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Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

Drawing on a recent online survey combined with city-level data, this paper examines the impact of the COVID-19 on consumers' online food purchase behavior in the short term. To address the potential endogeneity issues, we adopt an instrumental variable (IV) strategy, using the distance from the surveyed city to Wuhan as the instrumental variable. We show that our IV method is effective in minimizing potential bias. It is found that the share of confirmed COVID-19 cases increases the possibility of consumers purchasing food online. This is more likely to be the case for young people having a lower perceived risk of online purchases and living in large cities. Despite some limitations, this paper has policy implications for China and other countries that have been influenced by the COVID-19 epidemic. Specifically, government support and regulation should focus on (i) ensuring the safety of food sold on the internet, (ii) protecting the carrier from becoming infected, and (iii) providing financial support to the poor since they may have difficulties in obtaining access to food living in small cities. Moreover, how to help those who are unable to purchase food online because of their technical skills (e.g., the elderly who are not familiar with smart phones or the internet) also deserves more attention for the government and the public. [ABSTRACT FROM AUTHOR]

We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and 95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases. [ABSTRACT FROM AUTHOR]

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic. • M-ULS is introduced to break through the plight of city logistics. • A set of effective macro-micro incentive policies are established to promote the implementation of M-ULS. • The delivery time, transportation cost and government subsidies play different roles in the market penetration of M-ULS. • The benefits of M-ULS have been analyzed. [ABSTRACT FROM AUTHOR]

In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease (COVID-19) in Wuhan, China caused by SARS-CoV-2 has led to a serious epidemic in China and other countries, resulting in worldwide concern. With active efforts of prevention and control, more and more patients are being discharged. However, how to manage these patients normatively is still challenging. This paper reports an asymptomatic discharged patient with COVID-19 who retested positive for SARS-CoV-2, which arouses concern regarding the present discharge standards of COVID-19. [ABSTRACT FROM AUTHOR]

Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regional, hierarchical‐tier mathematical model for better understanding the complexity and heterogeneity of COVID‐19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi‐regional, hierarchical‐tier SLIHR model. With this model, we revealed insight into how COVID‐19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi‐regional, hierarchical‐tier SLIHR model revealed insight into how COVID‐19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China. [ABSTRACT FROM AUTHOR]

Objective: In December 2019, coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) broke out in Wuhan, China. The pandemic has posed a great challenge to radiation oncology departments, as interruptions in radiation therapy (RT) increase the risks of cancer recurrence or failure of the therapy as a whole. This study aimed to elucidate the impact of COVID‐19 on radiation therapy staff in China. Methods: As many working staff at different radiation oncology departments in China as possible were retrospectively enrolled from 23 January to 9 March 2020. They were then invited to answer a questionnaire, for essential data collection, from which their basic information, anxiety level, and workload were analyzed. Results: Seven (0.39%) of the 1 755 radiation therapy staff who answered the questionnaire had contracted COVID‐19, all of whom were from Wuhan. The factors influencing susceptibility were not sex (P = 1.000), age (P = 0.480), or comorbidities (P = 0.600), but geographic location (P < 0.001) and whether the respondent worked in a designated COVID‐19 hospital (P = 0.003). In terms of protection procedures, four participants carried out basic, one second‐level and two third‐level protection procedures. The difference was not statistically significant (P = 0.720). The infected respondents' anxiety level related to the outbreak (average score 6.57) was higher than that of their counterparts in Wuhan (5.18), as well as across the country (4.79), and 71.43% of those infected expressed the need for psychological interventions. During the epidemic, departments of 428 respondents (24.39%) shut down, while 76.71% of the respondents reported workload reduction. Conclusion: The factors related to COVID‐19 infection were the geographic location and whether the respondent worked in a designated COVID‐19 hospital. The infected respondents experienced greater psychological pressure than their uninfected counterparts and, therefore, required more psychological interventions. [ABSTRACT FROM AUTHOR]

