Innovative digital technology adapted in nursing education between Eastern and Western countries: a mini-review.

Advanced digital technologies have overcome the limitation of on-site teaching, especially after the COVID-19 epidemic. Various newly-developed digital technologies, such as e-learning, virtual reality, serious games, and podcasts, have gained renewed interest and come into the spotlight. Podcasts are becoming increasingly popular in nursing education as they provide a convenient and cost-effective way for students to access educational content. This mini-review article provides an overview of the development of podcasts in nursing education in Eastern and Western countries. It explores potential future trends in the use of this technology. The literature review demonstrates that nursing education in Western countries has already integrated podcasts into curriculum design, using the podcast to convey nursing education knowledge and skills and to improve students' learning outcomes. However, few articles address nursing education in Eastern countries. The benefits of integrating podcasts into nursing education appear far greater than the limitations. In the future, the application of podcasts can serve not only as a supplement to instructional methodologies but also as a tool for clinical practicing students in nursing education. In addition, with the aging population increasing in both Eastern and Western countries, podcasts have the potential to serve as an effective delivery modality for health education in the future, particularly for the older adult, whose eyesight declines with age, and those populations with visual impairments.

Quantifying the rebound of influenza epidemics after the adjustment of zero-COVID policy in China.

The coexistence of coronavirus disease 2019 (COVID-19) and seasonal influenza epidemics has become a potential threat to human health, particularly in China in the oncoming season. However, with the relaxation of nonpharmaceutical interventions (NPIs) during the COVID-19 pandemic, the rebound extent of the influenza activities is still poorly understood. In this study, we constructed a susceptible-vaccinated-infectious-recovered-susceptible (SVIRS) model to simulate influenza transmission and calibrated it using influenza surveillance data from 2018 to 2022. We projected the influenza transmission over the next 3 years using the SVIRS model. We observed that, in epidemiological year 2021-2022, the reproduction numbers of influenza in southern and northern China were reduced by 64.0 and 34.5%, respectively, compared with those before the pandemic. The percentage of people susceptible to influenza virus increased by 138.6 and 57.3% in southern and northern China by October 1, 2022, respectively. After relaxing NPIs, the potential accumulation of susceptibility to influenza infection may lead to a large-scale influenza outbreak in the year 2022-2023, the scale of which may be affected by the intensity of the NPIs. And later relaxation of NPIs in the year 2023 would not lead to much larger rebound of influenza activities in the year 2023-2024. To control the influenza epidemic to the prepandemic level after relaxing NPIs, the influenza vaccination rates in southern and northern China should increase to 53.8 and 33.8%, respectively. Vaccination for influenza should be advocated to reduce the potential reemergence of the influenza epidemic in the next few years.

Universal health coverage mitigated COVID-19 health-related consequences in Asia Oceania.

The COVID-19 pandemic has been a continual challenge since 2020, and it continues to impact people and industries as a disaster caused by a biological hazard. This study examined universal health coverage (UHC) scores in relation to the performance in combating COVID-19 in the Southeast Asian region (SEAR) and the Western Pacific region (WPR), along with the State Party Self-Assessment Annual Reporting (SPAR) index under the international health regulations (IHC). The numbers of infections and deaths per million population from December 2019 to June 2022 were used as primary outcomes to measure countries' performance. Countries with UHC scores of 63 or higher had a significantly lower number of infected patients and deaths. In addition, several inter-capacity correlations within the SPAR capacities, including with C8 (the National Health Emergency Framework), as well as a very strong correlation to C4 (Food Safety), C5 (Laboratory), and C7 (Human Resources). Furthermore, C9 (Health Service Provisions) has a very strong correlation to C1 (Legislation and Financing), C2 (International Health Regulation Coordination and a National IHR Focal Point function), and C4 (Food Safety), suggesting that the capability to manage an emerging infectious disease form blocks of capacities. In conclusion, UHC clearly mitigated the health-related consequences of COVID-19 in South-East Asia Region (SEAR) and Western Pacific Region (WPR). Investigating the correlation between the SPAR capacities and UHC is a promising approach for future research, including the importance of the provision of health services, points of entry, and, most importantly, risk communications as critical factors for managing pandemic. This study constitutes a good opportunity to apply the SPAR index to define which capacities correlate with the outcome of the pandemic in terms of infections and deaths.

