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1.
Chaos ; 32(4):041106, 2022.
Article in English | MEDLINE | ID: covidwho-1830316

ABSTRACT

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing-Tianjin-Hebei (BTH), Chengdu-Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

2.
Nat Med ; 2022.
Article in English | PubMed | ID: covidwho-1830085

ABSTRACT

Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether and for how long this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible (SLIRS) model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (i.e., number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies, and non-pharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6-times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of non-pharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies.

3.
Cellular & Molecular Immunology ; 19(5):577-587, 2022.
Article in English | MEDLINE | ID: covidwho-1830043

ABSTRACT

Neutrophil extracellular traps (NETs) can capture and kill viruses, such as influenza viruses, human immunodeficiency virus (HIV), and respiratory syncytial virus (RSV), thus contributing to host defense. Contrary to our expectation, we show here that the histones released by NETosis enhance the infectivity of SARS-CoV-2, as found by using live SARS-CoV-2 and two pseudovirus systems as well as a mouse model. The histone H3 or H4 selectively binds to subunit 2 of the spike (S) protein, as shown by a biochemical binding assay, surface plasmon resonance and binding energy calculation as well as the construction of a mutant S protein by replacing four acidic amino acids. Sialic acid on the host cell surface is the key molecule to which histones bridge subunit 2 of the S protein. Moreover, histones enhance cell-cell fusion. Finally, treatment with an inhibitor of NETosis, histone H3 or H4, or sialic acid notably affected the levels of sgRNA copies and the number of apoptotic cells in a mouse model. These findings suggest that SARS-CoV-2 could hijack histones from neutrophil NETosis to promote its host cell attachment and entry process and may be important in exploring pathogenesis and possible strategies to develop new effective therapies for COVID-19.

4.
Frontiers in Medicine ; 9:799736, 2022.
Article in English | MEDLINE | ID: covidwho-1817972

ABSTRACT

Background: The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has caused millions of deaths and greatly influenced the timely diagnosis and treatment of other diseases. Throughout the pandemic, there was a dramatic reduction in the prevalence of several sexually transmitted infections. However, the impact of the ongoing pandemic on human papillomavirus (HPV) infection rates has not been investigated thus far. Materials and Methods: We retrospectively collected data regarding HPV and cervical cancer screening results of outpatients from gynecological clinics of a tertiary hospital from 1 December 2018 to 31 December 2020 in Wuhan. Based on the timeline of the SARS-CoV-2 pandemic in Wuhan, we divided this period into four relatively independent stages to compare the HPV screening visit numbers and infection rates. Results: There was a 50% drop in HPV screening visits and a 10% drop in HPV infection rates throughout the pandemic when compared with the numbers collected before the pandemic. Strict lockdown measures greatly decreased the HPV infection rate (17.03 vs. 8.29, P = 0.003). During the pandemic, the most prevalent HPV genotypes were HPV 16, 52, 58, and 53. After the pandemic, the HPV infection rate recovered quickly, but it was still slightly lower than the infection rate found before the outbreak (23.3 vs. 21.2%). Conclusion: During coronavirus disease 2019 (COVID-19) pandemic, cervical cancer screening visits and HPV infection rates have decreased dramatically. The HPV transmission has also decreased after strict lockdown. Effective HPV and cervical cancer screening programs should be strengthened immediately to reduce the transmission of HPV during and after the pandemic.

