Consumers' perceived value and use intention of cashless payment in the physical distancing context: evidence from an Asian emerging market

PurposeCOVID-19 and its measures such as physical distancing have shifted consumer payment behaviors toward cashless payment. Physical distancing is likely to remain a norm for some time to come and will be relevant in any future pandemics. This study aims to examine the impact of consumers' perceived value of cashless payment on their use intention in the physical distancing context, with the mediating role of psychological safety and the moderating role of trust propensity.Design/methodology/approachThis study used a survey method to obtain data from 690 consumers in an Asian emerging market, i.e. Vietnam. The data were analyzed using different statistical methods, including structural equation modeling.FindingsResults show that perceived value of cashless payment positively affects use intention, and this effect is mediated by psychological safety. Furthermore, trust propensity has a positive moderating effect on the link between perceived value and psychological safety.Practical implicationsThis study's findings provide implications for retailers and other stakeholders in implementing and promoting cashless payment systems, especially in the physical distancing context.Originality/valueThis study is among the first attempt to explain the relationships between consumers' perceptions, feelings of psychological safety and use intention toward cashless payment in the physical distancing context. The study's findings may also be relevant to any future pandemics.

Inactivated COVID-19 vaccine booster dose shortened the viral shedding time of patients infected with the Omicron variant BA.2.

OBJECTIVES: We assessed the effect of inactivated COVID-19 vaccine boosting immunization on the viral shedding time for patients infected with the Omicron variant BA.2. METHODS: We performed a real-world study by analyzing the outbreak data of patients infected with the COVID-19 Omicron variant BA.2 from March to May 2022 in Shanghai, China. Patients were categorized into three groups, including not fully vaccinated (zero and one dose), fully vaccinated (two doses), and booster-vaccinated (three doses). RESULTS: A total of 4443 patients infected with COVID-19 were included in the analysis. The proportion of viral shedding within 14 days in the three groups was 94.7%, 95.5%, and 96.7%, respectively (P <0.001). After adjusting for sex, age, underlying conditions, and clinical symptoms, the booster vaccination had a 29% increased possibility (hazard ratio: 1.29, 95% confidence interval: 1.18-1.41) of no detectable viral shedding within 14 days, whereas the fully vaccinated group had an 11% increased possibility of no detectable viral shedding (hazard ratio: 1.11, 95% confidence interval: 1.01-1.23). The effect of booster vaccination was more significant in males, the elderly, and people with underlying conditions or symptomatic infections. CONCLUSION: Our study confirmed that the booster vaccination could significantly shorten the viral shedding time of patients infected with the Omicron variant BA.2.

A university-clustered tuberculosis outbreak during the COVID-19 pandemic in eastern China.

During the COVID-19 pandemic in 2020, a tuberculosis outbreak occurred in a university in eastern China, with 4,488 students and 421 staff on the campus. A 19-year-old student was diagnosed in August 2019. Later, the first round of screening was initiated among close contacts, but no active cases were found. Till September 2020, four rounds of screening were performed. Four rounds of screening were conducted on September 9, November 8, November 22-25 in 2019 and September 2020, with 0, 5, 0 and 43 cases identified, respectively. A total of 66 active tuberculosis were found in the same university, including 4 sputum culture-positive and 7 sputum smear-positive. The total attack rate of active tuberculosis was 1.34% (66/4909). The whole-genome sequencing showed that the isolates belonged to the same L2 sub-specie and were sensitive to all tested antituberculosis drugs. Delay detection, diagnosis and report of cases were the major cause of this university tuberculosis epidemic. More attention should be paid to the asymptomatic students in the index class. After the occurrence of tuberculosis cases in schools, multiple rounds of screening should be carried out, and preventive therapy should be applied in a timely manner.