The development of new remote technologies in disaster medicine education: A scoping review.

Background: Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education. Objectives: The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years. Literature scoping reviews: Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education. Methods: We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022. Results: A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems. Discussions: Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

SARS-CoV-2 Surveillance Through China Influenza Surveillance Information System - China, December 1, 2022 to February 12, 2023.

Introduction: The World Health Organization (WHO) proposed using influenza surveillance systems to carry out coronavirus disease 2019 (COVID-19) surveillance due to the similarity between the two diseases in some respiratory symptoms. To assess the prevalence of COVID-19, we analyzed the influenza-like illness (ILI) and positive rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detections in ILI patients reported to the influenza Surveillance Information System (CNISIS) since late 2022. Methods: Data related to ILI were reported by national surveillance sentinel hospitals. Positive testing for SARS-CoV-2 and influenza viruses was conducted using real-time reverse transcription polymerase chain reaction (rRT-PCR) detection by the national influenza surveillance network laboratories. Surveillance data were reported to CNISIS. Results: Beginning on December 12, 2022 (Week 50), the ILI percentage increased dramatically, peaking in Week 51 at 12.1%. Subsequently, the ILI percentage began to decline rapidly from Week 52, 2022, and by Week 6, 2023 (February 6-12), the ILI and ILI percentage had returned to the levels observed at the beginning of December 2022. From December 1, 2022 to February 12, 2023, 115,844 specimens were tested for both SARS-CoV-2 and influenza virus. Of these, 30,381 (26.2%) were positive for SARS-CoV-2 and 1,763 (1.5%) were positive for influenza virus. The positive rate of SARS-CoV-2 tests peaked at 74.1% around December 23 and 25. Conclusions: Sentinel-based surveillance, previously established for influenza, is an effective way to track the circulation trend of SARS-CoV-2 during community-level epidemics. There was no co-prevalence of SARS-CoV-2 and influenza virus during the outbreak of SARS-CoV-2, even during the winter influenza season. However, it is important to remain vigilant for the potential rise of influenza activities following the COVID-19 epidemic.

Genomic Surveillance for SARS-CoV-2 - China, September 26, 2022 to January 29, 2023.

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated 2,431 variants over the course of its global transmission over the past 3 years. To better evaluate the genomic variation of SARS-CoV-2 before and after the optimization of coronavirus disease 2019 (COVID-19) prevention and control strategies, we analyzed the genetic evolution branch composition and genomic variation of SARS-CoV-2 in both domestic and imported cases in China (the data from Hong Kong and Macau Special Administrative Regions and Taiwan, China were not included) from September 26, 2022 to January 29, 2023. Methods: Analysis of the number of genome sequences, sampling time, dynamic changes of evolutionary branches, origin, and clinical typing of SARS-CoV-2 variants submitted by 31 provincial-level administrative divisions (PLADs) and Xinjiang Production and Construction Corps (XPCC) was conducted to assess the accuracy and timeliness of SARS-CoV-2 variant surveillance. Results: From September 26, 2022 to January 29, 2023, 20,013 valid genome sequences of domestic cases were reported in China, with 72 evolutionary branches. Additionally, 1,978 valid genome sequences of imported cases were reported, with 169 evolutionary branches. The prevalence of the Omicron variants of SARS-CoV-2 in both domestic and imported cases was consistent with that of international epidemic variants. Conclusions: This study provides an overview of the prevalence of Omicron variants of SARS-CoV-2 in China. After optimizing COVID-19 prevention and control strategies, no novel Omicron variants of SARS-CoV-2 with altered biological characteristics or public health significance have been identified since December 1, 2022.