5.
Front Microbiol ; 13:735363, 2022.
Article in English | PubMed | ID: covidwho-1809432

ABSTRACT

OBJECTIVE: We aimed to evaluate the performance of nanopore amplicon sequencing detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples. METHOD: We carried out a single-center, prospective cohort study in a Wuhan hospital and collected a total of 86 clinical samples, including 54 pharyngeal swabs, 31 sputum samples, and 1 fecal sample, from 86 patients with coronavirus disease 2019 (COVID-19) from Feb 20 to May 15, 2020. We performed parallel detection with nanopore-based genome amplification and sequencing (NAS) on the Oxford Nanopore Technologies (ONT) minION platform and routine reverse transcription quantitative polymerase chain reaction (RT-qPCR). In addition, 27 negative control samples were detected using the two methods. The sensitivity and specificity of NAS were evaluated and compared with those of RT-qPCR. RESULTS: The viral read number and reference genome coverage were both significantly different between the two groups of samples, and the latter was a better indicator for SARS-CoV-2 detection. Based on the reference genome coverage, NAS revealed both high sensitivity (96.5%) and specificity (100%) compared with RT-qPCR (80.2 and 96.3%, respectively), although the samples had been stored for half a year before the detection. The total time cost was less than 15 h, which was acceptable compared with that of RT-qPCR (∼2.5 h). In addition, the reference genome coverage of the viral reads was in line with the cycle threshold value of RT-qPCR, indicating that this number could also be used as an indicator of the viral load in a sample. The viral load in sputum might be related to the severity of the infection, particularly in patients within 4 weeks after onset of clinical manifestations, which could be used to evaluate the infection. CONCLUSION: Our results showed the high sensitivity and specificity of the NAS method for SARS-CoV-2 detection compared with RT-qPCR. The sequencing results were also used as an indicator of the viral load to display the viral dynamics during infection. This study proved the wide application prospect of nanopore sequencing detection for SARS-CoV-2 and may more knowledge about the clinical characteristics of COVID-19.

6.
Front Immunol ; 13:814806, 2022.
Article in English | PubMed | ID: covidwho-1809386

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread and poses a major threat to public health worldwide. The whole genome sequencing plays a crucial role in virus surveillance and evolutionary analysis. In this study, five genome sequences of SARS-CoV-2 were obtained from nasopharyngeal swab samples from Zhengzhou, China. Following RNA extraction and cDNA synthesis, multiplex PCR was performed with two primer pools to produce the overlapped amplicons of ~1,200 bp. The viral genomes were obtained with 96% coverage using nanopore sequencing. Forty-five missense nucleotide mutations were identified;out of these, 5 mutations located at Nsp2, Nsp3, Nsp14, and ORF10 genes occurred with a <0.1% frequency in the global dataset. On the basis of mutation profiles, five genomes were clustered into two sublineages (B.1.617.2 and AY.31) or subclades (21A and 21I). The phylogenetic analysis of viral genomes from several regions of China and Myanmar revealed that five patients had different viral transmission chains. Taken together, we established a nanopore sequencing platform for genetic surveillance of SARS-CoV-2 and identified the variants circulating in Zhengzhou during August 2021. Our study provided crucial support for government policymaking and prevention and control of COVID-19.

7.
IEEE J Biomed Health Inform ; Pp, 2022.
Article in English | PubMed | ID: covidwho-1806951

ABSTRACT

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (R0), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

8.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333692

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~80% asymptomatic or mild cases and ~5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. METHODS: We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. RESULTS: We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis -expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8 + T (T RM ) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in T RM cells trajectory from moderate to severe patients. CONCLUSIONS: CXCR6 from the 3p21 . 31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and T RM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.

10.
IEEE Journal on Selected Topics in Signal Processing ; 2022.
Article in English | Scopus | ID: covidwho-1731025

ABSTRACT

The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic is spreading rapidly over the world and its outbreak has affected different people in different ways, it is significant to study or predict the evolution of its epidemic trend. However, most of the studies focused solely on either classical epidemiological models or machine learning models for COVID-19 pandemic forecasting, which either suffer from the limitation of the generalization ability and scalability or the lack of surveillance data. In this work, we propose T-SIRGAN that integrates the strengths of the epidemiological theories and deep learning models to be able to represent complex epidemic processes and model the non-linear relationship for more accurate prediction of the growth of COVID-19. T-SIRGAN first adopts the SusceptibleInfectiousRecovered (SIR) model to generate epidemiological-based simulation data, which are then fed into a generative adversarial network (GAN) as adversarial examples for data augmentation. Then, Transformers are used to predict the future trends of COVID-19 based on the generated synthetic data. Extensive experiments on real-world datasets demonstrate the superiority of our method. We also discuss the effectiveness of vaccine based on the difference between the predicted and the reported number of COVID-19 cases. IEEE

