A study on the assessment of risk management performance in maritime start-ups: evidence from China

Due to the COVID-19 pandemic, the international shipping market has been highly volatile, posing a serious threat to the survival and development of many maritime start-ups. With the development of the digital economy, digital transformation is affecting the evolution and upgrading of many traditional enterprises, including maritime enterprises. In the post-COVID-19 era, start-up small and medium-sized enterprises will need to consider the importance of enterprise risk management to achieve transformation and upgrading. The purpose of this study is to provide guidance for the establishment and upgrading of risk management systems for start-ups based on the identification of risk management strategies of maritime enterprises and the evaluation of their performance. The fuzzy analytic hierarchy process and importance-performance analysis methods were used to rank the operational risk, financial risk, market risk, innovation risk, and disaster risk according to sub-items and screen out the risk management schemes for priority improvements. Through empirical research, it was found that the financial risk and market risk response schemes have the lowest performance and need to be prioritised for improvement. This study argues that start-ups can appropriately challenge their risk management strategies to meet potential risk management needs based on their own circumstances.

Gasdermin D-mediated pyroptosis: mechanisms, diseases, and inhibitors.

Gasdermin D (GSDMD)-mediated pyroptosis and downstream inflammation are important self-protection mechanisms against stimuli and infections. Hosts can defend against intracellular bacterial infections by inducing cell pyroptosis, which triggers the clearance of pathogens. However, pyroptosis is a double-edged sword. Numerous studies have revealed the relationship between abnormal GSDMD activation and various inflammatory diseases, including sepsis, coronavirus disease 2019 (COVID-19), neurodegenerative diseases, nonalcoholic steatohepatitis (NASH), inflammatory bowel disease (IBD), and malignant tumors. GSDMD, a key pyroptosis-executing protein, is linked to inflammatory signal transduction, activation of various inflammasomes, and the release of downstream inflammatory cytokines. Thus, inhibiting GSDMD activation is considered an effective strategy for treating related inflammatory diseases. The study of the mechanism of GSDMD activation, the formation of GSDMD membrane pores, and the regulatory strategy of GSDMD-mediated pyroptosis is currently a hot topic. Moreover, studies of the structure of caspase-GSDMD complexes and more in-depth molecular mechanisms provide multiple strategies for the development of GSDMD inhibitors. This review will mainly discuss the structures of GSDMD and GSDMD pores, activation pathways, GSDMD-mediated diseases, and the development of GSDMD inhibitors.

SARS-CoV-2 N Protein Triggers Acute Lung Injury via Modulating Macrophage Activation and Infiltration in in vitro and in vivo.

Background: SARS-CoV-2-induced acute lung injury but its nucleocapsid (N) and/or Spike (S) protein involvements in the disease pathology remain elusive. Methods: In vitro, the cultured THP-1 macrophages were stimulated with alive SARS-CoV-2 virus at different loading dose, N protein or S protein with/without TICAM2-siRNA, TIRAP-siRNA or MyD88-siRNA. The TICAM2, TIRAP and MyD88 expression in the THP-1 cells after N protein stimulation were determined. In vivo, naïve mice or mice with depletion macrophages were injected with N protein or dead SARS-CoV-2. The macrophages in the lung were analyzed with flow cytometry, and lung sections were stained with H&E or immunohistochemistry. Culture supernatants and serum were harvested for cytokines measurements with cytometric bead array. Results: Alive SARS-CoV-2 virus or N protein but not S protein induced high cytokine releases from macrophages in a time or virus loading dependent manner. MyD88 and TIRAP but not TICAM2 were highly involved in macrophage activation triggered by N protein whilst both inhibited with siRNA decreased inflammatory responses. Moreover, N protein and dead SARS-CoV-2 caused systemic inflammation, macrophage accumulation and acute lung injury in mice. Macrophage depletion in mice decreased cytokines in response to N protein. Conclusion: SARS-CoV-2 and its N protein but not S protein induced acute lung injury and systemic inflammation, which was closely related to macrophage activation, infiltration and release cytokines.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine.

Background: Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM). Methods: Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified. Results: Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FCij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients. Conclusion: The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log2fold change (log2FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Do Socioeconomic Disparities Matter? Unraveling the Impacts of Online Vaccine Misinformation on Vaccination Intention During the COVID-19 Pandemic in China.