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. [ABSTRACT FROM AUTHOR]

At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000. [ABSTRACT FROM AUTHOR]

Objective: This study aims to explore the indicators for severity of coronavirus disease 2019 (COVID-19) in young patients between the ages of 18 and 40 years.Methods: This retrospective cohort study included 65 consecutively admitted patients with COVID-19 who were between 18 and 40 years old in Zhongnan Hospital of Wuhan University in Wuhan, China. Among them, 53 were moderate cases, and 12 were severe or critical cases. Epidemiological, clinical, and laboratory characteristics and treatment data were collected. A multivariate logistic regression analysis was implemented to explore risk factors.Results: The patients with severe/critical cases had obviously higher BMI (average 29.23 vs. 22.79 kg/m2 ) and lower liver computed tomography value (average 50.00 vs. 65.00 mU) than the group of moderate cases. The patients with severe/critical cases had higher fasting glucose, alanine aminotransferase, aspartate aminotransferase, and creatinine compared with patients with moderate cases (all P < 0.01). More severe/critical cases (58.33% vs. 1.92%) had positive urine protein levels. The severe/critical cases also experienced a significant process of serum albumin decline. Logistic regression analysis showed that male sex, high BMI (especially obesity), elevated fasting blood glucose, and urinary protein positivity were all risk factors for young patients with severe COVID-19.Conclusions: Obesity is an important predictor of COVID-19 severity in young patients. The main mechanism is related to damage of the liver and kidney. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has been identified as the main cause of the outbreak of the respiratory disease in Wuhan, Hubei Province of China in December 2019. Since then, the epidemic has spread rapidly throughout China and many other countries in the world. This study, therefore, examines the spatiotemporal distribution of the confirmed cases of COVID-19 and its effect on human development in China, and suggested social and non-pharmaceutical preventive interventions to help curb the further spread of the disease. The public open data available from January to February 2020, from the National Health Commission of the People's Republic of China and a medical knowledge sharing website were used, and spatial analysis was performed to visualize the spatial distribution pattern of COVID-19 in China. The results showed among others that COVID-19 had entered a dispersed spatial pattern, resulting in increased pressure to control the spread of the disease. In early March, there was a significant reduction in the existing number of cases, and the number of deaths also decreased. At the provincial level, the spatial distribution of the number of cumulative confirmed cases in China was divided into four patterns: Hubei was the initial core region; the eastern provinces adjacent to Hubei formed the second concentrated pattern; the western provinces adjacent to Hubei and the northeastern and southeastern provinces which were separated from Hubei by one province belonged to the third distribution pattern; while the rest of the provinces in the north, south and west showing sporadic distribution patterns formed the fourth. It has been estimated that about 80% of students' online learning at all schools were not effective due to lack of access to reliable and uninterrupted internet services especially in the rural areas of China. [ABSTRACT FROM AUTHOR]

Background: In December 2019, a novel coronavirus-associated pneumonia, now known as coronavirus disease 2019 (COVID-19), was first detected in Wuhan, China. To prevent the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and treat patients with mild symptoms, sports stadiums and convention centers were reconstructed into mobile hospitals.Research Question: It is unknown whether a mobile cabin hospital can provide a safe treatment site for patients with mild COVID-19 symptoms.Study Design and Methods: This study retrospectively reviewed the medical records of 421 patients with COVID-19 admitted to a mobile cabin hospital in Wuhan from February 9, 2020, to March 5, 2020. Clinical data comprised patient age, sex, clinical presentation, chest imaging, nucleic acid testing, length of hospitalization, and outcomes.Results: Of the patients who were discharged from the cabin hospital, 362 (86.0%) were categorized as recovered; 14.0% developed severe symptoms and were transferred to a designated hospital. The most common presenting symptoms were fever (60.6%) and cough (52.0%); 5.2% exhibited no obvious symptoms. High fever (> 39.0°C) was more common in severe cases than in recovered cases (18.6% vs 6.6%). The distribution of lung lesions was peripheral in 85.0% of patients, multifocal in 69.4%, and bilateral in 68.2%. The most common pattern was ground-glass opacity (67.7%), followed by patchy shadowing (49.2%). The incidence of patchy shadowing was higher in patients with severe disease (66.1%) than in those who recovered (31.8%, P < .0001). The median length of hospitalization was 17 days (interquartile range, 14-19 days), and the median time taken for positive real-time reverse transcriptase polymerase chain reaction results to become negative in recovered patients was 8 days (interquartile range, 6-10 days).Interpretation: Mobile cabin hospitals provide a safe treatment site for patients with mild COVID-19 symptoms and offer an effective isolation area to prevent the spread of severe acute respiratory syndrome coronavirus 2. [ABSTRACT FROM AUTHOR]