11.
Dili Xuebao/Acta Geographica Sinica ; 77(2):426-442, 2022.
Article in Chinese | Scopus | ID: covidwho-1726805

ABSTRACT

The Chinese government has curbed the rapid transmission of COVID-19 through a population flow control rarely seen in history. What is the effect of population flow control on pandemic prevention and control? How does it affect China's population mobility and short-term population distribution? In this paper, an SEIR model of virus transmission dynamics is used to evaluate the effectiveness of the control measures, and mobile location data are employed to track the temporal and spatial changes of population mobility in China, in order to review the positive and negative effects of population flow control during the major outbreaks of COVID-19: (1) Population flow control has significantly stabilized the daily new infection, serving as an essential part of China's non-pharmacological intervention measures in response to major public emergencies of COVID-19. Population flow control postponed the arrival of the peak day of daily new infections in China by 1.9 times, and reduced the number of newly infected people on that day by 63.4%. In the selected 5 provinces, 5 cities in Hubei, and 6 cities outside Hubei, the peak days were postponed by 1.4-8 times, 5.6-16.7 times, and 2.3-7.2 times, respectively, and the number of newly infected people on that day was reduced by 56.9%-85.5%, 62.2%-89.2%, and 67.1%-86.2%, respectively. Therefore, population flow control bought valuable buffer time for the prevention and control of the pandemic, and greatly weakened the impact of concentrated transmissions on medical facilities. (2) Population flow control limited intercity population flow. From January to April 2020, the average daily population flow intensity in China decreased by 40.18% compared with the same period in 2019. In particular, the coming-back-to-work flow after the Spring Festival travel rush in 2020 (from January 25 to February 18) decreased by 66.4% on average. (3) Population flow control and people's fear of the pandemic greatly affected the Spring Festival travel rush in 2020, and the spatial and temporal and distribution of China's population was changed for a short period. This paper helps the understanding of the impact of the population flow control strategy introduced by the government on major public emergencies, as well as the influences of geographical characteristics upon on the population flow and distribution. © 2022, Science Press. All right reserved.

12.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-329107

ABSTRACT

Background: Immunity after SARS-CoV-2 infection or vaccination has been threatened by recently emerged SARS-CoV-2 variants. A systematic summary of the landscape of neutralizing antibodies against emerging variants is needed. Methods: We systematically searched PubMed, Embase, Web of Science, and 3 pre-print servers for studies that evaluated neutralizing antibodies titers induced by previous infection or vaccination against SARS-CoV-2 variants and comprehensively collected individual data. We calculated lineage-specific GMTs across different study participants and types of neutralization assays. Findings: We identified 56 studies, including 2,483 individuals and 8,590 neutralization tests, meeting the eligibility criteria. Compared with lineage B, we estimate a 1.5-fold (95% CI: 1.0-2.2) reduction in neutralization against the B.1.1.7, 8.7-fold (95% CI: 6.5-11.7) reduction against B.1.351 and 5.0-fold (95% CI: 4.0-6.2) reduction against P.1. The estimated neutralization reductions for B.1.351 compared to lineage B were 240.2-fold (95% CI: 124.0-465.6) reduction for non-replicating vector platform, 4.6-fold (95% CI: 4.0-5.2) reduction for RNA platform, and 1.6-fold (95% CI: 1.2-2.1) reduction for protein subunit platform. The neutralizing antibodies induced by administration of inactivated vaccines and mRNA vaccines against lineage P.1 were also remarkably reduced by an average of 5.9-fold (95% CI: 3.7-9.3) and 1.5-fold (95% CI: 1.2-1.9). Interpretation: Our findings indicate that the antibody response established by natural infection or vaccination might be able to effectively neutralize B.1.1.7, but neutralizing titers against B.1.351 and P.1 suffered large reductions. Standardized protocols for neutralization assays, as well as updating immune-based prevention and treatment, are needed. Funding: Chinese National Science Fund for Distinguished Young Scholars. Research in context: Evidence before this study: Several newly emerged SARS-CoV-2 variants have raised significant concerns globally, and there is concern that SARS-CoV-2 variants can evade immune responses that are based on the prototype strain. It is not known to what extent do emerging SARS-CoV-2 variants escape the immune response induced by previous infection or vaccination. However, existing studies of neutralizing potency against SARS-CoV-2 variants are based on limited numbers of samples and lack comparability between different laboratory methods. Furthermore, there are no studies providing whole picture of neutralizing antibodies induced by prior infections or vaccination against emerging variants. Therefore, we systematically reviewed and quantitively synthesized evidence on the degree to which antibodies from previous SARS-CoV-2 infection or vaccination effectively neutralize variants. Added value of this study: In this study, 56 studies, including 2,483 individuals and 8,590 neutralization tests, were identified. Antibodies from natural infection or vaccination are likely to effectively neutralize B.1.1.7, but neutralizing titers against B.1.351 and P.1 suffered large reductions. Lineage B.1.351 escaped natural-infection-mediated neutralization the most, with GMT of 79.2 (95% CI: 68.5-91.6), while neutralizing antibody titers against the B.1.1.7 variant were largely preserved (254.6, 95% CI: 214.1-302.8). Compared with lineage B, we estimate a 1.5-fold (95% CI: 1.0-2.2) reduction in neutralization against the B.1.1.7, 8.7-fold (95% CI: 6.5-11.7) reduction against B.1.351 and 5.0-fold (95% CI: 4.0-6.2) reduction against P.1. The neutralizing antibody response after vaccinating with non-replicating vector vaccines against lineage B.1.351 was worse than responses elicited by vaccines on other platforms, with levels lower than that of individuals who were previously infected. The neutralizing antibodies induced by administration of inactivated vaccines and mRNA vaccines against lineage P.1 were also remarkably reduced by an average of 5.9-fold (95% CI: 3.7-9.3) and 1.5-fold (95% CI: 1.2-1.9). Implications of all the available evidence: Our findings indicate that antibodies from natural infection of the parent lineage of SARS-CoV-2 or vaccination may be less able to neutralize some emerging variants, and antibody-based therapies may need to be updated. Furthermore, standardized protocols for neutralizing antibody testing against SARS-CoV-2 are needed to reduce lab-to-lab variations, thus facilitating comparability and interpretability across studies.

