The Impact of the COVID-19 on International Higher Education: The Emerge of New Forms of Internationalization

With the rapid development of the massification of higher education, the growth of international mobility plays an important role in shaping the internationalization of higher education and increasing international student numbers. However, the impact of COVID-19 has significantly influenced international mobility, including student, academic staff, and program mobility, and in response the model is being shifted from physical to online education. Furthermore, the unavoidable changes provide an alternative for international students, which allows them to cope with the mobility crisis. In other words, virtual mobility or internationalization at home creates new forms of internationalization in higher education. Numerous articles discuss student mobility before the COVID-19 pandemic, and several studies reveal the motivation behind student mobility under the pandemic. However, fewer scholars pay attention to the effect of virtual mobility. Therefore, based on a literature review and second-hand survey data, this chapter analyses the impact of COVID-19 on the internationalization of higher education in the past two years, new forms of international mobility, and the perspective from both students and staff. In addition, this chapter further provides recommendations for the future development of the internationalization of higher education, especially with the new normal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

How do hotel star ratings affect the relationship between environmental CSR and green word-of-mouth?

Hotels have increasingly engaged in environmentally responsible initiatives to demonstrate their commitment to environmental concerns and sustainable hospitality and tourism. These initiatives are expected to become even more popular in the context that the COVID-19 crisis has driven people to further acknowledge the importance of the ecosystem. This study aims to examine how hotels' environmental corporate social responsibility (CSR) affects customers' green word-of-mouth (WOM). Structural equation modeling was employed to analyze data from an online survey of 749 Chinese respondents. The findings reveal that hotels' environmental CSR indirectly enhances customers' green WOM intention via green perceived value (i.e., cognitive route) and green hotel pride (i.e., emotional route). Furthermore, the indirect effects of hotels' environmental CSR on customers' green WOM are more substantial for hotels with higher star ratings. These findings offer valuable insights for hoteliers to develop genuine environmentally responsible initiatives that can generate positive customer responses.

Detection of Pneumonia Based on ResNet Improved by Attention Mechanism

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics. © 2023 IEEE.

Hyperkalemia caused by Qingfei Paidu decoction() in a patient with novel coronavirus pneumonia.

A 23-year-old male patient received moxifloxacin, recombinant human interferon alpha-2b for injection, and lopinavir and ritonavir for 7 days for novel coronavirus pneumonia. There was no abnor-malityof serum potassium. Moxifloxacin was stopped, Qingfei Paidu decoction() was given, and then the patient's serum potassium began to rise. On day 10 after taking the decoction, laboratory tests showed serum potassium 5.7 mmol/L and the patient was diagnosed with hyperkalemia. Insulin injection 4 U diluted to 5% glucose injection 250 ml was given once by IV infusion, and then the serum potassium decreased to 5.0 mmol/L 6 hours later and 4.6 mmol/L 2 days later. After 5 days, the serum potassium rose again and finally to 5.4 mmol/L on day 17 after taking the decoction. Insulin was given once that day and 2 days later once daily according to the previous method. Then the serum potassium decreased and did not rise again. The patient recovered from novel coronavirus pneumonia and was discharged on day 28 after hospitalization.Copyright © 2020 by the Chinese Medical Association.

Intrinsic and Extrinsic Motivation in Distance Education: A Self-Determination Perspective

Motivation activates and sustains one's behaviors toward a goal, and it is one of the most important factors affecting students' learning outcomes, especially for online learning. With the widespread of COVID-19, distance education has become more prevalent in today's universities and colleges. Understanding college students' motivation in taking online classes becomes more urgent for instructors to prepare, organize, and deliver their teaching materials effectively. This research is built upon self-determination theory to explore how specific online learning activities affect students' intrinsic and extrinsic motivation and online learning performance. The results suggest that online learning activities may not promote students' motivation and learning performance if those activities are not well-designed and developed. The learning activities need to be tailored to the specific coursework, and continuous improvements and revisions are necessary to make learning activities more effective in the course delivery. This study also provides evidence that extrinsic motivation could play a more crucial role than intrinsic motivation, and older students could be more self-motivated in online learning environments. Besides, amotivation may be considered a potential measure for the healthiness of online classes. © 2023 Taylor & Francis Group, LLC.

