Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

The relationship between anxiety and internet gaming disorder in children during COVID-19 lockdown: a network analysis.

Background: Internet gaming disorder (IGD) has become a social problem in children. Evidence from previous studies has proven that anxiety is associated with IGD. However, IGD was always assessed as a whole based on total scores, and the fine-grained relationship between anxiety and IGD was hidden. Objective: The present study aims to investigate the fine-grained relationship between anxiety and IGD in elementary school students during the COVID-19 lockdown, and to identify potential targets for psychological interventions. Methods: During the lockdown caused by the COVID-19 pandemic, 667 children from a primary school in China were investigated by the Spence Children's Anxiety Scale-Short Version and Internet Gaming Disorder Scale. R4.1.1 software was used to construct a network model, assess bridge centrality, and test the robustness of the network and conduct a network. Results: There were 23 cross-community edges (weight ranged from -0.03 to 0.12), and each node of anxiety was connected to different nodes of IGD. The nodes with the top 80th percentile bridge expected influence were A2 "social phobia" (0.20), A3 "panic disorder" (0.21) and IGD5 "escape" (0.22). The robustness of the network was acceptable. Conclusion: From the perspective of network analysis, the present study explored the correlation pathways between anxiety and IGD in children and identified social phobia and panic disorder as the best targets for intervention to reduce IGD.

Effects of dysfunctional beliefs about sleep on sleep quality and mental health among patients with COVID-19 treated in Fangcang shelter hospitals

Introduction With the COVID-19 pandemic in China, a large number of mild or ordinary confirmed cases have been sent to Fangcang shelter hospitals for treatment. We aimed to investigate the mental health condition of Fangcang patients 2 years after the pandemic when patients knew more about COVID-19 and the virus was less virulent. We focused on the effect of dysfunctional beliefs and attitudes about sleep on depression, anxiety, and insomnia. Methods A total of 1,014 patients from two large Fangcang shelter hospitals in Shanghai between 22 April and 8 May 2022 completed a set of questionnaires comprising: the Dysfunctional Beliefs and Attitudes about Sleep scale, the Generalized Anxiety Disorder scale, the Patient Health Questionnaire, and the Insomnia Severity Index scale. Results Results show that the positive screening rates for anxiety, depression, and insomnia among tested patients were 55.3, 27.0, and 47.8%, respectively. Patients were more likely to report higher anxiety, depression, and insomnia, and to endorse affective and sleep disorders if they were: female, aged 18–40 years, with undergraduate course or above, white-collar employees, or those who thought the pandemic would have severe economic effects. About 51.4% of the participants had dysfunctional beliefs about sleep to varying degrees. Compared with patients who had accurate beliefs about sleep, the ratios of insomnia, anxiety, and depression were significantly higher among patients with dysfunctional beliefs about sleep. Discussion Attention should be paid to the mental health problems of patients in Fangcang shelter hospitals. The results indicate that dysfunctional beliefs about sleep significantly increased anxiety, depression, and insomnia of Fangcang patients.

Effects of dysfunctional beliefs about sleep on sleep quality and mental health among patients with COVID-19 treated in Fangcang shelter hospitals.

Introduction: With the COVID-19 pandemic in China, a large number of mild or ordinary confirmed cases have been sent to Fangcang shelter hospitals for treatment. We aimed to investigate the mental health condition of Fangcang patients 2 years after the pandemic when patients knew more about COVID-19 and the virus was less virulent. We focused on the effect of dysfunctional beliefs and attitudes about sleep on depression, anxiety, and insomnia. Methods: A total of 1,014 patients from two large Fangcang shelter hospitals in Shanghai between 22 April and 8 May 2022 completed a set of questionnaires comprising: the Dysfunctional Beliefs and Attitudes about Sleep scale, the Generalized Anxiety Disorder scale, the Patient Health Questionnaire, and the Insomnia Severity Index scale. Results: Results show that the positive screening rates for anxiety, depression, and insomnia among tested patients were 55.3, 27.0, and 47.8%, respectively. Patients were more likely to report higher anxiety, depression, and insomnia, and to endorse affective and sleep disorders if they were: female, aged 18-40 years, with undergraduate course or above, white-collar employees, or those who thought the pandemic would have severe economic effects. About 51.4% of the participants had dysfunctional beliefs about sleep to varying degrees. Compared with patients who had accurate beliefs about sleep, the ratios of insomnia, anxiety, and depression were significantly higher among patients with dysfunctional beliefs about sleep. Discussion: Attention should be paid to the mental health problems of patients in Fangcang shelter hospitals. The results indicate that dysfunctional beliefs about sleep significantly increased anxiety, depression, and insomnia of Fangcang patients.

