Evaluation of COVID-19 Vaccines in Primary Prevention Against Infections and Reduction in Severity of Illness Following the Outbreak of Sars-Cov-2 Omicron Variant in Shanghai

Objectives: To evaluate COVID-19 vaccines in primary prevention against infections and lessening the severity of illness following the most recent outbreak of the SARS-CoV-2 Omicron variant in Shanghai. Method(s): To investigate whether inactivated vaccines were effective in protecting against COVID-19 infections, we estimated the odds ratio (OR) of the vaccination in COVID-19 cases vs. matched community-based healthy controls. To evaluate the potential benefits of vaccination in lowering the risk of symptomatic infection (vs. asymptomatic), we estimated the relative risk (RR) of symptomatic infections among diagnosed patients. We also applied the multivariate stepwise Logistic regression analyses to measure the risk of disease severity (symptomatic vs. asymptomatic and moderate/severe vs. mild) in COVID-19 patient cohort with vaccination status as an independent variable while controlling for potential confounding factors. Result(s): Out of the 153,544 COVID-19 patients included in the analysis, 118,124 (76.9%) patients had been vaccinated and 143,225(93.3%) were asymptomatic patients. Of the 10,319 symptomatic patients, 10,031(97.2%), 281(2.7%) and 7(0.1%) experienced mild, moderate, and severe infections, respectively. There is no evidence that the vaccination helped protect from infections (OR=0.82, p=0.613). The vaccination, however, offered a small but significant protection against symptomatic infections (RR=0.92, p < 0.001) and halved the risk of moderate/severe infections (OR=0.48, 95% CI: 0.37 - 0.61). Older age (> 60 years) and malignant tumors were significantly associated with moderate/severe infections. Gender also appeared to be a risk factor for symptomatic infections, with females being associated with a lower risk for moderate/severe illness. Conclusion(s): Inactivated COVID-19 vaccines helped provide a small but significant protection against symptomatic infections and halved risk of moderate/severe illness among symptomatic patients. The vaccination was not effective in blocking COVID-19 Omicron variant community spread.Copyright © 2023

Clinical Characteristics of Patients with Sars-Cov-2 Omicron Variant and COVID-19 Vaccination with Clinical Outcome Findings in Shanghai, China: A Single Center, Retrospective, Observational Study

Objectives: This study aimed to determine disease severity, clinical features, clinical outcome in hospitalized patients with the Omicron variant and evaluate the effectiveness of one-dose, two-dose, and three-dose inactivated vaccines in reducing viral loads, disease course, ICU admissions and severe diseases. Method(s): Retrospective cohort analysis was performed on 5,170 adult patients (>=18 years) identified as severe acute respiratory syndrome coronavirus 2 positive with Reverse Transcription Polymerase Chain Reaction admitted at Shanghai Medical Center for Gerontology between March 2022 and June 2022. COVID-19 vaccination effectiveness was assessed using logistic regression models evaluating the association between the risk of vaccination and clinical outcomes, adjusting for confounders. Result(s): Among 5,170 enrolled patients, the median age was 53 years, and 2,861 (55.3%) were male. 71.0% were mild COVID-19 cases, and cough (1,137 [22.0%]), fever (592 [11.5%]), sore throat (510 [9.9%]), and fatigue (334 [6.5%]) were the most common symptoms on the patient's first admission. Ct values increased generally over time and 27.1% patients experienced a high viral load (Ct value< 20) during their stay. 105(2.0%) of these patients were transferred to the intensive care unit after admission. 97.1% patients were cured or showed an improvement in symptoms and 0.9% died in hospital. The median length of hospital stay was 8.7+/-4.5 days. In multivariate logistic analysis, booster vaccination can significantly reduce ICU admissions and decrease the severity of COVID-19 outcome when compared with less doses of vaccine (OR=0.75, 95%CI, 0.62-0.91, P<=0.005;OR=0.99, 95%CI, 0.99-1.00, p<0.001). Conclusion(s): In summary, the most of patients who contracted SARSCoV-2 omicron variant had mild clinical features and patients with vaccination took less time to lower viral loads. As the COVID-19 pandemic progressed, an older and less vaccinated population was associated with higher risk for ICU admission and severe disease.Copyright © 2023