Completely Contactless and Online Finger Knuckle Identification for Real World Applications

Completely contactless and at-a-distance personal identification provides enhanced user convenience, and improved hygiene and is highly sought under the COVID-19 pandemic. This paper proposes an accurate and generalizable deep neural network-based framework for the ‘completely’contactless finger knuckle identification. We design and introduce a new loss function to enable a fully convolutional network to more effectively learn knuckle features that are imaged under at-a-distance imaging. A ‘completely’contactless system also requires efficient online finger knuckle detection capabilities. This paper, for the first time in our knowledge, develops and introduces accurate capabilities to efficiently detect and segment finger knuckle patterns from images with complex backgrounds as widely observed in real-world applications. We introduce angular loss to accurately predict oriented knuckle patterns and incorporate into our framework. Experimental results presented in this paper on five different public databases, using challenging protocols and cross-database performance evaluation, illustrate outperforming results and validate the effectiveness of the proposed framework for completely contactless applications. IEEE

VGG-FusionNet: A Feature Fusion Framework from CT scan and Chest X-ray Images based Deep Learning for COVID-19 Detection

COVID-19 has been rampant across the globe since it was discovered in 2020, but the method of virus detection still lacks efficiency and requires human resources. Given the slow delivery of the PCR test and the many possible false negatives of the rapid tests, medical imaging such as a chest computed tomography (CT) scan or chest X-ray (CXR) is an alternative and efficient way to detect the coronavirus accurately. For the past two years, many researchers have proposed different deep learning methods for COVID-19 detection using CT scans or CXR images. Due to the lack of available data, our study aims to propose a new deep learning framework VGG-FusionNet that takes advantage of integrating features from both CT scan and CXR images while avoiding some pitfalls from previous studies, including a high risk of bias due to lack of demographic information for the dataset, poor reproducibility, and no evaluation on different data sources to study the generalizability. Specifically, we use the convolutional layers of GoogLeNet, ResNet, and VGG to extract features from CT scan and CXR images and fuse them before training through fully connected layers. The result shows that using VGG's convolutional layers achieves the best overall performance with an accuracy of 0.93. Our proposed framework outperforms the deep learning models, using features from CT scans or CXR. © 2022 IEEE.

Simulation analysis of passengers' rescheduling strategies in metro station under COVID-19

The spread of COVID-19 has a great impact on public transport which is closely related to social life. As an essential carrier of the cities, metro has become an important object of concern during the epidemic. Due to the high infection risk of COVID-19 in public space, it is necessary to quantitatively evaluate and perform corresponding epidemic control measures on reducing public health risks in metro station. In this paper, three strategies of passenger rescheduling, i.e. controlling the flows of inbound and outbound passengers in the station, setting route guidance in the crucial areas and shortening the interval time of train, are simulated and analyzed based on Anylogic. The performances of different strategies are characterized and evaluated by the important parameters, which include local passengers' density, inbound and outbound time. Finally, the optimization experiments based on an objective function are carried out to obtain the best strategy combination considering passengers' health safety and travel efficiency. The crucial areas with high density are obtained from the simulation results of the initial model. The three independent strategies are helpful in reducing the maximum passengers' density and average travel time. The optimization results of strategy combination and the specific parameters of each strategy are obtained by the final simulation experiment. The research findings are important reference to enhance the present health risk management level and provide specific measures of passenger organization in metro station under COVID-19. © 2023 Elsevier Ltd

