Path planning research for mobile robots in dynamic environments

At present, due to the COVID-19, China's social and economic development has slowed down. Some life service e-commerce platforms have successively launched "contactless delivery" services, which can effectively curb the spread of the epidemic. Robot distribution is the current mainstream, but robots are different from people and need to have accurate program settings. Both path planning and obstacle avoidance are currently top issues. This requires the mobile robot to successfully arrive at the destination while minimizing the impact on the surrounding environment and pedestrians, and avoiding encroachment on the movement space of pedestrians. Therefore, the mobile robot needs to be able to actively avoid moving pedestrians in a dynamic environment, in addition to avoiding static obstacles, and safely and efficiently integrate into the pedestrian movement environment. In this paper, the path planning problem of unmanned delivery robot is studied, and the path of mobile robot in the crowd is determined by global planning and local planning, and the matlab simulation is used for verification. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

[Performance of screening of contacts of COVID-19 cases in same flight].

Objective: To understand the performance of 2019-nCoV nucleic acid detection in screening of contacts of COVID-19 cases in same flights and provide evidence for the effective screening of persons at high risk for the infection in domestic flights. Methods: The information of passengers who took same domestic flights with COVID-19 cases in China from April 1, 2020 to April 30, 2022 were retrospectively collected,and χ2 test was used to analyze positive nucleic acid detection rates in the passengers in different times before the onsets of the index cases, in different seat rows and in epidemic periods of different 2019-nCoV variants. Results: During the study period, a total of 433 index cases were identified among 23 548 passengers in 370 flights. Subsequently, 72 positive cases of 2019-nCoV nucleic acid were detected in the passengers, in whom 57 were accompanying persons of the index cases. Further analysis of the another 15 passengers who tested positive for the nucleic acid showed that 86.67% of them had onsets or positive detections within 3 days after the diagnosis of the index cases, and the boarding times were all within 4 days before the onsets of the index cases. The positive detection rate in the passengers who seated in first three rows before and after the index cases was 0.15% (95%CI: 0.08%-0.27%), significantly higher than in the passengers in other rows (0.04%, 95%CI: 0.02%-0.10%, P=0.007),and there was no significant difference in the positive detection rate among the passengers in each of the 3 rows before and after the index cases (P=0.577). No significant differences were found in the positive detection rate in the passengers, except the accompanying persons, among the epidemics caused by different 2019-nCoV variants (P=0.565). During the Omicron epidemic period, all the positive detections in the passengers, except the accompanying persons, were within 3 days before the onset of the index cases. Conclusions: The screening test of 2019-nCoV nucleic acid can be conducted in the passengers took the same flights within 4 days before the onsets of the index cases on board. Passengers who seated within 3 rows from the index cases can considered as the close contacts at high risk for 2019-nCoV, for whom screening should be conducted first and special managements are needed. The passengers in other rows can be classified as general risk persons for screening and management.

Quantifying the COVID-19-affected tourism education in China: a potential crisis in tourism recovery

Human resources is a crucial factor in supporting the development of tourism as a labour-intensive industry. This research enhances the understanding of China's tourism education associated with the spread of COVID-19 and its implications for tourism recovery. Initial findings imply that: COVID-19 had a profound lagging negative effect on the intention to apply for tourism-related majors of examinees, which is severe challenging for tourism recovery, and the impact was more pronounced in typical tourism-dependent cities than in non-tourism-dependent cities. The MICE Economics and Management was least affected, while the Sports Tourism was most affected. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Mediating role of resilience in relationship between occupational stress and depression of staff of centers for disease control and prevention.

