications), (3)involvement in COVID-19 pandemic prevention and control(awareness of the 10 instructions for COVID-19 pandemic containment in village clinics, participation in COVID-19 pandemic containment, and personal protective equipment materials for COVID-19). The interview was continued until data saturation. Results: Among the 18 rural physicians, 14(77.8%) were certified as rural physicians, 3(16.7%) were certified as rural assistant general practitioners, 2(11.1%) had a certificate of licensed physician and 1(5.6%) had a certificate of licensed assistant physician. Except for one(5.6%), the rural physicians〔17(94.4%)〕 indicated that they knew the 10 instructions for COVID-19 pandemic containment in the village clinic. The top three services about COVID-19 pandemic containment most frequently provided by the rural physicians were health education (94.4%), information reporting(72.2%) and diagnosis and treatment(64.7%), and the least provided was throat swab sampling〔only one case (5.6%)〕. In addition, three rural physicians participated in providing other services, which included screening suspected COVID-19 cases in the village, guiding COVID-19 pandemic containment in the village, and purchasing food for villagers. Ten physicians(55.6%)indicated that personal protective equipment materials for COVID-19 were adequate, but other 8(44.4%) expressed that such materials were inadequate during the first response phase. During the regular COVID-19 pandemic containment phase, 16 physicians(88.9%) indicated that personal protective equipment materials for COVID-19 were adequate, but other 2(11.1%) still indicated that such materials were inadequate. The top four personal protective equipment materials for COVID-19 owned by the physicians in regular COVID-19 pandemic containment phase were 84 Disinfectant(72.2%), ordinary disposable medical masks(66.7%), disposable gloves(66.7%) and medical surgical masks(61.1%), and the least owned were medical protective clothing(38.9%) and goggles(11.1%). Conclusion: Rural physicians play a necessary role in COVID-19 pandemic containment in rural areas, but the effectiveness of their services has been affected by limited personal capabilities in delivering COVID-19 pandemic containment services(including pharyngeal swab sampling), lack of a legal right to provide home-based isolation and monitoring services, and inadequate personal protective equipment materials. Therefore, it is recommended that relevant laws and regulations should be improved to provide a legal right for rural physicians to perform their duties and responsibilities in COVID-19 pandemic containment, recruit them to the public health team of the village committee, and ensure the provision of emergency materials for village physicians to help them to realize their potential in pandemic ontainment. Copyright © 2022 by the Chinese General Practice.

The EpiBench Platform to Propel AI/ML-Based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-Level Performance

During the COVID-19 pandemic, a significant effort has gone into developing ML-driven epidemic forecasting techniques. However, benchmarks do not exist to claim if a new AI/ML technique is better than the existing ones. The “covid-forecast-hub” is a collection of more than 30 teams, including us, that submit their forecasts weekly to the CDC. It is not possible to declare whether one method is better than the other using those forecasts because each team’s submission may correspond to different techniques over the period and involve human interventions as the teams are continuously changing/tuning their approach. Such forecasts may be considered “human-expert” forecasts and do not qualify as AI/ML approaches, although they can be used as an indicator of human expert performance. We are interested in supporting AI/ML research in epidemic forecasting which can lead to scalable forecasting without human intervention. Which modeling technique, learning strategy, and data pre-processing technique work well for epidemic forecasting is still an open problem. To help advance the state-of-the-art in AI/ML applied to epidemiology, a benchmark with a collection of performance points is needed and the current “state-of-the-art” techniques need to be identified. We propose EpiBench a platform consisting of community-driven benchmarks for AI/ML applied to epidemic forecasting to standardize the challenge with uniform evaluation protocol. In this paper, we introduce a prototype of EpiBench which is currently running and accepting submissions for the task of forecasting COVID-19 cases and deaths in the US states and We demonstrate that we can utilize the prototype to develop an ensemble relying on fully automated epidemic forecasts (no human intervention) that reaches human-expert level ensemble currently being used by the CDC. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

INDIVIDUALISM DURING CRISES

Individualism has long been linked to economic growth. Using the COVID-19 pandemic, we show that such a culture can hamper the econ-omy’s response to crises, a period with heightened coordination frictions. Exploiting variation in U.S. counties’ frontier experience, we show that more individualistic counties engage less in social distancing and charitable transfers and are less willing to receive COVID-19 vaccines. The effect of individualism is stronger where social distancing has higher externality and holds at the individual level when we exploit migrants for identification. Our results suggest that individualism can exacerbate collective action problems during economic downturns. © 2020 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Clinical Features and Outcomes of Patients with COVID-19 After Discharge: A Retrospective, Multi-center Study