In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been ≤ 1.0% since Mid-February. The LSTM model showed comparable accuracy. The GWR model took into account the influence of geographical differences, with R2 = 99.98% in fitting and 97.95% in prediction. Wilcoxon test showed that none of the three models outperformed the other two at the significance level of 0.05. The parametric analysis of the infectious rate and recovery rate demonstrated that China's national policies had effectively slowed down the spread of the epidemic. Furthermore, the models in this study provided a wide range of implications for other countries to predict the short-term and long-term trend of COVID-19, and to evaluate the intensity and effect of their interventions. [ABSTRACT FROM AUTHOR]

Background: Since the first cluster of cases was identified in Wuhan City, China, in December 2019, coronavirus disease 2019 (COVID-19) rapidly spreads globally. Scientists have made strides in estimating key transmission and epidemiological parameters. In particular, accumulating evidence points to a substantial fraction of asymptomatic or subclinical infections, which influences our understanding of the transmission potential and severity of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.Methods: We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis.Results: Our posterior estimates of basic reproduction number (R) in Wuhan City, China, in 2019-2020 reached values at 3.49 (95% CrI 3.39-3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23 in 2020 was associated with a significantly reduced R at 0.84 (95% CrI 0.81-0.88), with the total number of infections (i.e., cumulative infections) estimated at 1,906,634 (95% CrI 1,373,500-2,651,124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95% CrI 13.5-26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR at 0.04% (95% CrI 0.03-0.06%) and 0.12% (95% CrI 0.08-0.17%), respectively, estimates that are substantially smaller than the crude CFR estimated at 4.06%.Conclusions: We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China, during January-February 2020 using an ecological modeling approach that is suitable to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems. Our estimate of time-delay adjusted IFR falls in the range of the median IFR estimates based on multiple serological studies conducted in several areas of the world. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease (COVID-19) was first reported from Wuhan, China, on December 31st, 2019. As the number of coronavirus infections has exceeded 100,000 with toll deaths of about 5000 worldwide as of early March, 2020, scientists and researchers are racing to investigate the nature of this virus and evaluate the short and long term effects of this disease. Despite its negative impacts that obliged the World Health Organization to declare COVID-19 epidemic as a Public Health Emergency of International Concern, the rate of mortality of this infection has not exceeded 3.4% globally. On the other hand, the mortality rate caused by ambient air pollution has contributed to 7.6% of all deaths in 2016 worldwide. The outbreak of COVID-19 has forced China to lockdown its industrial activities and hence dropped its NO2 and carbon emissions by 30 and 25%, respectively. This work reports on the first case study that compares the air quality status before and after the crisis. It sheds light on the facts related to the demographics of deaths by gender, age and health status before infection. The historical data on air quality, estimates of annual deaths and its economic burden have been presented and analyzed. The actual daily deaths due to COVID-19 have been obtained from the official records of the daily Situation Reports published by World Health Organization as of March 11th. The rate of mortality due to COVID-19 was impacted by two factors: age and health status. Results show that 75% of deaths were related to cases that had underlying present diseases with the majority aged of 80+ years. The reported figures were compared with the average daily mortality due to poor air quality which reached up to 3287 deaths due to high levels of NO2, O3 and PM. The air quality status before the crisis was compared with the current situation showing that COVID-19 forced-industrial and anthropogenic activities lockdown may have saved more lives by preventing ambient air pollution than by preventing infection. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) first presented in Wuhan, Hubei province, China in December 2019. Since then, it has rapidly spread across the world, and is now formally considered a pandemic. The disease does not discriminate but increasing age and the presence of comorbidities are associated with severe form of the disease and poor outcomes. Although the prevalence of COVID-19 in patients with cardiovascular disease is under-reported, there is evidence that pre-existing cardiac disease can render individuals vulnerable. It is thought that COVID-19 may have both a direct and indirect effect on the cardiovascular system; however, the primary mechanism of underlying cardiovascular involvement is still uncertain. Of particular interest is the role of angiotensin-converting enzyme 2, which is well known for its cardiovascular effects and is also considered to be important in the pathogenesis of COVID-19. With a range of different drug candidates being suggested, effective anti-virals and vaccines are an area of on-going research. While our knowledge of COVID-19 continues to rapidly expand, this review highlights recent advances in our understanding of the interaction between COVID-19 and the cardiovascular system. [ABSTRACT FROM AUTHOR]

