Analysis of Pathogenic Bacteria and Drug Resistance Among Girls with Bacterial Vaginitis.

Objective: The aim of this study was to analyze the prevalence of vaginal flora and drug resistance in bacterial vaginitis among girls. Methods: A total of 3099 girls (0-10 years old) with vaginitis who visited the Beijing Children's Hospital from January 2020 to December 2021 were included in the present study. The clinical data, results of bacterial culture of vaginal secretions, and drug sensitivity reports of the subjects were collected and analyzed. Results: Of the 3099 girls with vaginitis, 399 girls had a positive bacterial culture of vaginal secretions. Nineteen types of bacteria were cultured from the vaginal secretions of these 399 girls, with a total of 419 strains. The top three infective bacteria were Haemophilus influenzae (127 strains, 30.31%), Staphylococcus aureus (66 strains, 15.75%), and Streptococcus agalactiae (32 strains, 7.64%). Additionally, 20 girls were simultaneously infected with two types of bacteria. Staphylococcus aureus, Group G Streptococcus, Haemophilus parainfluenzae, and Pseudomonas aeruginosa more frequently occurred in mixed infections. The number and bacterial detection rate among school-age girls were higher than those of preschool-age girls. We found seasonal variation in infection rates, and vaginitis among girls was higher in summer. Recurrence of vaginitis in girls was not related to the type of pathogenic bacteria in the infection. Drug sensitivity analyses showed that the resistance rates of clindamycin and erythromycin were generally high, 70-100%. After the coronavirus disease 2019 outbreak, the resistance rates of some antibiotics had decreased to varying degrees. Conclusion: Improving the understanding of vaginal flora and drug resistance in girls with vaginitis will facilitate the selection of highly effective and sensitive antibacterial drugs and reduce the production of drug-resistant strains.

Research on Design Strategy of Mask Recycling Service Based on Behavior Environment

The global novel coronavirus pandemic has caused a surge in the use of masks worldwide. A large number of used masks that have not been properly handled enter the environment, which caused and will cause serious ecological problems. The purpose of this study is to propose a solution to the problem of mask management from the perspective of science of design, and to build a good mask recycling service design strategy through the combination of design and psychology. Firstly, based on the theory of behavioral environment and field investigation, this study analyzes the correlation between the existing mask recycling device and its recycling efficiency, user behavior psychology and environment, and studies the behavioral scene of mask recycling, and then establishes the center of design strategy implementation. Secondly, a visual guidance system is designed, as is a special recycling device for masks by color psychology and product design. Thirdly, combined with the concept of social innovation service design, the design of a mask recycling strategy is conceived, and the optimization and formulation of mask recycling strategy is demonstrated through stakeholders, user journey maps and service flow charts. Finally, the design strategy is hierarchically established, and the feasibility analysis system model of a mask recycling strategy design is constructed. The data collection is carried out through expert interviews and questionnaires, and the weight is calculated by a fuzzy analytic hierarchy process. The final output comprehensive evaluation results show that the mask recycling strategy constructed in this study has public recognition.

COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults.

OBJECTIVES: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. DESIGN: A cross-sectional study. SETTING: AncestryDNA customers in the USA who consented to research. PARTICIPANTS: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. RESULTS: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study. CONCLUSIONS: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Empirical Study of Monthly Economic Losses Assessments for "Standard Unit Lockdown" Due to COVID-19.

Background: The pandemic of COVID-19 has been shaping economic developments of the world. From the standpoint of government measures to prevent and control the epidemic, the lockdown was widely used. It is essential to access the economic losses in a lockdown environment which will provide government administration with a necessary reference for decision making in controlling the epidemic. Methods: We introduce the concept of "standard unit incident" and an economic losses assessment methodology for both the standard and the assessed area. We build a "standard unit lockdown" economic losses assessment system and indicators to estimate the economic losses for the monthly lockdown. Using the comprehensive assessment system, the loss infected coefficient of monthly economic losses during lockdown in the 40 countries has been calculated to assess the economic losses by the entropy weighting method (EWM) with data from the CSMAR database and CDC website. Results: We observe that countries in North America suffered the most significant economic losses due to the epidemic, followed by South America and Europe, Asia and Africa, and Oceania and Antarctica suffered relatively minor economic losses. The top 10 countries for monthly economic losses during lockdown were the United States, India, Brazil, France, Turkey, Russia, the United Kingdom, Italy, Spain, and Germany. The United States suffered the greatest monthly economic losses under lockdown ($65.3 billion), roughly 1.5 times that of China, while Germany suffered the least ($56.4 billion), roughly 1.3 times that of China. Conclusion: Lockdown as a control and mitigation strategy has great impact on the economic development and causes huge economic losses. The economic impact due to the pandemic has varied widely among the 40 countries. It will be important to conduct further studies to compare and understand the differences and the reasons behind.