Are commodity futures a hedge against inflation? A Markov-switching approach

This study examines the inflation hedging ability of various commodity futures using Markov-switching vector error correction models (MS-VECM). We find that total commodity futures fail to provide a hedge against inflation over the sample period between January 1983 and December 2021. However, industrial metals and precious metals are able to hedge against inflation. Other sub-indexes, including energy, agriculture, and livestock, do not have a significant inflation hedging ability. The inflation hedging capacity of industrial metals exhibits substantial variation over time, with most of the inflation hedging power occurring during the relatively longer and more common regimes covering the Great Moderation, the post-subprime crisis, and the periods after the outbreak of the COVID-19 pandemic. We further evaluate the inflation hedge ability of commodity futures by including stocks and bonds in the model. Our results suggest that industrial metals are more reliable inflation hedges. © 2023 Elsevier Inc.

Performance of Rapid Diagnostic Testing at Days 4-6 from Diagnosis: Implications for Discharge from Isolation on a University Campus

Background. Omicron rapidly replaced delta as the predominant strain causing COVID-19 related illness in the United States (US) in December 2021, the same month the US CDC reduced the recommended isolation period from 10 to 5 days for asymptomatic individuals or those with resolving symptoms. New evidence suggests some asymptomatic individuals with omicron remain culture positive beyond 5 days from diagnosis. We sought to evaluate the performance of a SARS-CoV-2 antigen rapid diagnostic test (RDT) in predicting persistent potential for transmission at the end of a five-day isolation period among young, fully vaccinated individuals in a university community setting. Methods. A subgroup of participants enrolled in a longitudinal COVID-19 cohort were asked to self-perform RDTs on days 4 to 6 from diagnostic test date in addition to a separate self-collected anterior nasal swab used for culture and RT-PCR, and a daily symptom screen (15 COVID-19 symptom questions on a 4-point scale). We calculated the daily and overall sensitivity and specificity of the RDTs in comparison to SARS-CoV-2 culture result. We also compared the N1 cycle threshold (CT) values and symptom score on each day of the study by RDT results. Results. Of 23 participants, the mean age was 20 years, all had completed their primary COVID-19 vaccine series, and 13 (65.0%) had received a booster vaccine (Table 1). Compared to culture, sensitivity and specificity of the RDTs were 100% and 62% respectively (Table 2). Compared to participants with negative RDTs, median CT values were lower in those with positive RDTs on each day of the study (Figure 1). Participants who had positive RDTs on all three days had higher symptom scores (Figure 2) than those without. Conclusion. RDTs have a high sensitivity in detecting culture positive SARS-CoV-2 on Days 4 to 6 from initial diagnostic test. However, the high false positive rate of 38% means that over a third of culture negative individuals will stay in isolation longer than necessary if RDTs are used in test to release from isolation protocols. Viral loads (CT values) and symptom scores were higher for participants with persistently positive RDT result. An approach that uses a combination of RDTs, CT values and symptom score may prove useful in guiding isolation duration.

Time from last COVID-19 vaccination's impact on rapidity of viral culture conversion following SARS-CoV-2 infection: a prospective cohort study