Disease X Testing: The results of an international external quality assessment exercise

The United Nations Secretary-General Mechanism (UNSGM) for investigation of the alleged use of chemical and biological weapons is the only established international mechanism of this type under the UN. The UNGSM may launch an international investigation, relying on a roster of expert consultants, qualified experts, and analytical laboratories nominated by the member states. Under the framework of the UNSGM, we organized an external quality assurance exercise for nominated laboratories, named the Disease X Test, to improve the ability to discover and identify new pathogens that may cause possible epidemics and to determine their animal origin. The "what-if" scenario was to identify the etiological agent responsible for an outbreak that has tested negative for many known pathogens, including viruses and bacteria. Three microbes were added to the samples, Dabie bandavirus, Mammarenavirus, and Gemella spp., of which the last two have not been taxonomically named or published. The animal samples were from Rattus norvegicus, Marmota himalayana, New Zealand white rabbit, and the tick Haemaphysalis longicornis. Of the 11 international laboratories that participated in this activity, six accurately identified pathogen X as a new Mammarenavirus, and five correctly identified the animal origin as R. norvegicus. These results showed that many laboratories under the UNSGM have the capacity and ability to identify a new virus during a possible international investigation of a suspected biological event. The technical details are discussed in this report.Copyright © 2022

HiBGT: High-Performance Bayesian Group Testing for COVID-19

The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models were proposed, which offer substantial improvements in group testing efficiency by precisely quantifying uncertainty in diagnoses, acknowledging varying individual risk and dilution effects, and guiding optimally convergent sequential pooled test selections. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. HPC and big data stacks are needed for assessing computational and statistical performance across fluctuating prevalence levels at large scales. Here, we study how to design and optimize critical computational components of Bayesian group testing, including lattice model representation, test selection algorithms, and statistical analysis schemes, under the context of parallel computing. To realize this, we propose a high-performance Bayesian group testing framework named HiBGT, based on Apache Spark, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve high-performance, highly scalable Bayesian group testing. We show that HiBGT can perform large-scale test selections (> 250 state iterations) and accelerate statistical analyzes up to 15.9x (up to 363x with little trade-offs) through a varied selection of sophisticated parallel computing techniques while achieving near linear scalability using up to 924 CPU cores. © 2022 IEEE.

SIREN - smart ventilation for infection risk mitigation and HVAC energy efficiency: a case study amid the COVID-19 pandemic

The COVID-19 pandemic has underscored the need for effective ventilation control in public buildings. This study develops and evaluates a smart ventilation control algorithm (SIREN) that dynamically adjusts zone and system-level HVAC operation to maintain an acceptable COVID-19 infection risk and HVAC energy efficiency. SIREN uses real-time building operation data and Trim & Respond control logic to determine zone primary and system outdoor airflow rates. An EnergyPlus and CONTAM co-simulation framework was developed to assess its performance across various control scenarios and US climate zones. Results show that SIREN can flexibly control infection risk within a customized threshold (e.g. 3%) for every zone, while traditional controls cannot. At the building level, SIREN's HVAC energy consumption is comparable to a fixed 70% outdoor airflow fraction scenario, while its infection risk is lower than the 100% outdoor airflow scenario, illustrating its potential for safe and energy-efficient HVAC operation during pandemics.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance. © Wolters Kluwer Health, Inc. All rights reserved.