Nationwide Trends of Pediatric Obesity and BMI z-Score From 2017-2021 in China: Comparable Findings From Real-World Mobile- and Hospital-Based Data.

Introduction: Lifestyle changes including COVID-19 lockdown cause weight gain and may change obesity trends; however, timely changes are largely unknown and monitoring measures are usually lack. This first large-scale study aimed to analyze the real-world national trends of obesity prevalence of Chinese children in the past five years, and the impact of COVID-19 pandemic on pediatric obesity development through both mobile- and hospital-based data. Methods: This study included children aged 3 to 19 years old all over China from January 2017 to April 2021. Hospital-measured and parent-reported cases from XIGAO database were analyzed. Body mass index (BMI) z-score calculation and obesity status evaluation were made according to Chinese standards. We evaluated obesity/overweight prevalence over the past five years and the changes of BMI z-score during COVID-19 lockdown. Results: A total of 656396 children from 31 provinces were involved, including 447481 hospital-measured cases and 208915 parent-reported cases. The obesity and overweight prevalence were 8.05% (95%CI 7.76%-8.39%) and 10.06% (95%CI 10.79%-11.55%), comparable to those of China National Nutrition Surveys during 2015-2019. Northern China had the highest obesity prevalence. Parent-reported data had higher obesity/overweight prevalence than hospital-measured data (18.3% [95%CI 17.7%-18.9%] vs. 21.7% [95%CI 20.7%-23.0%]). The trend of obesity prevalence remained stable with slight decrease, but COVID-19 lockdown caused a significant increase of 1.86% in 2020. Both mobile- and hospital-based data showed weight gain in the first half of 2020. High BMI z-score increase were found among primary and junior middle school children, and children in northeast area during lockdown. Conclusion: Weight gain during COVID-19 among Chinese children had regional differences and mainly affect primary and junior middle school children, thus warrants targeted interventions. The mobile growth assessment based on parent-reported data was a feasible, efficient and timely way for obesity monitoring among Chinese children, especially during epidemic.

Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects.

Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four 'expanded' phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four 'expanded' COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification.

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.

Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2-2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10-8) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10-13), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.

How long did crops survive from floods caused by Cyclone Idai in Mozambique detected with multi-satellite data

Floods are causing massive losses of crops and agricultural infrastructures in many regions across the globe. During the 2018/2019 agricultural year, heavy rains from Cyclone Idai caused flooding in Central Mozambique and had the greatest impact on Sofala Province. The main objectives of this study are to map the flooding durations, evaluate how long crops survived the floods, and analyse the dynamics of the affected crops and their recovery following various flooding durations using multi-source satellite data. Our results indicate that Otsu method-based flooding mapping provides reliable flood extents and durations with an overall accuracy higher than 90%, which facilitates the assessment of how long crops can survive floods and their recovery progress. Croplands in both Buzi and Tica administrative units were the most severely impacted among all the regions in Sofala Province, with the largest flooded cropland extent at 23,101.1 ha in Buzi on 20 March 2019 and the most prolonged flooding duration of more than 42 days in Tica and Mafambisse. Major summer crops, including maize and rice, could survive when the fields were inundated for up to 12 days, while all crops died when the flooding duration was longer than 24 days. The recovery of surviving crops to pre-flooding status took a much longer time, from approximately 20 days to as long as one month after flooding. The findings presented herein can assist decision making in developing countries or remote regions for flood monitoring, mitigation and damage assessment. [Display omitted] • Image-dependent threshold method precisely extracts flood extent and duration. • Multi-satellite analysis is feasible to quantify the dynamic flood impacts on crops. • Crops could survive even inundate for 12 days, but recovery was slow. • All crops die with flooding durations longer than 24 days. • The Buzi and Tica regions were severely damaged by the 2019 floods in Mozambique. [ FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: 10.7910/DVN/86M4PO (Zhang et al., 2020).