[Background] Since the outbreak of the COVID-19 epidemic, staff of the centers for disease control and prevention (CDC) have been burdened with heavy epidemic prevention control, and excessive occupational stress can cause depression and other psychological problems. [Objective] To explore the status of occupational stress, resilience, and depression of CDC staff and potential relationships between them. [Methods] From December 2020 to April 2021, a survey was conducted at provincial and municipal levels, and the stratified cluster sampling method was used at county (district) level to select a total of 3 514 samples. Their occupational stress, resilience, and depression status were evaluated using the Chinese Effort-Reward Imbalance Scale (ERI), the Chinese Connor-Davidson Resilience Scale (CD-RISC), and the Patient Health Questionnaire (PHQ-9). Independent sample t test, analysis of variance, chi2 test, Pearson correlation analysis, and mediation test (structural equation model) were conducted. [Results] The positive rate of occupational stress was 34.29% in the CDC staff, the resilience score was 66.28+/-15.32, and the positive rate of depression was 48.58%. Significant differences were found in the positive rates of occupational stress among different groups of gender, age, education background, marital status, administrative duty, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05);in the resilience scores among different groups of gender, age, administrative duty, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05);in the positive rates of depression among different groups of gender, age, educational background, personal monthly income, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05). Occupational stress was negatively correlated with resilience (r=-0.165, P < 0.01). Resilience was negatively correlated with depression (r=-0.383, P < 0.01). Occupational stress was positively correlated with depression (r=0.343, P < 0.01). The structural equation embracing a partial mediating effect of resilience on the relationship occupational stress and depression was established, and the partial mediating effect was 0.039, accounting for 10.46% of the total effect. [Conclusion] High positive rates of occupational stress, reduced resilience, and depression are shown among CDC staff in the context of the COVID-19 epidemic, and resilience partially mediates the effect of occupational stress on depression. The study findings suggest that improving resilience may reduce occupational stress and depression in CDC staff.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Design of an authentic project-based learning course using project management approach

Using case studies, business simulations, seminars and lectures, and textbooks of relevance to the subject are helpful with learning and knowledge application. However, assisting students in the matter of practicing complex real-world projects management and solving actual problems are essential. Authentic project-based learning (APBL) is an active pedagogical approach that requires real clients, real users, and projects involving in the curriculum that demand real solutions provided by the learners. APBL has been shown to provide students with challenges that lead to greater motivation, higher levels of student performance, and retention rate across elementary, secondary, and postsecondary levels. Therefore, as a means of enhancing student interest and motivation and compensating for the absence of direct face-to-face contact imposed on higher education by the Covid 19 pandemic, a marketing course was redesigned using APBL pedagogy and Project Management Body of Knowledge (R) Guide Project Management Institute). This paper presents and introduces how the APBL course was designed, implemented, and delivered in the fall semester of 2020. This paper also elaborates how students applied integrated brand promotion (IBP) strategies and project management skills to promote and deliver a new ballet production to audiences. Several important learning outcomes were realised, including maximising students' learning, increasing efficiency, and significantly minimising 'student syndrome' (procrastination). Future research and teaching opportunities with limitations of this project are provided at the end.

How does emotional labor influence restaurant employees' service quality during COVID-19? The roles of work fatigue and supervisor–subordinate Guanxi

Purpose: This study aims to test the relationship between emotional labor and service quality of the frontline employees of Chinese restaurants during the coronavirus disease pandemic (COVID-19). This study further investigated the mediating role of work fatigue (WF) and the moderating role of supervisor–subordinate Guanxi (SSG). Design/methodology/approach: The authors used a time-lag approach to gather data from a sample of 365 frontline staff members working in Chinese restaurants. All hypotheses were tested using SPSS and AMOS. Findings: First, restaurant frontline employees' deep acting was associated with higher service quality, whereas surface acting leads to lower service quality. Second, WF mediated the relationship between emotional labor and service quality. Third, SSG moderated the impact of emotional labor on WF during COVID-19. Research limitations/implications: All variables measured in this study were self-reported by restaurant frontline employees, which may increase the risk of common-method bias. However, this study enriches the literature on emotional labor, WF and SSG during COVID-19. Practical implications: COVID-19 has severely affected the hotel, restaurant and catering sector and especially the psychological state and the work performance of frontline employees. Restaurant managers should implement some measures to improve employees' service quality during COVID-19. Originality/value: The present findings show that restaurant frontline employees adopted various emotional labor strategies when they were faced with higher than usual job demands and the risk of infection during COVID-19. © 2023, Emerald Publishing Limited.

Spatiotemporal characteristics of ozone and the formation sensitivity over the Fenwei Plain.

High surface ozone (O3) levels affect human and environmental health. The Fenwei Plain (FWP), one of the critical regions for China's "Blue Sky Protection Campaign", has reported severe O3 pollution. This study investigates the spatiotemporal properties and the causes of O3 pollution over the FWP using high-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) from 2019 to 2021. This study characterizes spatial and temporal variations in O3 concentration by linking O3 columns and surface monitoring using a trained deep forest machine learning model. O3 concentrations in summer were 2-3 times higher than those found in winter due to higher temperatures and greater solar irradiation. The spatial distributions of O3 correlate with the solar radiation showing decreased trends from the northeastern to the southwestern FWP, with the highest O3 values in Shanxi Province and the lowest in Shaanxi Province. For urban areas, croplands and grasslands, the O3 photochemistry in summer is NOx-limited or in the transitional regime, while it is VOC-limited in winter and other seasons. Reducing NOx emissions would be effective for decreasing O3 levels in summer, while VOC reductions are necessary for winter. The annual cycle in vegetated areas included both NOx-limited and transitional regimes, indicating the importance of NOx controls to protect ecosystems. The O3 response to limiting precursors shown here is of importance for optimizing control strategies and is illustrated by emission changes during the 2020 COVID-19 outbreak.

A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Anti-COVID-19 Traditional Chinese Medicine Database

Background: Novel coronavirus pneumonia COVID-19 has become a serious threat to human health. Traditional Chinese Medicine (TCM) has a good clinical effect in the treatment of COVID-19, with a high effective rate and a low rate of turning to the serious stage. Objective(s): We generated the web-accessed anti-COVID-19 TCM database to provide the anti-COVID-19 TCM information to develop effective drugs for the treatment of COVID-19. Method(s): Herein, we collected these prescriptions data by querying the CNKI and Wanfang Chinese da-tabases, the clinical guidance for COVID-19 pneumonia diagnosis and treatment, and further set up the web-accessible anti-COVID-19 TCM database. Result(s): Altogether, 293 different prescriptions are applied in four different COVID-19 stages of treat-ment, and the prevention of COVID-19 is composed of 452 TCM components. Conclusion(s): The database provides comprehensive information for anti-COVID TCM and thus would help to investigate novel ways to develop new anti-COVID-19 agents.Copyright © 2022 Bentham Science Publishers.

Prediction and Analysis of Infectious Disease Visit Data Based on DPC

Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2).

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.Copyright © 2022 Editorial Department of Journal of Shanghai Second Medical University. All rights reserved.

Nanozyme: a New Approach for Anti-microbial Infections

Bacteria and viruses always posed a threat to human health. Most impressively, SARS-CoV-2 has raged around the world for almost three years, causing huge loss to human health. Facing increasing challenges of drug-resistance and poor treatment efficacy, new solutions are urgently needed to combat pathogenic microorganisms. Recently, nanozymes with intrinsic enzyme-like activities emerged as a promising new type of "antibiotics”. Nanozymes exhibit superior antibacterial and antiviral activities under physiological conditions by efficiently catalyzing generation of a large number of reactive oxygen species. Moreover, enhanced therapeutic effects are achieved in nanozyme-based therapy aided by the unique physicochemical properties of nanomaterials such as photothermal and photodynamic effects. This paper reviews the latest research progress in the field of anti-microbial nanozymes, systematically summarizes and analyzes the principles of nanozymes in the treatment of bacteria and viruses from a mechanistic point of view. An outlook on the future direction and the challenges of new anti-microbial infection nanomaterials are proposed to provide inspiration for developing next generation anti-microbial nanozymes. © 2023 Science Press. All rights reserved.

Safety and efficacy of apremilast in adults hospitalized with COVID-19

Introduction: Apremilast, a nonbiologic oral phosphodiesterase 4 (PDE4) inhibitor, was evaluated as a treatment for the hyperinflammatory response in patients (pts) hospitalized with severe COVID-19. Aims and Objectives: To assess the safety and effectiveness of apremilast plus standard of care (SoC) in pts hospitalized with severe COVID-19. Method(s): COMMUNITY (EudraCT 2020-002594-10) was a phase 3, double-blind, randomized, multinational, platform trial in adult pts hospitalized with COVID-19. Pts received apremilast 30 mg BID (APR) or placebo (PBO) for 14 days or until hospital discharge, whichever occurred first. Supportive care (per study center practices) was allowed except for CYP3A inducers and concurrent PDE4 antagonists. Result(s): From November 24, 2020 to June 4, 2021, 384 pts were enrolled (APR+SoC: n=194;PBO+SoC: n=190);enrollment halted early due to futility. Mean age was 56.5 years;59% were men. Most pts had a COVID-19 clinical severity score of 4 (48%) or 3 (29%) (range: 1-8, 1=death). Median (95% CI) time to confirmed clinical recovery through Day 29 (primary endpoint) was 14 (11-15) days for both groups (P=0.8779). All-cause mortality incidence rates through Day 29 (key secondary endpoint) were 18% (APR+SoC) and 17% (PBO+SoC) (P=0.9665). Treatmentemergent adverse event (TEAE) rates were similar between APR+SoC (54%;n=189) and PBO+SoC (55%;n=187) groups. TEAEs Common Terminology Criteria AE grade >=3 occurred in 25% of APR+SoC pts and 30% of PBO+SoC pts. Serious TEAE rates were 27% (APR+SoC) and 30% (PBO+SoC). Conclusion(s): Although APR+SoC did not improve survival in pts hospitalized with severe COVID-19, APR was well tolerated with a safety profile consistent with the established safety profile.