REMDESIVIR RETAINS POTENT ACTIVITY AGAINST SARS-CoV-2 VARIANTS OF CONCERN

Background: Recent SARS-CoV-2 variants of concern (VOCs) have shown a progressive loss of sensitivity to monoclonal antibody therapeutics. Remdesivir (RDV) is a nucleotide analog prodrug that targets the viral RNA-dependent RNA polymerase (RdRp) Nsp12 and is approved to treat COVID-19 in hospitalized and non-hospitalized patients. Nsp12 is highly conserved across VOCs to date and RDV antiviral activity against previous VOCs (Alpha to Omicron BA.1) has been maintained. Here, we conduct a structural analysis of Nsp12 substitutions observed in recent Omicron subvariants (BA.2, BA.2.12.1, BA.4, BA.5 and BA.2.75) and assess RDV antiviral activity against clinical isolates and sitedirected mutants (SDMs) in a replicon system. Method(s): The prevalence of Nsp12 substitutions in Omicron subvariants was evaluated by analysis of sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) EpiCoV database. Structural analysis of identified substitutions was conducted on a prior cryo-electron microscopy-based model of the replication-transcription complex. Antiviral activity against subvariant clinical isolates was assessed by nucleoprotein ELISA in A549-hACE2-TMPRSS2 cells and by SDMs in the replicon system. Result(s): Genomic analysis of >1.4 million Omicron subvariant sequences revealed unique substitutions in Nsp12 compared to the ancestral WA1 strain. Besides P323L, present in all subvariants, G671S was observed in 95.9% of BA.2.75 sequences, F694Y was observed in <=1.9% of BA.4, BA.5 and BA.2.75 sequences, and Y521C was observed in 1.7% of BA.5 sequences. As anticipated, structural analysis of these substitutions showed no direct interaction with the incoming RDV nucleotide triphosphate or the viral RNA. Phenotyping of clinical isolates of Omicron subvariants BA.2, BA.2.12.1, BA.4, BA.5, and BA.2.75 consistently resulted in mean RDV EC50 values of 24.5 nM (BA.2) to 106.0 nM (BA.5). This represented 0.15-to 0.66-fold changes compared to WA1, indicating no loss of in vitro RDV antiviral activity against these VOCs. P323L, G671S, and F694Y were shown previously to have no impact on RDV antiviral activity. Similarly, the individual substitution Y521C showed no change in RDV susceptibility in the SARS-CoV-2 replicon system. Conclusion(s): RDV retained potent in vitro antiviral activity against all tested Omicron VOCs with potencies comparable to the WA1 isolate. These data support the continued use of RDV in patients infected with Omicron subvariants.

Socialist Political Economy with Chinese Characteristics and Research on the Chinese and Foreign Economies

In 2021, responding to changes in the world political and economic situation and basing itself on Marxist political economy, the New Marxian Economics Synthesis School led by Professor Enfu Cheng carried forward its traditions and forged ahead into the future. The school conducted active, in-depth research on how to uphold the integrity of socialist political economy with Chinese characteristics and enrich it with new elements, putting forward a series of theoretical innovations in areas that include the ten essentials of socialist political economy with Chinese characteristics, its sources of innovation and logical starting point, the orientation of its practice, and so forth. Based on these theoretical innovations, many of the scholars who make up the school engaged in lively discussion on a range of focal issues of today's Chinese economy, including common prosperity, the new "dual circulation" development pattern, artificial intelligence and the digital economy, the modernization of national governance and so on. In addition, they made searching criticisms of the financialization of the contemporary capitalist economy and of the new developments seen in liberalism and hegemonism since the COVID-19 pandemic broke out. In sum, they recorded a long series of fruitful theoretical achievements.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound popula-tion flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

A Smart CO2-Based Ventilation Control Framework to Minimize the Infection Risk of COVID-19 In Public Buildings

This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance. © International Building Performance Simulation Association, 2022

DOES THE COVID-19 MEDIA COVERAGE AFFECT AH PREMIUM DISPARITY?

This article explores the dynamic causality between the COVID-19 Media Coverage Index (MCI) in China (Chinese mainland and Hong Kong) and the AH premium index (both price and volatility) by applying a novel time-varying causality technology. Our findings show that the MCIs in China do not significantly cause the log-prices of the AH premium index throughout the full sample period, whereas significantly positive and time-varying causalities from the MCIs in China to the volatilities of the AH premium index are detected. The results thus provide evidence that the change of the MCIs does not lead to a wider or narrower AH premium but unidirectionally causes the change of its volatilities. Furthermore, the effect of MCIs in Chinese mainland on the AH premium volatilities is more pronounced and stable compared to that in Hong Kong, which indicates that the AH premium disparity is more sensitive to the media coverage in the Chinese mainland than in Hong Kong. Finally, the causal relationship from the MCIs in China to the AH premium volatilities disappears after November 2021. Our results provide implications for policymakers to decrease the fluctuation of the AH premium by effectively guiding the trend of media coverage;the results also remind AH stock investors to pay more attention to the COVID-19 media coverage. © 2023, Bucharest University of Economic Studies. All rights reserved.

Time-frequency connectedness and cross-quantile dependence between carbon emission trading and commodity markets: Evidence from China

This paper aims to investigate the dynamic connectedness and the cross-quantile dependence structure between carbon emission trading and commodity markets in China. We employ both the Baruník and Křehlík (2018) connectedness method and the Baruník and Kley (2019) cross-quantile dependence method to provide time-frequency-quantile evidence. In addition, we use a daily dataset from September 2, 2013, to September 30, 2022, to gauge the macroeconomic effects of the COVID-19 pandemic. We find that Petrochemical is the biggest contributor and recipient in the carbon-commodities system, and the results show that carbon markets are more influenced by other commodity markets than the reverse. Furthermore, the total connectedness is stronger in the short term but can increase over the long term, especially during the onset of COVID-19. The dynamic pair-wise results show that the carbon market can impact other commodity markets, but the effects are diverse and varied. The quantile-varying dependence between the carbon market and commodities is detected, and the cross-quantile dependence gradually strengthens as the trading days increase. This paper concludes with fruitful policy implications for resource decision-makers. © 2023 Elsevier Ltd

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients' lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

A Remote Visiting and Monitoring System for Neonatal Intensive Care Units via Internet of Things

To meet the visiting needs of families of children in neonatal intensive care unit and reduce the burden of hospital management during the COVID-19 epidemic, we developed a remote visiting and monitoring system using the internet of things. The Raspberry Pi is used as the core hardware platform. The real-time signal of the bedside monitor is converted into a virtual camera, and is connected to the Raspberry Pi which has a real camera with CMOS Serial Interface (CSI). The frames of the two cameras are collected via FFmpeg technology, and then are pushed to the cloud server through Real-Time Messaging Protocol (RTMP). The video streams are then transferred and distributed via a Nginx server running RTMP protocol, and finally are displayed on the web page via the Flask framework. When tested, the system ran stably, and the real-time pictures from the camera and the bedside monitor screen in the hospital were clearly shown on a personal computer or a mobile phone in a remote distance out of the hospital, just by click the link of the associated web page. We think this system is helpful for families to remotely visit the babies anywhere any time, and it is also helpful for hospitals to reduce the human workload and the financial expenditure. © 2022 ACM.

Integrating Strategy and Dosage: A New Conceptual Formula for Overcoming Unintended Effects in Public Health Crisis Communication (PHCC)