Variation in synonymous evolutionary rates in the SARS-CoV-2 genome.

Introduction: Coronavirus disease 2019 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Influential variants and mutants of this virus continue to emerge, and more effective virus-related information is urgently required for identifying and predicting new mutants. According to earlier reports, synonymous substitutions were considered phenotypically silent; thus, such mutations were frequently ignored in studies of viral mutations because they did not directly cause amino acid changes. However, recent studies have shown that synonymous substitutions are not completely silent, and their patterns and potential functional correlations should thus be delineated for better control of the pandemic. Methods: In this study, we estimated the synonymous evolutionary rate (SER) across the SARS-CoV-2 genome and used it to infer the relationship between the viral RNA and host protein. We also assessed the patterns of characteristic mutations found in different viral lineages. Results: We found that the SER varies across the genome and that the variation is primarily influenced by codon-related factors. Moreover, the conserved motifs identified based on the SER were found to be related to host RNA transport and regulation. Importantly, the majority of the existing fixed-characteristic mutations for five important virus lineages (Alpha, Beta, Gamma, Delta, and Omicron) were significantly enriched in partially constrained regions. Discussion: Taken together, our results provide unique information on the evolutionary and functional dynamics of SARS-CoV-2 based on synonymous mutations and offer potentially useful information for better control of the SARS-CoV-2 pandemic.

Common and rare variant associations with clonal haematopoiesis phenotypes.

Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes1-5. Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75, which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes6, our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications.

Factors affecting worriedness: A study of the COVID-19 pandemic in Japan.

The global effect of COVID-19 is no longer simply a public health issue; it is causing an economic crisis that has a significant impact on the job market and people's lives. The disease has led to 43% of businesses temporarily closing, and almost all these closures are due to COVID-19. Organizations that have temporarily suspended their activities have pointed mainly to a decline in demand and employee health issues as the reasons for closure. In emergency and disaster management, perception often helps shape personality and how people act in certain situations. This study aims to examine personal risk perception of COVID-19 from many viewpoints and whether it affects motivation with regard to improving personal preparedness. We collected data from three major Japanese cities through a questionnaire survey and analyzed the results of the survey through factor analysis and multiple regression analysis by using the Partial Least Square Structural Equation Modeling (PLS-SEM). The three study areas include (1) the most damaged regions from the 2011 Great East Japan earthquake and tsunami, (2) the capital city and surrounding areas of Tokyo, and (3) Kumamoto, which has recently experienced an earthquake. The findings show a correlation between the nature of the information received during COVID-19 and worriedness and the necessity for adequate information. The expected benefit of this study is to provide guidelines for the government or organizations to make a suitable emergency management plan based on pertinent factors for future pandemics.

A network view of human immune system and virus-human interaction.

The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.

Factors affecting worriedness: A study of the COVID-19 pandemic in Japan

The global effect of COVID-19 is no longer simply a public health issue;it is causing an economic crisis that has a significant impact on the job market and people's lives. The disease has led to 43% of businesses temporarily closing, and almost all these closures are due to COVID-19. Organizations that have temporarily suspended their activities have pointed mainly to a decline in demand and employee health issues as the reasons for closure. In emergency and disaster management, perception often helps shape personality and how people act in certain situations. This study aims to examine personal risk perception of COVID-19 from many viewpoints and whether it affects motivation with regard to improving personal preparedness. We collected data from three major Japanese cities through a questionnaire survey and analyzed the results of the survey through factor analysis and multiple regression analysis by using the Partial Least Square Structural Equation Modeling (PLS-SEM). The three study areas include (1) the most damaged regions from the 2011 Great East Japan earthquake and tsunami, (2) the capital city and surrounding areas of Tokyo, and (3) Kumamoto, which has recently experienced an earthquake. The findings show a correlation between the nature of the information received during COVID-19 and worriedness and the necessity for adequate information. The expected benefit of this study is to provide guidelines for the government or organizations to make a suitable emergency management plan based on pertinent factors for future pandemics.

Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning

The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms (decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis, with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants, and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed: COVID-19 and current political tensions. COVID19, Machine learning, Sentiment analysis, Tweet, Tourism, Thailand.

COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network.

With the amount of medical waste rapidly increasing since the corona virus disease 2019 (COVID-19) pandemic, medical waste treatment risk evaluation has become an important task. The transportation of medical waste is an essential process of medical waste treatment. This paper aims to develop an integrated model to evaluate COVID-19 medical waste transportation risk by integrating an extended type-2 fuzzy total interpretive structural model (TISM) with a Bayesian network (BN). First, an interval type-2 fuzzy based transportation risk rating scale is introduced to help experts express uncertain evaluation information, in which a new double alpha-cut method is developed for the defuzzification of the interval type-2 fuzzy numbers (IT2FNs). Second, TISM is combined with IT2FNs to construct a hierarchical structural model of COVID-19 medical waste transportation risk factors under a high uncertain environment; a new bidirectional extraction method is proposed to describe the hierarchy of risk factors more reasonably and accurately. Third, the BN is integrated with IT2FNs to make a comprehensive medical waste transportation risk evaluation, including identifying the sensitive factors and diagnosing the event's causation. Then, a case study of COVID-19 medical waste transportation is displayed to demonstrate the effectiveness of the proposed model. Further, a comparison of the proposed model with the traditional TISM and BN model is conducted to stress the advantages of the proposed model.

Exploring the mechanism of action of Xuanfei Baidu granule (XFBD) in the treatment of COVID-19 based on molecular docking and molecular dynamics.

Purpose: The Corona Virus Disease 2019 (COVID-19) pandemic has become a challenge of world. The latest research has proved that Xuanfei Baidu granule (XFBD) significantly improved patient's clinical symptoms, the compound drug improves immunity by increasing the number of white blood cells and lymphocytes, and exerts anti-inflammatory effects. However, the analysis of the effective monomer components of XFBD and its mechanism of action in the treatment of COVID-19 is currently lacking. Therefore, this study used computer simulation to study the effective monomer components of XFBD and its therapeutic mechanism. Methods: We screened out the key active ingredients in XFBD through TCMSP database. Besides GeneCards database was used to search disease gene targets and screen intersection gene targets. The intersection gene targets were analyzed by GO and KEGG. The disease-core gene target-drug network was analyzed and molecular docking was used for verification. Molecular dynamics simulation verification was carried out to combine the active ingredient and the target with a stable combination. The supercomputer platform was used to measure and analyze the number of hydrogen bonds, the binding free energy, the stability of protein target at the residue level, the solvent accessible surface area, and the radius of gyration. Results: XFBD had 1308 gene targets, COVID-19 had 4600 gene targets, the intersection gene targets were 548. GO and KEGG analysis showed that XFBD played a vital role by the signaling pathways of immune response and inflammation. Molecular docking showed that I-SPD, Pachypodol and Vestitol in XFBD played a role in treating COVID-19 by acting on NLRP3, CSF2, and relieve the clinical symptoms of SARS-CoV-2 infection. Molecular dynamics was used to prove the binding stability of active ingredients and protein targets, CSF2/I-SPD combination has the strongest binding energy. Conclusion: For the first time, it was found that the important active chemical components in XFBD, such as I-SPD, Pachypodol and Vestitol, reduce inflammatory response and apoptosis by inhibiting the activation of NLRP3, and reduce the production of inflammatory factors and chemotaxis of inflammatory cells by inhibiting the activation of CSF2. Therefore, XFBD can effectively alleviate the clinical symptoms of COVID-19 through NLRP3 and CSF2.