BACKGROUND: The follow-up data of discharged patients with coronavirus disease 19 (COVID-19) have not yet been fully analyzed and reported. This study aimed to evaluate the clinical features, test results, and outcomes of COVID-19 patients after discharge. METHODS: 149 COVID-19 patients with follow-up data after discharge were included. Post-hospitalization data related to clinical features and outcomes were obtained by following the patients up to 6 weeks. RESULTS: The COVID-19 patients were followed for a median of 28.0 days (range of 22 days to 42 days) after discharge from hospital. At the end of follow-up, four patients (2.7%) still had cough. The proportions of leukopenia and lymphopenia were 7.4% and 4.7%, respectively. The proportions of ALT, AST, and Cr abnormalities were 26.2%, 6.0%, and 0%, respectively. Abnormal chest CT was detected in 94 (63.1%) patients, including 14 (9.4%) unilateral pneumonia and 80 (53.7%) bilateral pneumonia. However, the proportion of chest CT abnormality significantly decreased compared to that at the time of admission. CONCLUSIONS: One month after discharge, few patients with COVID-19 had clinical symptoms;however, a substantial proportion of COVID-19 patients harbored abnormal laboratory and radiological examinations. Moderately long-term medical follow-up would justifiably benefit COVID-19 patients after discharge.

A Dialogue-based Information Extraction System for Medical Insurance Assessment

In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant. This is a highly professional job that involves many parts, such as identifying personal information, collecting related evidence, and making a final insurance report. Due to the coronavirus (COVID-19) pandemic, the previous offline insurance assessment has to be conducted online. However, for the junior assessor often lacking practical experience, it is not easy to quickly handle such a complex online procedure, yet this is important as the insurance company needs to decide how much compensation the claimant should receive based on the assessor's feedback. In order to promote assessors' work efficiency and speed up the overall procedure, in this paper, we propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. With the assistance of our system, the average time cost of the procedure is reduced from 55 minutes to 35 minutes, and the total human resources cost is saved 30% compared with the previous offline procedure. Until now, the system has already served thousands of online claim cases. © 2021 Association for Computational Linguistics

How Did COVID-19 Reshape Food Procurement Around the Globe? Effective Operation and Redesign of the Food Retail Industry in China, Portugal, Turkey, and the USA

During the COVID-19 pandemic, consumers’ food procurement activities underwent considerable transformations due to restrictive policies on social distancing. These behavioral changes induced a new industrial landscape within the food retail sector, with franchise stores gaining more consumers and more intensive competition occurring across online food retail platforms. This study employs cross-sectional survey data from China, Portugal, Turkey, and the USA to examine the major changes in consumer food procurement behaviors during the pandemic worldwide, as compared to behaviors before the pandemic. Based on the findings, we provide operable implications necessary for food retailers to sustain their businesses in a post-pandemic context. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ProMVR - Protein multiplayer virtual reality tool

Due to the pandemic limitations caused by Covid-19, people need to work at home and carry on the meetings virtually. Virtual meeting tools start popularizing and thriving. Those tools allow users to see each other through screen and camera, chat through voice and text, and share content or ideas through screen share. However, screen sharing protein models through virtual meetings is not easy due to the difficulty of viewing protein 3D (Three Dimensional) structures from a 2D (Two Dimensional) screen. Moreover, interactions upon a protein are also limited. ProMVR is a tool the author developed to tackle the issue that protein designers may find limitations working in a traditional 2D or 3D environment and they may find it hard to communicate their ideas with other designers. Since ProMVR is a VR tool, it allows users to “jump into” a virtual environment, take a close look at protein models, and have intuitive interactions. © 2021 Copyright held by the owner/author(s).

Global food security assessment during 1961–2019

Quantified components of the global food system are used to assess long-term global food security under a series of socio-economic, epidemic normalization and climate change scenarios. Here, we evaluate the global food security including the global farming system as well as the global food trade, reserve and loss systems from 1961 to 2019, and analyze their temporal and spatial characteristics by using the global food vulnerability (GFV) model. The spatio–temporal patterns of the vulnerability of the global food system were consistent with the GFSI. As food production and consumption vary greatly in different countries which have continued for a long time, food exports from many developed agricultural countries have compensated for food shortages in most countries (about 120 net grain-importing countries). As a result, many countries have relied heavily on food imports to maintain their domestic food supplies, ultimately causing the global food trade stability to have an increasing impact on the food security of most countries. The impact of global food trade on global food security increased from 9% to 17% during 1961–2019, which has increased the vulnerability of the global food system. The food damage in the United States, Russia, China, and India has varied significantly, and global cereal stocks have fluctuated even more since 2000. From 1961 to 2019, the food system security of some Nordic countries significantly improved, while the food system security of most African countries significantly deteriorated. Most countries with high food insecurity are located in Africa and South Asia. In order to cope with extreme events, these countries need to strengthen and improve their own food production and storage systems, which will help the World Food and Agriculture Organization to formulate relevant food policies and maintain sustainable development. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.