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2)

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.

Nanozyme: a New Approach for Anti-microbial Infections

Bacteria and viruses always posed a threat to human health. Most impressively, SARS-CoV-2 has raged around the world for almost three years, causing huge loss to human health. Facing increasing challenges of drug-resistance and poor treatment efficacy, new solutions are urgently needed to combat pathogenic microorganisms. Recently, nanozymes with intrinsic enzyme-like activities emerged as a promising new type of "antibiotics". Nanozymes exhibit superior antibacterial and antiviral activities under physiological conditions by efficiently catalyzing generation of a large number of reactive oxygen species. Moreover, enhanced therapeutic effects are achieved in nanozyme-based therapy aided by the unique physicochemical properties of nanomaterials such as photothermal and photodynamic effects. This paper reviews the latest research progress in the field of anti-microbial nanozymes, systematically summarizes and analyzes the principles of nanozymes in the treatment of bacteria and viruses from a mechanistic point of view. An outlook on the future direction and the challenges of new anti-microbial infection nanomaterials are proposed to provide inspiration for developing next generation anti-microbial nanozymes.

Modeling and Reviewing Analysis of the COVID-19 Epidemic in Algeria with Diagnostic Shadow

In this paper, we formulate a special epidemic dynamic model to describe the transmission of COVID-19 in Algeria. We derive the threshold parameter con-trol reproduction number (R0c), and present the effective control reproduction number (Rc(t)) as a real-time index for evaluating the epidemic under different control strate-gies. Due to the limitation of the reported data, we redefine the number of accumu-lative confirmed cases with diagnostic shadow and then use the processed data to do the optimal numerical simulations. According to the control measures, we divide the whole research period into six stages. And then the corresponding medical resource estimations and the average effective control reproduction numbers for each stage are given. Meanwhile, we use the parameter values which are obtained from the optimal numerical simulations to forecast the whole epidemic tendency under different control strategies.

Personal GHG emissions accounting and the driving forces decomposition in the past 10 years

Personal greenhouse gas (PGHG) emissions were crucial for achieving carbon peak and neutrality targets. The accounting methodology and driving forces identification of PGHG emissions were helpful for the quantification and the reduction of the PGHG emissions. In this study, the methodology of PGHG emissions was developed from resource obtaining to waste disposal, and the variations of Shanghainese PGHG emissions from 2010 to 2020 were evaluated, with the driving forces analysis based on Logarithmic Mean Divisia Index (LMDI) model. It showed that the emissions decreased from 3796.05 (2010) to 3046.87 kg carbon dioxides (CO2) (2014) and then increased to 3411.35 kg CO2 (2018). The emissions from consumptions accounted for around 62.1% of the total emissions, and that from waste disposal were around 3.1%, which were neglected in most previous studies. The PGHG emissions decreased by around 0.53 kg CO2 (2019) and 405.86 kg CO2 (2020) compared to 2018 and 2019, respectively, which were mainly affected by the waste forced source separation policy and the COVID-19 pandemic. The income level and consumption GHG intensity were two key factors influencing the contractively of GHG emissions from consumption, with the contributing rate of 169.3% and − 188.1%, respectively. Energy consumption was the main factor contributing to the growth of the direct GHG emissions (296.4%), and the energy GHG emission factor was the main factor in suppressing it (− 92.2%). Green consumption, low carbon lifestyles, green levy programs, and energy structure optimization were suggested to reduce the PGHG emissions. © 2023, The Author(s).

How rumors diffuse in the infodemic: Evidence from the healthy online social change in China

Infodemic is defined as 'an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it' by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through usergenerated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.

Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.