Epidemiological and virological surveillance of influenza viruses in China during 2020-2021.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, seasonal influenza activity declined globally and remained below previous seasonal levels, but intensified in China since 2021. Preventive measures to COVID-19 accompanied by different epidemic characteristics of influenza in different regions of the world. To better respond to influenza outbreaks under the COVID-19 pandemic, we analyzed the epidemiology, antigenic and genetic characteristics, and antiviral susceptibility of influenza viruses in the mainland of China during 2020-2021. METHODS: Respiratory specimens from influenza like illness cases were collected by sentinel hospitals and sent to network laboratories in Chinese National Influenza Surveillance Network. Antigenic mutation analysis of influenza virus isolates was performed by hemagglutination inhibition assay. Next-generation sequencing was used for genetic analyses. We also conducted molecular characterization and phylogenetic analysis of circulating influenza viruses. Viruses were tested for resistance to antiviral medications using phenotypic and/or sequence-based methods. RESULTS: In the mainland of China, influenza activity recovered in 2021 compared with that in 2020 and intensified during the traditional influenza winter season, but it did not exceed the peak in previous years. Almost all viruses isolated during the study period were of the B/Victoria lineage and were characterized by genetic diversity, with the subgroup 1A.3a.2 viruses currently predominated. 37.8% viruses tested were antigenically similar to reference viruses representing the components of the vaccine for the 2020-2021 and 2021-2022 Northern Hemisphere influenza seasons. In addition, China has a unique subgroup of 1A.3a.1 viruses. All viruses tested were sensitive to neuraminidase inhibitors and endonuclease inhibitors, except two B/Victoria lineage viruses identified to have reduced sensitivity to neuraminidase inhibitors. CONCLUSIONS: Influenza activity increased in the mainland of China in 2021, and caused flu season in the winter of 2021-2022. Although the diversity of influenza (sub)type decreases, B/Victoria lineage viruses show increased genetic and antigenic diversity. The world needs to be fully prepared for the co-epidemic of influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus globally.

Study on the Multi-Governance of Public Rental Housing Exit in China Based on the Evolutionary Game Theory

The COVID-19 pandemic has plunged the housing affordability and living sanitary conditions of China's low-income groups into a crisis. Public rental housing has made a significant contribution to alleviating this crisis, yet its exit problem has become more acute due to the impact of the epidemic. In order to explore how to effectively play the role of multi-governance in public rental housing exit, this paper adopts game theory to analyze the evolution of the behavioral strategies of government departments and public rental housing tenants in public rental housing exit by combining four influencing parameters: the epidemic severity, the epidemic influence coefficient, the public participation degree, and the public reporting fairness. In response to exploring the conditions for the effective functioning of the multi-governance model under the impact of the epidemic, the influences of parameter change on the stability of the system are analyzed. The results show that when the COVID-19 epidemic impact coefficient is high, government departments need to reduce their own supervision costs to achieve better multi-governance effects;when public reports are distorted, the government departments’ own supervision capabilities determine the impact of public participation on governance effects.

Impact of COVID-19 Measures on Mobility in Bangkok, Thailand

Transportation is considered to be one of the main activities of daily life. The COVID-19 pandemic, which had its origin at the end of 2019, restricted the movement of the people due to its deadly impacts. Most governments also had a hand in limiting the mobility of the people through various measures and policies. Thailand was one of the first countries impacted by COVID-19, and transportation in Bangkok, the capital city, was greatly affected by both government measures and the COVID-19 disease. In this study, the number of passengers using the various modes of public transportation in Bangkok and the daily number of COVID-19 cases there are analyzed, and a correlation is found. In addition, the measures taken against COVID-19 are summarized to identify any impacts on Bangkok’s mobility and transportation. The mobility and transportation data include the passengers of the four modes of transportation: air, water, road, and rail. The findings show a moderate yet significant correlation between the number of passengers and the daily number of COVID-19 cases. This correlation was also amplified by the announcements and policies of the government.