13.
IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 1450-1455, 2021.
Article in English | Web of Science | ID: covidwho-1699840

ABSTRACT

Deep learning methods have achieved great performances in face recognition. However, the performances of deep learning methods deteriorate in case of wearing a mask. Recently, due to the world-wide COVID-19 pandemic, masked face recognition attracts more attention. It is non-trivial and urgent to improve the performances in masked face recognition. In this work, a simple and effective data augmentation method, named MaskOut, is proposed. MaskOut replaces a random region below the nose of a face with a random mask template to mask out original face features. Our method is computing and memory efficient and convenient to combine with other methods. The experimental results show that the performances in masked face recognition are improved by a large margin with MaskOut. Besides, we construct a real-life masked face dataset, named MCPRL-Mask, to evaluate the performance of masked face recognition models.

14.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-326998

ABSTRACT

As the COVID-19 pandemic continues, the SARS-CoV-2 virus continues to rapidly mutate and change in ways that impact virulence, transmissibility, and immune evasion. Genome sequencing is a critical tool, as other biological techniques can be more costly, time-consuming, and difficult. However, the rapid and complex evolution of SARS-CoV-2 challenges conventional sequence analysis methods like phylogenetic analysis. The virus picks up and loses mutations independently in multiple subclades, often in novel or unexpected combinations, and, as for the newly emerged Omicron variant, sometimes with long explained branches. We propose interpretable deep sequence models trained by machine learning to complement conventional methods. We apply Transformer-based neural network models developed for natural language processing to analyze protein sequences. We add network layers to generate sample embeddings and sequence-wide attention to interpret models and visualize multiscale patterns. We demonstrate and validate our framework by modeling SARS-CoV-2 and coronavirus taxonomy. We then develop an interpretable predictive model of disease severity that integrates SARS-CoV-2 spike protein sequence and patient demographic variables, using publicly available data from the GISAID database. We also apply our model to Omicron. Based on knowledge prior to the availability of empirical data for Omicron, we predict: 1) reduced neutralization antibody activity (15-50 fold) greater than any previously characterized variant, varying between Omicron sublineages, and 2) reduced risk of severe disease (by 35-40%) relative to Delta. Both predictions are in accord with recent epidemiological and experimental data.