Background. Vaccination is a fundamental element of pandemic control;however, insufficient data exists on vaccine's impact on SARS-CoV-2 viral dynamics. We aimed to evaluate the relationship between time to negative viral culture conversion after diagnosis and time since most recent COVID-19 vaccination. Scatterplot illustrating relationship between time since completion of initial COVID-19 vaccine series and time to culture conversion among un-boosted participants. The vaccine type received has also been designated by plot labels. The black solid line shows the best fit for the Spearman correlation model;gray shading denotes 95% confidence interval around this estimate. Spearman correlation coefficient, R, and p-value were estimates for the model. Methods. CoViD Post-vax is longitudinal cohort study collecting baseline clinical questionnaires, and daily anterior nasal swabs and symptom screens on enrolled Boston University SARS-CoV-2 PCR-positive cases detected through a serial screening testing program or symptomatic testing. Data was collected from November 2021 to March 2022. Participants were excluded from analysis if they lacked at least one positive culture or did not culture convert during their study involvement. Scatter plots comparing time to culture conversion to time from initial vaccine series were created.We calculated spearman correlation coefficients to determine the relationship between time to culture conversion and time from last vaccination for all participants, those who completed the initial vaccine series (unboosted), and those who were boosted. Scatterplot illustrating relationship between time since receiving a booster COVID-19 vaccine dose and time to culture conversion among boosted participants. The vaccine type received has also been designated by plot labels. The black solid line shows the best fit for the Spearman correlation model;Gray shading denotes 95% confidence interval around this estimate. Spearman correlation coefficient, R, and p-value were estimates for the model. Results. Of 54 CoViD Post-Vax participants included in this analysis, the mean age was 21 years (SD=2) and culture conversion occurred after a median of 4 days (IQR=3-5.75). There was no association between time to culture conversion and time since last dose of a COVID-19 vaccination (R= -0.13, p= .34). When stratified by vaccination status, there was no association between time to culture conversion and time since initial COVID-19 vaccine series (R= -0.25, p= .21, n=26) or time since COVID-19 booster vaccination (R= -0.24, p= .22, n=28). Conclusion. Our results show no significant relationship between time to culture conversion and time since most recent dose of COVID-19 vaccination in an initially culture positive, young, University-based cohort. More work needs to be done to understand the impact of symptom severity, disease burden, SARS-CoV-2 variants, and COVID-19 vaccine status on duration of transmissible SARS-CoV-2 infection. (Figure Presented).

BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging than before. This paper explored machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. Also, we investigated a way to conduct such BCI experiments remotely via Zoom. The results showed that Random Forest and RBF SVM performed well for EEG classification tasks. The remote experiment during the pandemic yielded several challenges, and we discussed the possible solutions;nevertheless, we developed a protocol that grants non-experts who are interested a guideline for such data collection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Stock Price Prediction of E-commerce Platforms under COVID-19's Influence Based on Machine Learning

After the COVID-19, the global economy was hit greatly. Due to various reasons, the real economy has been seriously damaged, and whether the e-commerce platform will benefit from it is under debate. This paper will study this issue and give an argument. The stock price is a simple and intuitive reflection of the value of the company, which often reflects the company's situation. As the world's largest economies, China and the United States have mature e-commerce conditions and huge E-commerce markets, which are representative and more applicable to macro laws. Therefore, to study the impact of the epidemic on the e-commerce industry, this paper selects the five most representative e-commerce enterprises in China and the United States, collects their stock price information in recent five years, uses machine learning (LSTM neural network and GRU neural network) to predict their stock price trend, evaluates the results and gives a conclusion. According to the results, it is found that although the share prices of three Chinese companies may fall in the short term, the positive effect of the epidemic on ecommerce platforms is greater than its negative effect. © 2022 IEEE.

Finding meaning from the present and future: The mediating role of meaning in life between temporal focus and mental health

Literature has shown that temporal focus (TF) is closely related to mental health, and both of them are associated with meaning in life (MIL). Nevertheless, few studies have investigated the functional role of MIL in the relationship between TF and mental health, especially in the context of COVID-19. The present study aims to explore the mediating role of MIL between different categories of TF (i.e., past, present, and future TF) and mental health. A two-wave longitudinal survey was conducted among 538 Chinese participants aged between 22 and 70 (M ± SD = 35.07 ± 8.82). TF was assessed at Time 1, while MIL, anxiety, and life satisfaction were measured at Time 2. The results of SEM showed that past TF negatively predicted MIL, while present and future TF positively predicted MIL. In addition, MIL mediated the relationship between TF and mental health indicators (anxiety and life satisfaction). Specifically, past TF demonstrated a negative indirect effect on life satisfaction by reducing MIL, while present and future TF had a positive indirect effect on life satisfaction by strengthening MIL. In contrast, past TF demonstrated a positive indirect effect on anxiety by reducing MIL, while present and future TF had a negative indirect effect on anxiety by strengthening MIL. Therefore, the present results supported the positive roles of present and future TF in promoting MIL and mental health during the COVID-19 pandemic. For the MIL intervention during the pandemic, the present findings suggested that compared to recalling the past, more attention should be paid to living in the present and hoping for the future. © The Author(s) 2022.