Interventions to support tourism and its impact on air quality - a case study of the Go To Travel Campaign in Japan. (Special Issue: COVID-19 and historical pandemics part 2.)

The effects of COVID-19 measures on the quantitative changes in atmospheric substances associated with economic activities have been explored in various studies. However, the specific relationship between the changes in economic activities due to government interventions and the associated changes in atmospheric substances remains unclear. This relationship needs to be understood in detail, especially in the tourism industry where conflicting responses are needed, such as promoting people flow and socioeconomic development while adopting measures to prevent the spread of infectious diseases. Therefore, this study focused on the tourism industry in Japan, used Google Earth Engine to calculate the changes in five atmospheric substances before and during the government's Go To Travel Campaign, which was implemented to support domestic travel, and analyzed the correlations with statistical data such as the number of travelers, use of buses, and taxis. As a result, a series of connected behaviors clarified the fact that socioeconomic activities affected by the government's Go To Travel Campaign surfaced with the changes in the atmospheric substances. To further understand the relationship between interventions to promote tourism and air quality, additional datasets and approaches are needed, including data on the operation status of facilities and the behavioral characteristics of Japanese travelers.

Consequences of COVID-19 on Health, Economy, and Tourism in Asia: A Systematic Review

Since the beginning of the COVID-19 situation, academic and disaster-related organizations have focused on the severity of COVID-19 and how to prevent the infection. The side effects of COVID-19 also created serious impacts on human lives from multiple perspectives. This study primarily aims to conduct a systematic review on the COVID-19 impacts from the aspects of health, economy, and tourism, focusing on Asia. We use the preferred reporting items for systematic reviews and meta-analyses as the main tool in conducting the systematic review analysis. The keywords related to the focused aspect are searched based on the two prominent academic journal databases: Web of Science and PubMed. In each aforementioned aspect, the consequences and the main stakeholders who were directly and indirectly affected are explained. Moreover, the connection between the three aspects based on the impact of COVID-19, which still continues to intensify, and strategies to prepare for future pandemic situation are also presented.

The effectiveness of governmental nonpharmaceutical interventions against COVID-19 at controlling seasonal influenza transmission: an ecological study.

BACKGROUND: A range of strict nonpharmaceutical interventions (NPIs) were implemented in many countries to combat the coronavirus 2019 (COVID-19) pandemic. These NPIs may also be effective at controlling seasonal influenza virus infections, as influenza viruses have the same transmission path as severe acute respiratory syndrome coronavirus 2. The aim of this study was to evaluate the effects of different NPIs on the control of seasonal influenza. METHODS: Data for 14 NPIs implemented in 33 countries and the corresponding influenza virological surveillance data were collected. The influenza suppression index was calculated as the difference between the influenza positivity rate during its period of decline from 2019 to 2020 and during the influenza epidemic seasons in the previous 9 years. A machine learning model was developed using an extreme gradient boosting tree regressor to fit the NPI and influenza suppression index data. The SHapley Additive exPlanations tool was used to characterize the NPIs that suppressed the transmission of influenza. RESULTS: Of all NPIs tested, gathering limitations had the greatest contribution (37.60%) to suppressing influenza transmission during the 2019-2020 influenza season. The three most effective NPIs were gathering limitations, international travel restrictions, and school closures. For these three NPIs, their intensity threshold required to generate an effect were restrictions on the size of gatherings less than 1000 people, ban of travel to all regions or total border closures, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask-wearing requirements and gathering limitations, whereas merely implementing a mask-wearing requirement, and not other NPIs, diluted the effectiveness of mask-wearing requirements at suppressing influenza transmission. CONCLUSIONS: Gathering limitations, ban of travel to all regions or total border closures, and closing some levels of schools were found to be the most effective NPIs at suppressing influenza transmission. It is recommended that the mask-wearing requirement be combined with gathering limitations and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and other potential pandemics.