[Evolution and Characteristics of Full-process Vehicular VOCs Emissions in Tianjin from 2000 to 2020].

Vehicle emissions are an important source of anthropogenic volatile organic compound (VOCs) emissions in urban areas and are commonly quantified using vehicle emission inventories. However, most previous studies on vehicle emission inventories have incomplete emission factors and emission processes or insufficient consideration of meteorological parameters. Based on the localized full-process emission factors attained from tested data and previous studies, a method to develop a monthly vehicular VOC emission inventory of full process for the long-term was established, which covered exhaust and evaporative emissions (including running loss, diurnal breathing loss, hot soak loss, and refueling emission). Then, the method was used to develop a full-process vehicular VOC emission inventory in Tianjin from 2000 to 2020. The results showed that the total vehicular VOC emissions in Tianjin rose slowly and then gradually decreased. In 2020, the total emissions were 21400 tons. The light-duty passenger vehicles were the dominant contributors and covered 75.00% of the total emissions. Unlike the continuous decline in exhaust emissions, evaporative emissions showed an inverted U-shaped trend with an increasing contribution to total emissions yearly, accounting for 31.69% in 2020. Monthly emissions were affected by both vehicle activity and emission factors. VOC emissions were high in autumn and winter and low in spring and summer. During the COVID-19 epidemic in 2020, vehicle activity was limited by closure and control, making VOC emissions significantly lower than those during the same period in previous years. The method and data in this study can provide technical reference and a decision-making basis for air pollution prevention and control.

No Causal Effects Detected in COVID-19 and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Two Sample Mendelian Randomization Study.

New clinical observational studies suggest that Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a sequela of COVID-19 infection, but whether there is an exact causal relationship between COVID-19 and ME/CFS remains to be verified. To investigate whether infection with COVID-19 actually causes ME/CFS, this paper obtained pooled data from the Genome Wide Association Study (GWAS) and analyzed the relationship between COVID susceptibility, hospitalization and severity of COVID and ME/CFS, respectively, using two-sample Mendelian randomization (TSMR). TSMR analysis was performed by inverse variance weighting (IVW), weighted median method, MR-Egger regression and weighted mode and simple mode methods, respectively, and then the causal relationship between COVID-19 and ME/CFS was further evaluated by odds ratio (OR). Eventually, we found that COVID-19 severity, hospitalization and susceptibility were all not significantly correlated with ME/CFS (OR:1.000,1.000,1.000; 95% CI:0.999-1.000, 0.999-1.001, 0.998-1.002; p = 0.333, 0.862, 0.998, respectively). We found the results to be reliable after sensitivity analysis. These results suggested that SARS-CoV-2 infection may not significantly contribute to the elevated risk of developing CFS, and therefore ME/CFS may not be a sequela of COVID-19, but may simply present with symptoms similar to those of CFS after COVID-19 infection, and thus should be judged and differentiated by physicians when diagnosing and treating the disease in clinical practice.

The relations between different components of intolerance of uncertainty and symptoms of depression during the COVID-19 pandemic: A network analysis.

Background: The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic. Methods: A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network. Results: Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities. Conclusion: By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

Research on Emergency Response Policy for Public Health Emergencies in China-Based on Content Analysis of Policy Text and PMC-Index Model.

Policy is an important support for risk society to prevent and resolve crises. Based on the content analysis of the policy text and PMC-Index model, this paper takes texts of 327 public health emergency response policies (PHERP) at the central level in China from 1989 to 2022 as the analysis object, designs an indicator system, and combines qualitative and quantitative methods to evaluate the existing policies. The results of content analysis indicate that current policy focuses on emergency rather than preventive control, the main policy-making and issuing authority is the Ministry of Health and policies are mostly issued in the form of notice. The PMC-Index of ten selected policies is all ranked above acceptable, which means that the overall quality of policy text is relatively high. However, the PMC-Surface shows that there is still considerable variability in the scores of the main indicators for each policy. The top three main scoring indicators are policy nature, policy evaluation and policy instrument, while the bottom three are policy time, policy release agency and policy target groups, which reminds us that the design of policy text can still be improved in terms of optimizing policy time, policy issuing institutions and expanding policy target groups. In response to these problems, this paper puts forward six suggestions for optimization.