Navigating Robots in Dynamic Environment With Deep Reinforcement Learning

In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots. IEEE

BeSharing: A Copyright-aware Blockchain-enabled Knowledge Sharing Platform

In response to the coronavirus pandemic, COVID-19, the use of online media and teaching tools has soared, leading to rampant cheating and plagiarism. To provide a better online environment, A copyright-aware Blockchain-enabled Knowledge Sharing platform named as BeSharing, enables students to share part of their assignments or ideas without the worry of being plagiarized. Thanks to blockchain technology which ensures the safety of encrypted shared files and the immutability of the shared records, the intellectual property rights of students can be protected whenever plagiarism issues occur. At present, we have released the platform with the realization of relevant functions and finished the alpha test. We believe this platform has great potential to foster collaboration among students while protecting their ideas. © 2022 IEEE.

Establishment of sample panel for detection of SARS-CoV-2 antigen and its application in development and quality evaluation of colllidal gold test cassettes

Objective To establish a sample panel for detection of severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) antigen and apply to the development and quality evaluation of SARS-CoV-2 antigen colloidal gold test cassettes. Methods A sample panel for detection of SARS-CoV-2 antigen was established using 12 kinds of bulks of inactivated non-SARS-CoV-2 vaccine as negative controls, while two batches (Bl and B2) of bulks of inactivated SARS-CoV-2 vaccine (Bl, B2) and one batch (SI) of inactivated SARS-CoV-2 culture as positive controls. Bl was used as a positive control to evaluate the colloidal gold test cassettes from four manufacturers (A, B, C and D), and to monitor the development process of cassette from manufacturer A to improve its sensitivity. The negative sample panel was used to evaluate the specificity of colloidal gold test cassettes from five manufacturers (A, C, E, F and G), while positive sample panel (B2, SI and recombinant N protein) to evaluate the sensitivity. Inactivated SARS-CoV-2 culture SI was deter-mined with the commercial SARS-CoV-2 nucleic acid detection kit, and the result was compared with that by the colloidal gold test cassette from manufacturer A. Results N protein was determined as the main epitope of SARS-CoV-2 antigen by evaluation with positive control. The colloidal gold test cassettes from manufacturer A showed a sensitivity of 1 : 2 x 103to B1. The colloidal gold test cassettes from five manufacturers showed no cross reactions with inactivated non-SARS-CoV-2 vaccines, indicating a high specificity. The sensitivity of colloidal gold test cassette from manufacturer A was 106to B2 and 1 : 2 x 107to S1. However, the sensitivities of colloidal gold test cassettes from manufacturers E, F and were more than 1 : 103to B2 and 1 : 104- 1 : 105to SI, and that from manufacturer C was 1 : 104to B2 and 1 : 106to SI. The sensitivity of colloidal gold test cassette from manufacturer A was 100 pg/mL, while those from the other four manufacturers were 10 pg/mL, to recombinant N protein. The sensitivity of commercial nucleic acid detection kit to SI was 1 : 107, which was equal to that of colloidal gold test cassette from manufacturer A (1 : 2 x 107). Conclusion A sample panel for detection of SARS-CoV-2 antigen was successfully established, which showed high specificity and sensitivity, and might be used for the development and quality evaluation of SARS-CoV-2 antigen colloidal gold test cassettes. Copyright © 2022 Changchun Institute of Biological Products. All rights reserved.

Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Objective: Shiwei Qingwen decoction (SWQWD) is effective in preventing COVID-19. This study examined the active components of SWQWD and its potential targets for preventing COVID-19. The study used network pharmacology and molecular docking technology to verify the role of SWQWD targets through animal experiments and explored the mechanisms that enhance immunity to alleviate mild COVID-19 symptoms. Methods: First, SWQWD- and COVID-19-related targets were retrieved from TCMSP, GeneCards, and OMIM databases. Second, protein-protein interaction networks were established using the String database. The drug active ingredient target network was constructed in Cytoscape to identify the core target proteins. Third, Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the SWQWD mechanism of action. Finally, the targets were validated by molecular docking in an acute lung injury (ALI) rat model. Results: The SWQWD compound target network contained 79 compounds and 277 targets, coinciding with the 73 targets of COVID-19. The most important gene in the core subnetwork was a tumor necrosis factor (TNF). The 3 most potent compounds, quercetin, kaempferol, and luteolin, can enter the active pockets of TNF and have potential therapeutic roles in COVID-19. Conclusion: Quercetin, kaempferol, and luteolin in SWQWD may enhance immunity by regulating multiple TNF signal pathways. After administering SWQWD, the content of tumor necrosis factor-alpha was significantly reduced in the bronchoalveolar lavage fluid (BALF) of ALI rats in comparison to the model group. We believe SWQWD is able to prevent and control COVID-19 through the target of TNF.

Epidemiology and evolution of novel deltacoronaviruses in birds in central China

BIRDS : The variety and widespread of coronavirus in natural reservoir animals is likely to cause epidemics via interspecific transmission, which has attracted much attention due to frequent coronavirus epidemics in recent decades. Birds are natural reservoir of various viruses, but the existence of coronaviruses in wild birds in central China has been barely studied. Some bird coronaviruses belong to the genus of Deltacoronavirus. To explore the diversity of bird deltacoronaviruses in central China, we tested faecal samples from 415 wild birds in Hunan Province, China. By RT-PCR detection, we identified eight samples positive for deltacoronaviruses which were all from common magpies, and in four of them, we successfully amplified complete deltacoronavirus genomes distinct from currently known deltacoronavirus, indicating four novel deltacoronavirus stains (HNU1-1, HNU1-2, HNU2 and HNU3). Comparative analysis on the four genomic sequences showed that these novel magpie deltacoronaviruses shared three different S genes among which the S genes of HNU1-1 and HNU1-2 showed 93.8% amino acid (aa) identity to that of thrush coronavirus HKU12, HNU2 S showed 71.9% aa identity to that of White-eye coronavirus HKU16, and HNU3 S showed 72.4% aa identity to that of sparrow coronavirus HKU17. Recombination analysis showed that frequent recombination events of the S genes occurred among these deltacoronavirus strains. Two novel putative cleavage sites separating the non-structural proteins in the HNU coronaviruses were found. Bayesian phylogeographic analysis showed that the south coast of China might be a potential origin of bird deltacoronaviruses existing in inland China. In summary, these results suggest that common magpie in China carries diverse deltacoronaviruses with novel genomic features, indicating an important source of environmental coronaviruses closed to human communities, which may provide key information for prevention and control of future coronavirus epidemics

Analysis of Chinese tilapia supply and demand without and with the COVID-19 epidemic impact

We analyzed the supply and demand for tilapia in China while assessing the future developmental trends. China has become the world’s largest producer, exporter, and consumer of tilapia. China entered a period of rapid aquaculture development in the 1990s, and the tilapia supply has increased yearly. Tilapia products are mainly supplied to the international market, especially in the US. The global market for the Chinese tilapia has grown dramatically, but a downward trend occurred in 2019–2020. The Chinese domestic market demand is relatively stable, and even the COVID-19 epidemic did not significantly impact the supply and demand of tilapia. Internationally, it is expected that the demand for tilapia will decline considerably in the near future. However, this decline could be alleviated after the impact of the COVID-19 epidemic passes, and increasing demand will resume. The increased supply of Chinese tilapia might slow down or even decrease due to market uncertainty, the increasing constraints on natural resources, and the Chinese government's requirements for high-quality aquaculture environments. © 2022, Israeli Journal of Aquaculture - Bamidgeh. All rights reserved.