Consumers’ perceived value and use intention of cashless payment in the physical distancing context: evidence from an Asian emerging market

Purpose COVID-19 and its measures such as physical distancing have shifted consumer payment behaviors toward cashless payment. Physical distancing is likely to remain a norm for some time to come and will be relevant in any future pandemics. This study aims to examine the impact of consumers' perceived value of cashless payment on their use intention in the physical distancing context, with the mediating role of psychological safety and the moderating role of trust propensity. Design/methodology/approach This study used a survey method to obtain data from 690 consumers in an Asian emerging market, i.e. Vietnam. The data were analyzed using different statistical methods, including structural equation modeling. Findings Results show that perceived value of cashless payment positively affects use intention, and this effect is mediated by psychological safety. Furthermore, trust propensity has a positive moderating effect on the link between perceived value and psychological safety. Practical implications This study's findings provide implications for retailers and other stakeholders in implementing and promoting cashless payment systems, especially in the physical distancing context. Originality/value This study is among the first attempt to explain the relationships between consumers' perceptions, feelings of psychological safety and use intention toward cashless payment in the physical distancing context. The study's findings may also be relevant to any future pandemics.

Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19.

OBJECTIVES: Previous studies have suggested a relationship between outdoor air pollution and the risk of coronavirus disease 2019 (COVID-19). However, there is a lack of data related to the severity of disease, especially in China. This study aimed to explore the association between short-term exposure to outdoor particulate matter (PM) and the risk of severe COVID-19. METHODS: We recruited patients diagnosed with COVID-19 during a recent large-scale outbreak in eastern China caused by the Delta variant. We collected data on meteorological factors and ambient air pollution during the same time period and in the same region where the cases occurred and applied a generalized additive model (GAM) to analyze the effects of short-term ambient PM exposure on the risk of severe COVID-19. RESULTS: A total of 476 adult patients with confirmed COVID-19 were recruited, of which 42 (8.82%) had severe disease. With a unit increase in PM10, the risk of severe COVID-19 increased by 81.70% (95% confidence interval [CI]: 35.45, 143.76) at a lag of 0-7 days, 86.04% (95% CI: 38.71, 149.53) at a lag of 0-14 days, 76.26% (95% CI: 33.68, 132.42) at a lag of 0-21 days, and 72.15% (95% CI: 21.02, 144.88) at a lag of 0-28 days. The associations remained significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. With a unit increase in PM2.5, the risk of severe COVID-19 increased by 299.08% (95% CI: 92.94, 725.46) at a lag of 0-7 days, 289.23% (95% CI: 85.62, 716.20) at a lag of 0-14 days, 234.34% (95% CI: 63.81, 582.40) at a lag of 0-21 days, and 204.04% (95% CI: 39.28, 563.71) at a lag of 0-28 days. The associations were still significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. CONCLUSIONS: Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and that reducing air pollution may contribute to the control of COVID-19.

[A comparative study of time series models in predicting COVID-19 cases].

Objective: To compare the performances of different time series models in predicting COVID-19 in different countries. Methods: We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020. Results: For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively. Conclusions: The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.

Clinical Characteristics of Coronavirus Disease 2019 in China.

BACKGROUND: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. METHODS: We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. RESULTS: The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. CONCLUSIONS: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).

Collateral Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Tuberculosis Control in Jiangsu Province, China.

The coronavirus disease 2019 (COVID-19) pandemic may impede global tuberculosis elimination goals. In Jiangsu Province, China, tuberculosis notifications dropped 52% in 2020 compared to 2015-2019. Treatment completion and screening for drug resistance decreased continuously in 2020. Urgent attention must be paid to tuberculosis control efforts during and after the COVID-19 pandemic.

Are we ready to deal with a global COVID-19 pandemic? Rethinking countries' capacity based on the Global Health Security Index.

BACKGROUND: The Global Health Security (GHS) Index has been developed to measure a country's capacity to cope with a public health emergency; however, evidence for whether it corresponds to the response to a global pandemic is lacking. This study performed a multidimensional association analysis to explore the correlation between the GHS Index and COVID-19-associated morbidity, mortality, and disease increase rate (DIR) in 178 countries (regions). METHODS: The GHS Index and COVID-19 pandemic data - including total cases per million (TCPM), total deaths per million (TDPM), and daily growth rate - were extracted from online databases. The Spearman correlation coefficient was applied to describe the strength of the association between the GHS Index, sociological characteristics, and the epidemic situation of COVID-19. DIRs were compared, and the impact of the GHS Index on the DIR by the time of "lockdown" was visualized. RESULTS: The overall GHS Index was positively correlated with TCPM and TDPM, with coefficients of 0.34 and 0.41, respectively. Countries categorized into different GHS Indextiers had different DIRs before implementing lockdown measures. However, no significant difference was observed between countries in the middle and upper tiers after implementing lockdown measures. The correlation between GHS Index and DIR was positive five days before lockdown measures were taken, but it became negative 13 days later. CONCLUSIONS: The GHS Index has limited value in assessing a country's capacity to respond to a global pandemic. Nevertheless, it has potential value in determining the country's ability to cope with a local epidemic situation.

Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. The cumulative number of new confirmed cases and deaths are still increasing out of China. Independent predicted factors associated with fatal outcomes remain uncertain. RESEARCH QUESTION: The goal of the current study was to investigate the potential risk factors associated with fatal outcomes from COVID-19 through a multivariate Cox regression analysis and a nomogram model. STUDY DESIGN AND METHODS: A retrospective cohort of 1,590 hospitalized patients with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed by using Kaplan-Meier methods and a Cox proportional hazards model. A prognostic nomogram was formulated to predict the survival of patients with COVID-19. RESULTS: In this nationwide cohort, nonsurvivors included a higher incidence of elderly people and subjects with coexisting chronic illness, dyspnea, and laboratory abnormalities on admission compared with survivors. Multivariate Cox regression analysis showed that age ≥ 75 years (hazard ratio [HR], 7.86; 95% CI, 2.44-25.35), age between 65 and 74 years (HR, 3.43; 95% CI, 1.24-9.5), coronary heart disease (HR, 4.28; 95% CI, 1.14-16.13), cerebrovascular disease (HR, 3.1; 95% CI, 1.07-8.94), dyspnea (HR, 3.96; 95% CI, 1.42-11), procalcitonin level > 0.5 ng/mL (HR, 8.72; 95% CI, 3.42-22.28), and aspartate aminotransferase level > 40 U/L (HR, 2.2; 95% CI, 1.1-6.73) were independent risk factors associated with fatal outcome. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has sufficient discriminatory power with a C-index of 0.91 (95% CI, 0.85-0.97). The calibration plots also showed good consistency between the prediction and the observation. INTERPRETATION: The proposed nomogram accurately predicted clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance, and appropriate therapy should be considered in patients at high risk.

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

Identifying the outbreak signal of COVID-19 before the response of the traditional disease monitoring system.

SYNOPSIS: Early identification of the emergence of an outbreak of a novel infectious disease is critical to generating a timely response. The traditional monitoring system is adequate for detecting the outbreak of common diseases; however, it is insufficient for the discovery of novel infectious diseases. In this study, we used COVID-19 as an example to compare the delay time of different tools for identifying disease outbreaks. The results showed that both the abnormal spike in influenza-like illnesses and the peak of online searches of key terms could provide early signals. We emphasize the importance of testing these findings and discussing the broader potential to use syndromic surveillance, internet searches, and social media data together with traditional disease surveillance systems for early detection and understanding of novel emerging infectious diseases.

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

Background: Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods: We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings: In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940). Interpretation: A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding: Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicentre) and outside Hubei (non-epicentre): a nationwide analysis of China.

BACKGROUND: During the outbreak of coronavirus disease 2019 (COVID-19), consistent and considerable differences in disease severity and mortality rate of patients treated in Hubei province compared to those in other parts of China have been observed. We sought to compare the clinical characteristics and outcomes of patients being treated inside and outside Hubei province, and explore the factors underlying these differences. METHODS: Collaborating with the National Health Commission, we established a retrospective cohort to study hospitalised COVID-19 cases in China. Clinical characteristics, the rate of severe events and deaths, and the time to critical illness (invasive ventilation or intensive care unit admission or death) were compared between patients within and outside Hubei. The impact of Wuhan-related exposure (a presumed key factor that drove the severe situation in Hubei, as Wuhan is the epicentre as well the administrative centre of Hubei province) and the duration between symptom onset and admission on prognosis were also determined. RESULTS: At the data cut-off (31 January 2020), 1590 cases from 575 hospitals in 31 provincial administrative regions were collected (core cohort). The overall rate of severe cases and mortality was 16.0% and 3.2%, respectively. Patients in Hubei (predominantly with Wuhan-related exposure, 597 (92.3%) out of 647) were older (mean age 49.7 versus 44.9 years), had more cases with comorbidity (32.9% versus 19.7%), higher symptomatic burden, abnormal radiologic manifestations and, especially, a longer waiting time between symptom onset and admission (5.7 versus 4.5 days) compared with patients outside Hubei. Patients in Hubei (severe event rate 23.0% versus 11.1%, death rate 7.3% versus 0.3%, HR (95% CI) for critical illness 1.59 (1.05-2.41)) have a poorer prognosis compared with patients outside Hubei after adjusting for age and comorbidity. However, among patients outside Hubei, the duration from symptom onset to hospitalisation (mean 4.4 versus 4.7 days) and prognosis (HR (95%) 0.84 (0.40-1.80)) were similar between patients with or without Wuhan-related exposure. In the overall population, the waiting time, but neither treated in Hubei nor Wuhan-related exposure, remained an independent prognostic factor (HR (95%) 1.05 (1.01-1.08)). CONCLUSION: There were more severe cases and poorer outcomes for COVID-19 patients treated in Hubei, which might be attributed to the prolonged duration of symptom onset to hospitalisation in the epicentre. Future studies to determine the reason for delaying hospitalisation are warranted.