Lu and Jin provide insights into public health crisis communication (PHCC) by reconceptualizing how we think about the concept of dosage. The chapter extends the notion of dosage from the amount of exposure to publics' engagement over time in a competitive and conflicting media environment. Lu and Jin delineate a new direction in PHCC by formulating the effect of crisis communication strategy and dosage according to a chemical analogy of solution concentration (i.e. strategy) and volume of solution (i.e. dosage). First, this chapter visualizes PHCC as a neutralization process, in which the base solution (i.e. PHCC strategy and dosage) to neutralize the harm caused by the acid solution (i.e. a public health crisis). Second, this chapter further analogizes the PHCC as the base solution consisting of a solute dissolved into a solvent, where the solute is the message strategy (e.g. emotional appeal) and the solvent is the carrier of the message similar to messengers and channels. Lu and Jin define the concept of PHCC dosage as the volume of "base solution,” which will influence the effectiveness of the neutralization (i.e. PHCC). This new conceptual framework, illustrated with recent public health crisis cases, helps explain PHCC (in)effectiveness. Lu and Jin also provide a theoretical foundation for empirical studies that examine and predict how both the strategy and dosage of a crisis response message might exert intended and/or unintended effects among publics confronted with information clutters and desensitized by previous and/or ongoing crisis situations. The chapter explores new possibilities for research and application of PHCC. © 2023 John Wiley & Sons Ltd.

Evolution Analysis of Network Public Opinion Theme Based on LDA Model

Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the 'COVID-19' event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability. © 2022 IEEE.

Experts' consensus on severe acute respiratory syndrome coronavirus-2 vaccination of children.

At present, severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still rampant worldwide.As of September 10, 2021, there were about 222 million confirmed cases of corona virus disease 2019(COVID-19)and more than 4.6 million deaths worldwide.With the development of COVID-19 vaccines and the gradual vaccination worldwide, the increasing number of cases in children and unvaccinated young people has drawn attention.According to World Health Organization surveillance data, the proportion of COVID-19 infection cases in children gradually increased, and the proportion of cases in the age groups of under 5 years and 5-14 years increased from 1.0% and 2.5% in January 2020 to 2.0% and 8.7% in July 2021, respectively.At present, billions of adults have been vaccinated with various COVID-19 vaccines worldwide, and their protective effects including reducing infection and transmission, reducing severe disease and hospitalization, and reducing death, as well as high safety have been confirmed.Canada, the United States, Europe and other countries have approved the emergency COVID-19 vaccination in children and adolescents aged 12 to 17 years, and China has also approved the phased vaccination of COVID-19 vaccination in children and adolescents aged 3 to 17 years. For smooth advancement and implementation of COVID-19 vaccination in children, academic institutions, including National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, and The Society of Pediatrics, Chinese Medical Association organized relevant experts to reach this consensus on COVID-19 vaccination in children.Copyright © 2021 by the Chinese Medical Association.

Reconsidering Tourism Destination Images by Exploring Similarities between Travelogue Texts and Photographs

With the rise of user-generated content (UGC) and deep learning technology, more and more researchers construct and measure the tourism destination image (TDI) through online travelogues. However, due to the impact of COVID-19 prevention and control, the number of online travelogues has decreased significantly and, therefore, the scientific validity of the TDI based only on text or photos has been questioned. This research fills a gap by comparing the differences between visual and semantic images in terms of the overall image perception and image formation through natural language processing technology and image caption technology in obtaining TDIs, taking Tiantai County in Zhejiang Province of China as a case. Our results show that the texts and photographs shared major similarities in the overall TDI, but from the perspective of interest, they reflect differently. Therefore, when considering the data source selection for TDI research with a small number of travelogues, texts should be the main content, supplemented by photographs. © 2022 by the authors.

Liver injury caused by antiviral agents for COVID-19.

Antiviral therapy is important for COVID-19. Currently, the anti-2019-nCoV drugs in clinical trials include broad-spectrum antiviral drugs (alpha interferon and ribavirin), hemagglutinin inhibitors (arbidol), human immunodeficiency virus protease inhibitors (lopinavir/ritonavir and darunavir/cobicistat), nucleoside analogues (favipiravir and remdesivir) and antimalarial drug (chloroquine);while liver damage may occur in some patients with the medication. This article reviews the research on liver damage associated with anti-2019-nCoV drugs, aiming at promoting the safe and effective antiviral therapy for COVID-19 patients.Copyright © 2020 by the Chinese Medical Association.