Coronavirus has become a very serious global health problem. Since December 2019, a number of new cases of patients with pneumonia caused by the Coronavirus disease have been found in Wuhan City, Hubei Province, China. With the global spread of the epidemic, other types of cases have been found in other regions of China and abroad. Factory workers play an important and essential role in the production of goods needed by society. Protecting them is essential to maintain the stability of factory productivity. This editorial provides guidance in how to protect factory workers from COVID-19 infection. [ABSTRACT FROM AUTHOR]

Background: Whether the patients with coronavirus disease 19 (COVID-19) infected by severe acute respiratory syndrome (SARS)-CoV-2 would commonly develop acute kidney injury (AKI) is an important issue worthy of clinical attention. This study aimed to explore the effects of SARS-CoV-2 infection on renal function through analyzing the clinical data of 116 hospitalized COVID-19-confirmed patients.Methods: One hundred sixteen COVID-19-confirmed patients enrolled in this study were hospitalized in the Department of Infectious Diseases, Renmin Hospital of Wuhan University from January 14 to February 13, 2020. The recorded information includes demographic data, medical history, contact history, potential comorbidities, symptoms, signs, laboratory test results, chest computer tomography scans, and treatment measures. SARS-CoV-2 RNA in 53 urine sediments of enrolled patients was detected by real-time reverse transcription-polymerase chain reaction.Results: Twelve (10.8%) patients showed mild increase of blood urea nitrogen or creatinine (<26 μmol/L within 48 h), and 8 (7.2%) patients showed trace or 1+ albuminuria in 111 COVID-19-confirmed patients without chronic kidney disease (CKD). All these patients did not meet the diagnostic criteria of AKI. In addition, 5 patients with CKD who were undergone regular continuous renal replacement therapy (CRRT) before admission were confirmed infection of SARS-CoV-2 and diagnosed as COVID-19. In addition to therapy for COVID-19, CRRT was also applied 3 times weekly during hospitalization for these 5 patients with CKD. In the course of treatment, the renal function indicators showed stable state in all 5 patients with CKD, without exacerbation of CKD, and pulmonary inflammation was gradually absorbed. All 5 patients with CKD were survived. Moreover, SARS-CoV-2 RNA in urine sediments was positive only in 3 patients from 48 cases without CKD, and 1 patient had a positive for SARS-CoV-2 open reading frame 1ab from 5 cases with CKD.Conclusion: AKI was uncommon in COVID-19. SARS-CoV-2 infection does not result in AKI, or aggravate CKD in the COVID-19 patients. [ABSTRACT FROM AUTHOR]