Concerns have been raised about whether and how groups at high risk of COVID-19 are more likely affected by online vaccine misinformation during the pandemic. This study examined the associations between exposure to online vaccine misinformation and vaccination intention through vaccination perceptions and investigated the moderating role of individuals' socioeconomic status. eHealth literacy was also investigated as a protective factor that mediated the effect of socioeconomic status. A survey of 1,700 Chinese netizens revealed that increased exposure to online COVID-19 vaccine misinformation predicted lower vaccination intention, which was mediated by negative attitudes, lowered subjective norms, lowered perceived benefits, and higher perceived barriers toward vaccination. Socio-economic status (i.e. education, income, and residence), in general, did not guarantee individuals against the negative impacts of vaccine misinformation. eHealth literacy is critical in reducing susceptibility to vaccine misinformation during the COVID-19 pandemic.

Investigation on the spatial and temporal patterns of coupling sustainable development posture and economic development in World Natural Heritage Sites: A case study of Jiuzhaigou, China

To continue directing global sustainable development efforts from 2015 to 2030, the United Nations adopted 17 global development goals known as the Sustainable Development Goals (SDGs) when the Millennium Development Goals (MDGs) from 2000 to 2015 expired. Sustainable development of World Natural Heritage Sites is one of these 17 MDGs and a crucial step toward achieving global sustainability. A scientific and systematic indicator system that can measure the sustainable development of natural World Heritage Sites more objectively and fairly is urgently needed to support the establishment of SDG11.4 on a Chinese scale and to help with the subsequent promotion of the development of natural World Heritage Sites. This study proposes a comprehensive assessment indicator system for the sustainable development of natural heritage sites based on the theoretical framework of "value contribution-environmental effect” to quantify the sustainable development of natural heritage sites. The study is based on the ecological environment and regional economic and social data of Jiuzhaigou World Natural Heritage Site from 2010 to 2020. Finally, the degree of coupling and coordination between the natural environment and economic development is assessed and studied. The results show that tourism to the World Heritage Site drove rapid economic development in Jiuzhaigou County between 2010 and 2020. As the fame of the World Heritage Site Jiuzhaigou has grown, so has the per capita income of local locals, making them unduly reliant on tourists for a living. Meanwhile, both the 2017 earthquake and the COVID-19 epidemic in 2019 have had substantial detrimental effects on the local economy. Furthermore, the Jiuzhaigou sustainable development trend from 2010 to 2020 exhibits a "W-shaped” curve, and there is a high level of positive coupling between the Jiuzhaigou sustainable development trend and economic development, and the two are mutually reinforcing.

Effects of enhanced hygiene measures on severe diarrhea and anastomotic leak after colorectal cancer surgery: the experience of a tertiary referral hospital in China.

Background: The 2019 novel coronavirus (COVID-19) global pandemic has greatly changed the mode of hospital admissions. This study summarized and analyzed the incidence of severe diarrhea and anastomotic leakage during different periods for colorectal cancer surgery. Methods: From January 2017 to September 2020, 2,619 colorectal operations were performed in Peking Union Medical College Hospital. In contrast with previous years, enhanced hand hygiene training, more frequent ventilation of the wards, and separate bed treatments for patients were implemented in 2020. Data on incidence of severe diarrhea and anastomotic leakage were retrieved and collected. Results: The number of cases of severe diarrhea after colorectal surgery was 32 (4.60%), 24 (3.33%), 32 (3.83%), and 11 (2.99%) in 2017, 2018, 2019, and 2020 respectively, while the incidence of anastomotic leakage was 3.30% (23/696), 3.75% (27/720), 2.87% (24/835), and 2.17% (8/368), respectively. There was no significant difference in the incidence of postoperative severe diarrhea or anastomotic leakage across the various years. Conclusions: The number of colorectal surgeries in 2020 was significantly decreased due to the COVID-19 pandemic. Among the different years, no difference was observed regarding the incidence of postoperative flora disorder or anastomosis leakage. Enhanced hygiene measures during the COVID-19 epidemic partially contributed to the decrease of severe diarrhea and anastomotic leakage.

Longitudinal proteomic investigation of COVID-19 vaccination (preprint)

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac) at a 28-day interval. Using TMT-based proteomics, we identified 1715 serum and 7342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

Effects of electromagnetic waves on pathogenic viruses and relevant mechanisms: a review.