Effects of the COVID-19 Pandemic on Dietary Practices for Mothers in Japan

To the best of our knowledge, changes in mothers' dietary behaviors or perceptions of dietary intake during the COVID-19 pandemic have not been extensively discussed. Therefore, the present study aimed to clarify how the dietary behaviors of mothers living with their children changed during the pandemic in Japan. Ordered probit and interval regressions were applied to individual data for mothers living with children under 18 years of age from five independently conducted Surveys of Attitudes toward Food and Nutrition Education (Shokuiku in Japanese). The estimation results showed that although mothers were more likely to eat dinner with their families during the pandemic, attention to healthy dietary practices, frequency of eating nutritionally balanced food, and dietary practices for preventing lifestyle-related diseases were negatively affected by the pandemic. In conclusion, mothers were more likely to decrease their commitment to a healthy and well-balanced diet during the pandemic than before it. Further research based on the latest datasets with detailed information over the long-term is required.

Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19.

The outbreak of the COVID-19 pandemic has significantly augmented the complexity of information, adding to the challenges that firms face in effectively processing and grasping accurate information. As a result, the production uncertainty of firms has been seriously intensified during the pandemic, disrupting the normal operation of firms and their supply chains. Digital technologies serve as salient tools that help firms to process and analyse information, consequently enhancing firm resilience in the face of supply chain disruptions. This study aims to examine how digital technologies affect firm resilience in the context of COVID-19 through the lens of information processing theory and a large-scale survey conducted among Chinese manufacturers. Specifically, our study evaluates the mediating effect of supply chain integration (internal integration, customer integration and supplier integration) and the moderating effect of information complexity. The results show that supply chain integration plays a mediating role in the effect of digital technologies on firm resilience, and the mediation effect is particularly significant for customer integration. Furthermore, digital technologies have a stronger impact on firm resilience when information complexity is high. The findings advance our understanding and recognition of the resilience implications of digital technologies and provide important managerial implications for improving firm resilience in the context of COVID-19.

Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes.

BACKGROUND: Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. METHODS: In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. RESULTS: Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014-1.143, p=0.016), elevated glucose level (HR = 1.153, 95% CI = 1.038-1.28, p=0.0079), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001-1.014, p=0.022), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032-0.73, p=0.0036), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019-0.462, p=0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. CONCLUSIONS: By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.

Effects of Governmental Intervention on Foodborne Disease Events: Evidence from China.

Foodborne disease events (FDEs) endanger residents' health around the world, including China. Most countries have formulated food safety regulation policies, but the effects of governmental intervention (GI) on FDEs are still unclear. So, this paper purposes to explore the effects of GI on FDEs by using Chinese provincial panel data from 2011 to 2019. The results show that: (i) GI has a significant negative impact on FDEs. Ceteris paribus, FDEs decreased by 1.3% when government expenditure on FDEs increased by 1%. (ii) By strengthening food safety standards and guiding enterprises to offer safer food, government can further improve FDEs. (iii) However, GI has a strong negative externality. Although GI alleviates FDEs in local areas, it aggravates FDEs in other areas. (iv) Compared with the eastern and coastal areas, the effects of GI on FDEs in the central, western, and inland areas are more significant. GI is conducive to ensuring Chinese health and equity. Policymakers should pay attention to two tasks in food safety regulation. Firstly, they should continue to strengthen GI in food safety issues, enhance food safety certification, and strive to ensure food safety. Secondly, they should reinforce the co-governance of regional food safety issues and reduce the negative externality of GI.

Network dynamic model of epidemic transmission introducing a heterogeneous control factor.

The COVID-19 epidemic is not only a medical issue but also a sophisticated social problem. We propose a network dynamics model of epidemic transmission introducing a heterogeneous control factor. The proposed model applied the classical susceptible- exposed-infectious-recovered model to the network based on effective distance and was modified by introducing a heterogeneous control factor with temporal and spatial characteristics. International aviation data were approximately used to estimate the flux fraction matrix, and the effective distance was calculated. Through parameter estimation and simulation, the theoretical values of the modified model fit well with practical values. By adjusting the parameters and observing the change of the results, we found that the modified model is more in line with the actual needs and has higher credibility in the comprehensive analysis. The assessment shows that the number of confirmed cases worldwide will reach about 20 million optimistically. In severe cases, the peak value will exceed 80 million, and the late stage of the epidemic shows a long tail shape, lasting more than one and a half years. The effective way to control the global epidemic is to strengthen international cooperation and to impose international travel restrictions and other measures.