15.
Medical Journal of Wuhan University ; 43(1):19-23, 2022.
Article in Chinese | Scopus | ID: covidwho-1600040

ABSTRACT

Objective: To analyze the clinical characteristics and laboratory indices of 25 SARS‑CoV‑2 nucleic acid re⁃positive patients who reached the discharge standard after quarantined at hotels after 2 or 4 weeks. Methods: The clinical data of 25 re‑positive COVID⁃19 patients admitted to Dongfeng Hospital of Hubei University of Medicine from January to April 2020 were retrospectively analyzed at different time points (the first diagnosis, hospital discharge, and re⁃test positive after treatment). Results: Lymphocyte counts (LYM) were significantly different among each periods (all P<0.05). Compared with that at the first diagnosis, hsCRP level at re‑positive and discharge time was significantly reduced (both P<0.05), the Ct value of the SARS‑CoV‑2 nucleic acid test and WBC count were significantly increased (all P<0.05). The NEU%, LYM%, and PCT showed no statistical changes among all time points (all P>0.05). Compared with that at hospital discharge, better or none changes in lung CT images were found in the patients when they were found re⁃positives in nucleic acid test. Conclusion: In SARS‑CoV‑2 nucleic acid re‑positive patients, progressive increases of LYM and WBC counts, and progressive decrease of hs‑CRP were found. The clinical symptoms and laboratory examination indicators were better than before, we speculated that the re⁃occurrence of SARS‑CoV‑2 infection may have no pathogenic effect on patients, although its infectivity needs to be further investigated. Therefore, the disease control department need to continue to strengthen the follow‑up and prevention management of COVID‑19 discharged patients. © 2022, Editorial Board of Medical Journal of Wuhan University. All right reserved.

16.
2d Materials ; 9(1):8, 2022.
Article in English | Web of Science | ID: covidwho-1585203

ABSTRACT

Recently, the coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally with major impact on public health. Novel methods that enable fast and efficient detection of the virus and the associated biomarkers, such as SARS-CoV-2 antibodies, may provide alterative opportunities for early diagnosis, disease status monitoring, and the development of vaccines. Here, we report the fabrication of a functionalized MoS2-field effect transistor (FET) for sensitive and quantitative detection of antibodies against SARS-CoV-2 spike protein receptor binding domain (S-RBD) in vaccinated serum specimens. The device was modified by SARS-CoV-2 S-RBD fusion protein on the surface and enabled rapid detection of SARS-CoV-2 antibodies. In addition, an on-chip calibration method was established for quantitative measurement. Furthermore, this method was applied to measure the levels of S-RBD antibodies in serum specimens from vaccinated donors. The devices showed no response to negative control samples from individuals who did not receive vaccination, suggesting the high specificity of this method. This study illustrated the successful fabrication of S-RBD functionalized MoS2-FET with potential clinical applications to facilitate vaccine development and efficacy evaluation.

17.
Progress in Geography ; 40(7):1073-1085, 2021.
Article in Chinese | Scopus | ID: covidwho-1566882

ABSTRACT

The Chinese government has curbed the outbreak of COVID-19 through a population flow control rarely seen in history. The COVID-19 pandemic has greatly impacted the recreation industry. Using mobile location data, this study quantitatively analyzed the impact of the COVID-19 pandemic on population heat map in the leisure areas within the Third Ring Road of Beijing City on the Qingming Festival and Labor Day. The results showed that: 1) The COVID-19 pandemic significantly impacted population heat map in leisure areas in Beijing on holidays, and the population heat map values of the three types of leisure areas investigated in this study declined by 54.2% and 53.0% on the Qingming Festival and Labor Day in 2020 as compared to the 2019 values, respectively. To be specific, the population heat map values of famous scenery, shopping services, and hotel accommodation decreased by 53.6%, 57.5%, and 52.9% on the Qingming Festival, and by 48.5%, 52.0%, and 55.6% on Labor Day, respectively. 2) There were differences in the degree of the impact on population heat map in different types of areas in famous scenery. The impact on the three major segments of famous scenery can be ranked in ascending order as follows: temples and churches (41.7%, 50.3%), parks and squares (53.1%, 47.1%), and scenic spots (61.1%, 51.2%). Wilcoxon rank sum test showed that the hourly variation of population heat map in temples and churches was smaller, and the overall demand can be ranked in ascending order as follows: sightseeing, daily leisure, and religious activities. 3) The 2020 population heat map of the leisure areas within the Third Ring Road of Beijing City was significantly negatively and positively correlated with the population heat map before the pandemic and area of these leisure areas, respectively. This can be attributed to the risk perception of the leisure crowds and the spatial and environmental factors of the disease prevention and control measures. This study provides a scientific basis for assessing the impact of the COVID-19 pandemic on leisure forms in big cities of China. © 2021, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.