Pathogenic viral infections have become a serious public health issue worldwide. Viruses can infect all cell-based organisms and cause varying injuries and damage, resulting in diseases or even death. With the prevalence of highly pathogenic viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is urgent to develop efficient and safe approaches to inactivate pathogenic viruses. Traditional methods of inactivating pathogenic viruses are practical but have several limitations. Electromagnetic waves, with high penetration capacity, physical resonance, and non-contamination, have emerged as a potential strategy to inactivate pathogenic viruses and have attracted increasing attention. This paper reviews the recent literature on the effects of electromagnetic waves on pathogenic viruses and their mechanisms, as well as promising applications of electromagnetic waves to inactivate pathogenic viruses, to provide new ideas and methods for this inactivation.

Proteomics Investigation of Diverse Serological Patterns in COVID-19 (preprint)

Serum antibodies IgM and IgG are elevated during COVID-19 to defend against viral attack. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their two-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense, and that high titers of IgM might not be favorable to COVID-19 recovery.

Using Humor to Promote Social Distancing on Tiktok During the COVID-19 Pandemic.

Background: During the COVID-19 pandemic, many humorous videos on how to practice social distancing appeared on social media. However, the effect of using humor as a crisis communication strategy to persuade people to conform to social distancing rules is not known. Objective: Drawing on the literature on humorous message framing and crisis communication, this research explores the effectiveness of a humorous message in communicating social distancing rules in two crisis severity phases (low vs. high severity) and also evaluates how humor affects individuals' online and offline engagement intentions during the COVID-19 pandemic. Methods: A 2 (message framing: humorous vs. non-humorous) x 2 (crisis severity phase: low vs. high) between-subjects design experiment was conducted to test the research questions during the first weeks of the COVID-19 pandemic in China from January 30 to February 2, 2020. Results: The results showed that the severity of the phase of a health crisis can significantly affect stakeholders' online and offline responses toward the disease. More specifically, in a low severity phase, humor led to increased source likability for the message, and more online and offline engagement intentions. However, no differences between a humorous and non-humorous message in perceived risk were observed. Whereas, in a high severity crisis phase, humor reduced individuals' offline engagement intentions and a decrease in perceived risk, no significant difference was found between a humorous and non-humorous message on source likeability. Conclusion: Humor can motivate both more online engagement and offline protective action intention when the crisis severity phase is low, while when crisis severity soars, a non-humorous message should be more desirable. More specifically, using humor in communicating information about an infectious disease can enhance the spokesperson's likeability in a low severity phase, and also helps to spread health information to a larger audience. While, the negative side of using humor in communicating an infectious disease appears in severe crisis phases, as it then decreased the public's perception of risk, and triggers less protective actions. Going beyond previous research, this study recognized that crisis severity changes in different phases of the spread of infectious disease, thereby providing actionable strategy selections for crisis practitioners in a dynamic communication environment.

iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition (preprint)

Online exams via video conference software like Zoom have been adopted in many schools due to COVID-19. While it is convenient, it is challenging for teachers to supervise online exams from simultaneously displayed student Zoom windows. In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis. To build such a novel system in its first kind, we overcome three challenges. First, we discover a lightweight approach to capturing exam video streams and analyzing them in real time. Second, we utilize the left-corner names that are displayed on each student's Zoom window and propose an improved OCR (optical character recognition) technique to automatically gather the ground truth for the student faces with dynamic positions. Third, we perform several experimental comparisons and optimizations to efficiently shorten the training and testing time required on teachers' PC. Our evaluation shows that iExam achieves high accuracy, 90.4% for real-time face detection and 98.4% for post-exam face recognition, while maintaining acceptable runtime performance. We have made iExam's source code available at https://github.com/VPRLab/iExam.

Association of Night Shift Work With Chronic Spontaneous Urticaria and Effect Modification by Circadian Dysfunction Among Workers.

Purpose: Night shift work is common in the current working environment and is a risk factor for many diseases. The study aimed to explore the relationship between night shift work with chronic spontaneous urticaria (CSU), and the modification effect of circadian dysfunction on it. Methods: A cross-sectional survey was conducted among Chinese workers. Exposure was measured by night work history and duration. Circadian dysfunction was characterized by excessive daytime sleepiness (EDS). The diagnosis of CSU was made by dermatologists who were investigating on the spot. The effect size was expressed as odds ratios (ORs). Results: A total of 8,057 participants were recruited, and 7,411 (92%) with complete information were included in the final analyses. The prevalence rates of CSU for workers without night shift and those with night shift history were 0.73 and 1.28%, respectively. Compared with workers who never worked night shifts, the risk of CSU increased with the length of night shift work: OR = 1.55 (95% confidence interval [CI]: 0.78-3.06) for duration <5 years and OR = 1.91 (95% CI: 1.12-3.26) for duration ≥5 years. EDS s EDS has been shown to modify this combination. Among workers without EDS, there was no association between night shift and CSU (OR = 0.94; 95% CI: 0.49-1.79). Whereas, in participants with EDS, the correlation was significant (OR = 3.58; 95% CI: 1.14-11.20). However, the effect modification by sleep disturbance was not observed. Conclusions: Night shift work is a risk factor for CSU, and there is a dose-response relationship between night shift work hours and the risk of CSU. This connection may be modified by circadian dysfunction.