China Public Psychology Analysis About COVID-19 Under Considering Sina Weibo Data.

COVID-19 not only poses a huge threat to public health, but also affects people's mental health. Take scientific and effective psychological crisis intervention to prevent large-scale negative emotional contagion is an important task for epidemic prevention and control. This paper established a sentiment classification model to make sentiment annotation (positive and negative) about the 105,536 epidemic comments in 86 days on the official Weibo of People's Daily, the test results showed that the accuracy of the model reached 88%, and the AUC value was greater than 0.9. Based on the marked data set, we explored the potential law between the changes in Internet public opinion and epidemic situation in China. First of all, we found that most of the Weibo users showed positive emotions, and the negative emotions were mainly caused by the fear and concern about the epidemic itself and the doubts about the work of the government. Secondly, there is a strong correlation between the changes of epidemic situation and people's emotion. Also, we divided the epidemic into three period. The proportion of people's negative emotions showed a similar trend with the number of newly confirmed cases in the growth and decay period, and the extinction period. In addition, we also found that women have more positive emotional performance than men, and the high-impact groups is also more positive than the low-impact groups. We hope that these conclusions can help China and other countries experiencing severe epidemics to guide publics respond.

Impact of COVID-19 on Acute Stroke Presentation in a Designated COVID-19 Hospital.

Objectives: Thousands of designated COVID-19 hospitals have been set up in China to fight the ongoing COVID-19 pandemic. Anecdotal reports indicate a falling rate of acute stroke diagnoses in these hospitals during the COVID-19 period. We conducted an exploratory single-center analysis to estimate the change in acute stroke presentation at the designated COVID-19 hospitals. Methods: This retrospective observational study included all patients admitted to Yongchuan Hospital Affiliated to Chongqing Medical University with acute stroke between January 24 and March 10, 2020. Patient demographics, characteristics of the stroke, treatment details, and clinical outcomes were compared with those of patients admitted in the corresponding period in the year before (2019, "the pre-COVID-19 period"). Subgroup analysis was performed in the ischemic and hemorrhagic stroke groups. Results: A total of 110 patients presented with acute stroke symptoms during the COVID-19 pandemic, compared with 173 patients in the pre-COVID-19 period. A higher proportion of stroke patients presented to the hospital via emergency medical services during the pandemic (48.2 vs. 31.8%, p = 0.006). There was a lower proportion of ischemic stroke patients (50.9 vs. 65.3%, p = 0.016) than in the preceding year. There were significantly fewer patients with 90-day modified Rankin Scale score ≥3 in the COVID-19 period compared with the pre-COVID-19 period (17.3 vs. 30.6%, p = 0.012). Among patients with ischemic stroke, the mean time from patient arrival to vessel puncture for emergency endovascular therapy in the COVID-19 period was shorter than that in the pre-COVID-19 period (109.18 ± 71.39 vs. 270.50 ± 161.51 min, p = 0.002). Among patients with hemorrhagic stroke, the rate of emergency surgical operation in the COVID-19 period was higher than that in the pre-COVID-19 period (48.1 vs. 30.0%, p = 0.047). The mean time from patient arrival to emergency surgical operation (15.31 ± 22.89 vs. 51.72 ± 40.47 min, p = 0.002) was shorter in the COVID-19 period than in the pre-COVID-19 period. Conclusions: Although fewer acute stroke patients sought medical care in this designated COVID-19 hospital during the COVID-19 pandemic, this type of hospital was more efficient for timely treatment of acute stroke. Recognizing how acute strokes presented in designated COVID-19 hospitals will contribute to appropriate adjustments in strategy for dealing with acute stroke during COVID-19 and future pandemics.

Electrophysiological and Proarrhythmic Effects of Hydroxychloroquine Challenge in Guinea-Pig Hearts.