A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19.

Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN's backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.

Chinese and western medicine study on vigorous heat of Qi and Ying type in severe COVID-19 patients

Vigorous heat of Qi and Ying type in severe coronavirus disease 2019 COVID-19 patients mainly shows heat syndrome in Qi thermal perturbation syndrome in heart and Ying and stasis heat syndrome in blood it mainly shows dyspnea and or hypoxemia in Western medicine severe cases can be rapidly develop for acute respiratory distress syndrome sepsis shock blood coagulation dysfunction and multiple organ failure and so on. There is a consistency between the vigorous heat of Qi and Ying type in COVID 19 and sepsis in clinical manifestations development and treatment which provides theoretical basis for the treatment of severe COVID19 patients with vigorous heat of Qi and Ying type.

Development of a Physiologically Based Pharmacokinetic Model for Hydroxychloroquine and Its Application in Dose Optimization in Specific COVID-19 Patients.

In Feb 2020, we developed a physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine (HCQ) and integrated in vitro anti-viral effect to support dosing design of HCQ in the treatment of COVID-19 patients in China. This, along with emerging research and clinical findings, supported broader uptake of HCQ as a potential treatment for COVID-19 globally at the beginning of the pandemics. Therefore, many COVID-19 patients have been or will be exposed to HCQ, including specific populations with underlying intrinsic and/or extrinsic characteristics that may affect the disposition and drug actions of HCQ. It is critical to update our PBPK model of HCQ with adequate drug absorption and disposition mechanisms to support optimal dosing of HCQ in these specific populations. We conducted relevant in vitro and in vivo experiments to support HCQ PBPK model update. Different aspects of this model are validated using PK study from 11 published references. With parameterization informed by results from monkeys, a permeability-limited lung model is employed to describe HCQ distribution in the lung tissues. The updated model is applied to optimize HCQ dosing regimens for specific populations, including those taking concomitant medications. In order to meet predefined HCQ exposure target, HCQ dose may need to be reduced in young children, elderly subjects with organ impairment and/or coadministration with a strong CYP2C8/CYP2D6/CYP3A4 inhibitor, and be increased in pregnant women. The updated HCQ PBPK model informed by new metabolism and distribution data can be used to effectively support dosing recommendations for clinical trials in specific COVID-19 patients and treatment of patients with malaria or autoimmune diseases.

Molecular mechanism prediction analysis of compound Kushen injection in the treatment of COVID-19 based on network pharmacology and molecular docking

Background: As one of the eight effective traditional Chinese medicines for the treatment of atypical pneumonia, compound Kushen injection (CKI) played an important role in combating pneumonia caused by severe acute respiratory syndrome coronavirus 2 virus in China in 2003. CKI is known to inhibit inflammation, and its main chemical components, namely matrine and oxymatrine, can promote Th cells to recognize and eliminate viruses. In this study, network pharmacology and molecular docking were used to explore the mechanisms of CKI for treating coronavirus disease 2019. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and other related literature were used to screen CKI's active ingredients in the blood. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Swiss Target Prediction and STITCH were used to search for potential targets of the active ingredients. The "ingredient-target" network was constructed using the Cytoscape software. The STRING online database was used to construct a target protein-protein interaction network that can be visualized and analyzed using the Cytoscape software to obtain key targets. Results: Sophocarpine, sophoridine, matrine, (+)-allomatrine, AIDS211310, and sophranol were the six active ingredients. After docking the active ingredients with severe acute respiratory syndrome coronavirus 2 3CL hydrolase and angiotensin-converting enzyme 2 (ACE2), they displayed suitable affinity, which could block viral replication and its binding to ACE2. The key targets mainly involved inflammatory factors, such as interleukin-6 (IL-6) and tumor necrosis factor (TNF). Gene Ontology enrichment analysis mainly indicated the IL-6 cytokine-mediated signaling pathway and cytokine-mediated signaling pathway. The Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis mainly indicated steroid hormone biosynthesis and the TNF signaling pathway. Conclusion: The alkaloids in CKI can block viral replication and its binding to severe acute respiratory syndrome coronavirus 2 and ACE2 receptors. They regulate the IL-6-mediated signaling pathway, TNF signaling pathway, and steroid hormone biosynthesis, thereby initiating therapeutic responses against coronavirus disease 2019.