18.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; : 8563-8567, 2021.
Article in English | Web of Science | ID: covidwho-1532674

ABSTRACT

Research on automated diagnosis of Coronavirus Disease 2019 (COVID-19) has increased in recent months. SPGC COVID19 aims at classifying the grouped images of the same patient into COVID, Community Acquired Pneumonia(CAP) or normal. In this paper, we propose a novel ensemble learning framework to solve this problem. Moreover, adaptive boosting and dataset clustering algorithms are introduced to improve the classification performance. In our experiments, we demonstrate that our framework is superior to existing networks in terms of both accuracy and sensitivity.

19.
Kexue Tongbao/Chinese Science Bulletin ; 66(31):3925-3931, 2021.
Article in Chinese | Scopus | ID: covidwho-1523391

ABSTRACT

Left unmitigated, climate change poses a catastrophic risk to human health, demanding an urgent and concerted response from every country. The 2015 Lancet Commission on Health and Climate Change and The Lancet Countdown: Tracking Progress on Health and Climate Change have been initiated to map out the impacts of climate change and the necessary policy responses. To meet these challenges, Tsinghua University, partnering with the University College London and 17 Chinese and international institutions, has prepared the Chinese Lancet Countdown report, which has a national focus and builds on the work of the global Lancet Countdown: Tracking Progress on Health and Climate Change. Drawing on international methodologies and frameworks, this report aims to deepen the understanding of the links between public health and climate change at the national level and track them with 23 indicators. This work is part of the Lancet's Countdown broader efforts to develop regional expertise on this topic, and coincides with the launch of the Lancet Countdown Regional Centre in Asia, based at Tsinghua University. The data and results of this report are presented at the provincial level, where possible, to facilitate targeted response strategies for local decision-makers. Based on the data and findings of the 2020 Chinese Lancet Countdown report, five recommendations are proposed to key stakeholders in health and climate change in China: (1) Enhance inter-departmental cooperation. Climate change is a challenge that demands an integrated response from all sectors, urgently requiring substantial inter-departmental cooperation among health, environment, energy, economic, financial, and education authorities. (2) Strengthen health emergency preparedness. Knowledge and findings on current and future climate-related health threats still lack the required attention and should be fully integrated into the emergency preparedness and response system. (3) Support research and raise awareness. Additional financial support should be allocated to health and climate change research in China to enhance health system adaptation, mitigation measures, and their health benefits. At the same time, media and academia should be fully motivated to raise the public and politicians' awareness of this topic. (4) Increase climate change mitigation. Speeding up the phasing out of coal is necessary to be consistent with China's pledge to be carbon neutral by 2060 and to continue to reduce air pollution. Fossil fuel subsidies must also be phased out. (5) Ensure the recovery from COVID-19 to protect health now and in the future. China's efforts to recover from COVID-19 will shape public health for years to come. Climate change should be a priority in these interventions. © 2021, Science Press. All right reserved.

20.
2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2021 ; : 764-770, 2021.
Article in English | Scopus | ID: covidwho-1522558

ABSTRACT

According to Wikipedia Covid-19 Statistics, on May 17, 2021, more than a total of 163 million cases have been confirmed, with more than 3.38 million deaths attributed to Coronavirus. Yet, with such a dreadful number of cases, the world is still facing many challenges, such as scarce medical resources. A machine learning algorithm that utilizes neural networks to diagnose patients' positivity of Covid-19 can be helpful relief of the current medical resource shortage. It is not only time-saving assistance for the frontline physicians to process the diagnosis but also a potential remainder for individuals to pay careful attention to their health status. This paper proposed an application based on a deep two-dimensional parallel convolutional neural network model to classify patients' chest X-ray images. The input dataset consists of two folders: Covid19 Negative (∼1600 images) and Covid19 Positive (∼500 images), where each file from the two folders is a chest X-ray radiograph of one individual. The model is established using the Keras API (an Application Programming Interface in the library of Tensorflow) and is optimized to have the least validation loss after the training process. The final experimental results illustrate a validation accuracy of approximately 99% for the model in only 20 epochs. Furthermore, we have created a simple user interface to upload an image and initiate the prediction process. The interface tells the users their probability of catching the Covid, and it also applies Natural Language Processing (NLP) and Speech Synthesis Communication in a chat box for further interactions. © 2021 IEEE.

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