Hydroxychloroquine (HCQ), clinically established in antimalarial and autoimmune therapy, recently raised cardiac arrhythmogenic concerns when used alone or with azithromycin (HCQ+AZM) in Covid-19. We report complementary, experimental, studies of its electrophysiological effects. In patch clamped HEK293 cells expressing human cardiac ion channels, HCQ inhibited IKr and IK1 at a therapeutic concentrations (IC50s: 10 ± 0.6 and 34 ± 5.0 µM). INa and ICaL showed higher IC50s; Ito and IKs were unaffected. AZM slightly inhibited INa, ICaL, IKs, and IKr, sparing IK1 and Ito. (HCQ+AZM) inhibited IKr and IK1 (IC50s: 7.7 ± 0.8 and 30.4 ± 3.0 µM), sparing INa, ICaL, and Ito. Molecular induced-fit docking modeling confirmed potential HCQ-hERG but weak AZM-hERG binding. Effects of µM-HCQ were studied in isolated perfused guinea-pig hearts by multielectrode, optical RH237 voltage, and Rhod-2 mapping. These revealed reversibly reduced left atrial and ventricular action potential (AP) conduction velocities increasing their heterogeneities, increased AP durations (APDs), and increased durations and dispersions of intracellular [Ca2+] transients, respectively. Hearts also became bradycardic with increased electrocardiographic PR and QRS durations. The (HCQ+AZM) combination accentuated these effects. Contrastingly, (HCQ+AZM) and not HCQ alone disrupted AP propagation, inducing alternans and torsadogenic-like episodes on voltage mapping during forced pacing. O'Hara-Rudy modeling showed that the observed IKr and IK1 effects explained the APD alterations and the consequently prolonged Ca2+ transients. The latter might then downregulate INa, reducing AP conduction velocity through recently reported INa downregulation by cytosolic [Ca2+] in a novel scheme for drug action. The findings may thus prompt future investigations of HCQ's cardiac safety under particular, chronic and acute, clinical situations.

An analysis of COVID-19 impacts on S&P 500 and FinTech index

COVID-19 developed into an extremely serious pandemic by the middle of 2020. It has caused enormous negative impacts such as a crush to the global market. In this study, we tested the correlation between COVID-19 and stock market in a more intuitive way with the COVID-19 transmission rate and recovery rate. They were generated by using Unscented Kalman Filter method incorporated with SEIR model. Since the Unscented Kalman Filter method analyzes data at daily intervals, we can study the trend of COVID-19 development and the fund index rate change in detail.

Rapidly Remote: Providing Seamless Library Support during a Pandemic

This column describes efforts adapting services and programming to meet the changing needs of users during the COVID-19 pandemic. More specifically, the column outlines strategies for engaging patrons in both synchronous and asynchronous learning environments, details of the technologies used coupled with free or low-cost technology alternatives, and insight to lessons learned throughout the process that may be relevant to practicing hospital librarians.

A Nomogram-Based Prediction for Severe Pneumonia in Patients with Coronavirus Disease 2019 (COVID-19).

BACKGROUND: The outbreak of a novel coronavirus disease 2019 (COVID-19) is currently ongoing worldwide. A proportion of COVID-19 patients progress rapidly to acute respiratory failure. OBJECTIVE: We aimed to build a model to predict the risk of developing severe pneumonia in patients with COVID-19 in the early stage. METHODS: Data from patients who were confirmed to have COVID-19 and were admitted within 7 days from the onset of respiratory symptoms were retrospectively collected. The patients were classified into severe and non-severe groups according to the presence or absence of severe pneumonia during 1-2 weeks of follow-up. The clinical characteristics and laboratory indicators were screened by cross-validation based on LASSO regression to build a prediction model presented by a nomogram. The discrimination and stability, as well as the prediction performance of the model, were analysed. RESULTS: The neutrophil-lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus were collected for the model. Bootstrap resampling showed the apparent C-statistics, and the brier scores were 0.929 and 0.098. The optimism of the C-statistics and brier score was 0.0172 and -0.019, respectively. The adjusted C-statistics and brier score were 0.9108 and 0.1169, respectively. The optimal cut-off value of the total nomogram score was determined to be 119 according to the maximal Youden index. The sensitivity, specificity, positive predictive value, and negative predictive value for differentiating the presence and absence of severe pneumonia were 83%, 89%, 74%, and 94%, respectively. CONCLUSION: In our study, the neutrophil-lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus showed great discrimination and stability for the prediction of the presence of severe pneumonia and were selected for the model.