Therapeutic experience of an 89-year-old high-risk patient with incarcerated cholecystolithiasis: A case report and literature review.

BACKGROUND: The global pandemic of coronavirus disease 2019 pneumonia poses a particular challenge to the emergency surgical treatment of elderly patients with high-risk acute abdominal diseases. Elderly patients are a high-risk group for surgical treatment. If the incarceration of gallstones cannot be relieved, emergency surgery is unavoidable. CASE SUMMARY: We report an 89-year-old male patient with acute gangrenous cholecystitis and septic shock induced by incarcerated cholecystolithiasis. He had several coexisting, high-risk underlying diseases, had a history of radical gastrectomy for gastric cancer, and was taking aspirin before the operation. Nevertheless, he underwent emergency laparoscopic cholecystectomy, with maintenance of postoperative heart and lung function, successfully recovered, and was discharged on day 8 after the operation. CONCLUSION: Emergency surgery for elderly patients with acute abdominal disease is safe and feasible during the coronavirus disease 2019 pandemic, the key is to abide strictly by the hospital's epidemic prevention regulations, fully implement the epidemic prevention procedure for emergency surgery, fully prepare before the operation, accurately perform the operation, and carefully manage the patient postoperatively.

A metagenomic-based method to study hospital air dust resistome.

As a symbol of the defense mechanisms that bacteria have evolved over time, the genes that make bacteria resist antibiotics are overwhelmingly present in the environment. Currently, bacterial antibiotic resistance genes (ARGs) in the air are a serious concern. Previous studies have identified bacterial communities and summarized putative routes of transmissions for some dominant hospital-associated pathogens from hospital indoor samples. However, little is known about the possible indoor air ARG transportation. In this study, we mainly surveyed air-conditioner air dust samples under different airflow conditions and analyzed these samples using a metagenomic-based method. The results show air dust samples exhibited a complex resistome, and the average concentration is 0.00042 copies/16S rRNA gene, which is comparable to some other environments. The hospital air-conditioners can form resistome over time and accumulate pathogens. In addition, our results indicate that the Outpatient hall is one of the main ARG transmission sources, which can distribute ARGs to other departments (explains >80% resistome). We believe that the management should focus on ARG carrier genera such as Staphylococcus, Micrococcus, Streptococcus, and Enterococcus in this hospital and our novel evidence-based network strategy proves that plasmid-mediated ARG transfer can occur frequently. Overall, these results provide insights into the characteristics of air dust resistome and possible route for how ARGs are spread in air.

Assessment of monthly economic losses in Wuhan under the lockdown against COVID-19

With the outbreak of COVID-19 in Wuhan, aggressive countermeasures have been taken, including the implementation of the unprecedented lockdown of the city, which will necessarily cause huge economic losses for the city of Wuhan. In this paper, we attempt to uncover the interactions between epidemic prevention and control measures and economic-social development by estimating the health loss and meso-economic loss from a human-oriented perspective. We implemented a compartmental model for the transmission dynamics and health burden assessment to evaluate the health losses, then estimated the direct and indirect economic losses of industries using the Input-Output model. Based on these estimates, the first monthly health losses and meso-economic losses caused by the lockdown was assessed. The overall policy effect of the lockdown policy in Wuhan was also investigated. The health loss and meso-economic losses are used to evaluate the health burden and loss of residents' mental health, the direct economic loss of several worst-hit industries, and the indirect economic loss of all industries, respectively. Our findings reveal that the health burden caused by this pandemic is estimated to be 4.4899 billion yuan (CNY), and the loss of residents' mental health is evaluated to be 114.545 billion yuan, the direct economic losses in transport, logistics, and warehousing, postal service, food, and beverage service industries reach 21.6094 billion yuan, and the monthly indirect economic losses of all industries are 36.39661994 billion yuan caused by the lockdown. The total monthly economic losses during the lockdown reach 177.0413 billion yuan. However, the lockdown policy has been considered to reduce COVID-19 infections by >180 thousand, which saves about 20 thousand lives, as well as nearly 30 billion yuan on medical costs. Therefore, the lockdown policy in Wuhan has obvious long-term benefits on the society and the total economic losses will be at a controllable level if effective measures are taken to combat COVID-19.