Socioeconomic Determinants of Melanoma-Related Health Literacy and Attitudes Among College Students in China: A Population-Based Cross-Sectional Study.

Objectives: To investigate the association of gender, ethnicity, living region, and socioeconomic status (SES) with health literacy and attitudes toward nevi and melanoma in Chinese adolescents and to examine whether health literacy mediates the association of SES with attitudes. Study Design: A multicenter cross-sectional study was conducted among newly enrolled college students. First-year students were recruited from five universities in different regions of China in 2018 using the cluster sampling method. The observers were blinded to the participants. Methods: Health literacy and attitudes were measured using a previously validated tool (Nevus and Melanoma Health Literacy and attitudes Test). SES was measured by annual family income and parental highest educational level. Nonparametric test was used to examine the association of participants' characteristics with health literacy and attitudes. Two-level generalized linear model with logarithm link function and Gamma distribution was used individually for SES. The mediation effect model was used to examine the mediation effect of health literacy. Results: A total of 21,086 questionnaires were completed by college students with a mean age of 18.0 ± 0.8 years. The mean scores of health literacy and attitudes were 9.83 ± 7.46 (maximum score: 28) and 16.98 ± 2.92 (maximum score: 20), respectively. Female, Han nationality, annual family income, and parental educational levels were positively associated with health literacy and attitudes. Regional differences showed different effects on health literacy and attitudes. A mediation model showed that literacy mediated the association of SES with attitudes toward nevi and melanoma. Health literacy mediated ~30-50% of the association of SES with attitudes. Conclusions: Melanoma-related health literacy among Chinese college students is generally insufficient and needs to be improved. Targeted and personalized health education for improving health literacy related to nevi and melanoma may improve the general population's attitudes and further promote health-related behavior to prevent and identify early-stage melanoma.

Pulmonary and Renal Long COVID at Two-year Revisit (preprint)

More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Integration of protein context improves protein- based COVID-19 patient stratification (preprint)

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating the four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods: A SWATH-based proteomic data set of 54 sera samples from 40 COVID-19 patients was employed as the training cohort. Results: Machine learning prioritized two complexes, one stoichiometric ratio, five pathways, twelve proteins and five network degrees. A model based on these 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP complex, the stoichiometric ratio of SAA2/ YLPM1, and the network extent of SIRT7 and A2M were highlighted in this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort and an independent SWATH-based proteomic data set from Germany, reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Consumer Privacy Protection With the Growth of AI-Empowered Online Shopping Based on the Evolutionary Game Model.

Social distancing due to the COVID-19 pandemic has driven some consumers to online shopping, and concerns about pandemic risks and personal hygiene have increased the demand for e-commerce. Providing personalized recommendations seems quite profitable for e-commerce platforms, and consumers also benefit from personalized content with the advancement of AI technologies. However, this possible win-win situation is marred by the increase in consumers' privacy concerns. Technical solutions have been widely studied to protect consumer privacy, while few analyses have been conducted from the perspective of psychological and behavioral implications. In this paper, an evolutionary game model of privacy protection between e-commerce platforms and consumers is established to determine the mechanisms by which various factors exert influence, and evolutionary stable strategies are obtained from equilibrium points. Then, the strategy selections are simulated with MATLAB 2020 software. Based on the results, the following conclusions are drawn: (1) the application of AI technologies in e-commerce will fundamentally benefit consumers, which makes them actively share personal information with e-commerce platforms with incentives for generous rewards; (2) it is profitable for e-commerce platforms to conduct data mining by improving the ability to use AI technologies and making efforts to reduce technical costs; and (3) regulators should improve the level of supervision instead of imposing a large penalty to enhance consumer trust, which could effectively increase the profits of e-commerce platforms and protect consumers' privacy.

Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States.

Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.