Clinical characteristics of COVID-19 infection in chronic obstructive pulmonary disease: a multicenter, retrospective, observational study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been a global pandemic disease, with more than 4 million cases and nearly 300,000 deaths. Little is known about COVID-19 in patients with chronic obstructive pulmonary disease (COPD). We aimed to evaluate the influence of preexisting COPD on the progress and outcomes of COVID-19. METHODS: This was a multicenter, retrospective, observational study. We enrolled 1,048 patients aged 40 years and above, including 50 patients with COPD and 998 patients without COPD, and with COVID-19 confirmed via high-throughput sequencing or real-time reverse transcription-polymerase chain reaction, between December 11, 2019 and February 20, 2020. We collected data of demographics, pathologic test results, radiologic imaging, and treatments. The primary outcomes were composite endpoints determined by admission to an intensive care unit, the use of mechanical ventilation, or death. RESULTS: Compared with patients who had COVID-19 but not COPD, those with COPD had higher rates of fatigue (56.0% vs. 40.2%), dyspnea (66.0% vs. 26.3%), diarrhea (16.0% vs. 3.6%), and unconsciousness (8.0% vs. 1.7%) and a significantly higher proportion of increased activated partial thromboplastin time (23.5% vs. 5.2%) and D-dimer (65.9% vs. 29.3%), as well as ground-glass opacities (77.6% vs. 60.3%), local patchy shadowing (61.2% vs. 41.4%), and interstitial abnormalities (51.0% vs. 19.8%) on chest computed tomography. Patients with COPD were more likely to develop bacterial or fungal coinfection (20.0% vs. 5.9%), acute respiratory distress syndrome (ARDS) (20.0% vs. 7.3%), septic shock (14.0% vs. 2.3%), or acute renal failure (12.0% vs. 1.3%). Patients with COPD and COVID-19 had a higher risk of reaching the composite endpoints [hazard ratio (HR): 2.17, 95% confidence interval (CI): 1.40-3.38; P=0.001] or death (HR: 2.28, 95% CI: 1.15-4.51; P=0.019), after adjustment. CONCLUSIONS: In this study, patients with COPD who developed COVID-19 showed a higher risk of admission to the intensive care unit, mechanical ventilation, or death.

Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. OBJECTIVE: To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status. METHODS: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities. RESULTS: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (1.61-4.17) among patients with two or more comorbidities. CONCLUSION: Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.

Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China.

Previous studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease (COVID-19) and the evidence of person-to-person transmission. Limited data are available for asymptomatic infections. This study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers. Epidemiological investigations were conducted among all close contacts of COVID-19 patients (or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community. Asymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples. Their clinical records, laboratory assessments, and chest CT scans were reviewed. As a result, none of the 24 asymptomatic cases presented any obvious symptoms while nucleic acid screening. Five cases (20.8%) developed symptoms (fever, cough, fatigue, etc.) during hospitalization. Twelve (50.0%) cases showed typical CT images of ground-glass chest and 5 (20.8%) presented stripe shadowing in the lungs. The remaining 7 (29.2%) cases showed normal CT image and had no symptoms during hospitalization. These 7 cases were younger (median age: 14.0 years; P=0.012) than the rest. None of the 24 cases developed severe COVID-19 pneumonia or died. The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases). Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia. Overall, the asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged.