A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places

With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic,health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human lives.The enforcement of social distancing at work environments and public areas is one of these obligatory precautions.Crowd management is one of the effective measures for social distancing.By reducing the social contacts of individuals,the spread of the disease will be immensely reduced.In this paper,a model for crowd counting in public places of high and low densities is proposed.The model works under various scene conditions and with no prior knowledge.A Deep CNN model(DCNN)is built based on convolutional neural network(CNN)structure with small kernel size and two fronts.To increase the efficiency of the model,a convolutional neural network(CNN)as the front-end and a multi-column layer with Dilated Convolution as the back-end were chosen.Also,the proposed method accepts images of arbitrary sizes/scales as inputs from different cameras.To evaluate the proposed model,a dataset was created from images of Saudi people with traditional and non-traditional Saudi outfits.The model was also trained and tested on some existing datasets.Compared to current counting methods,the results show that the proposed model has significantly improved efficiency and reduced the error rate.We achieve the lowest MAE by 67%,32%.and 15.63%and lowest MSE by around 47%,15%and 8.1%than M-CNN,Cascaded-MTL,and CSRNet respectively.

COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)

Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and treatment.As RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for diagnosis.Deep learning has a key role to play in the field of medical imaging.The most important issue in this area is the choice of key features.Here,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify COVID-19.Initially,this method was used to prepare a database of three classes:Pneumonia,COVID19,and Healthy.The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach.In the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer learning.The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach.For each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach.Later,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)approach.The final fused vector was finally classified using the extreme machine classifier.The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.

A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,China.After that,it quickly spread throughout the world and became a disease.It has significantly impacted our everyday lives,the national and international economies,and public health.However,early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system.Clinical radiologists primarily use chest X-rays,and computerized tomography(CT)scans to test for pneumonia infection.We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study.We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization.We begin by extracting standard features such as discrete wavelet transforms(DWT),discrete cosine transforms(DCT),and dominant rotated local binary patterns(DRLBP).In addition,we extracted Shanon Entropy and Kurtosis features.In the following step,a Max-Covariance-based maximization approach for feature fusion is proposed.The fused features are optimized in the preliminary phase using Particle Swarm Optimization(PSO)and the ELM fitness function.For final prediction,PSO is used to obtain robust features,which are then implanted in a Support Vector Data Description(SVDD)classifier.The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients.These images are from the Radiopaedia website.For the proposed scheme,the fusion and selection process accuracy is 88.6%and 93.1%,respectively.A detailed analysis is conducted,which supports the proposed system efficiency.

COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

Social networking sites in the most modernized world are flooded with large data volumes.Extracting the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they write.The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale.In a very short period of time,tweets indicate unpredicted increase of coronavirus.They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society.The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and sparsity.In this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short texts.The proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment analysis.The tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or negative.The quality of the proposed model is validated by applying it to a popular product and a widespread topic.The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

Impact of COVID19 on Routine Immunization: A Cross-Sectional Study in Senegal

Introduction: The COVID 19 pandemic has prompted the world to implement drastic prevention methods based on limiting population movements that have an impact on public health policies such as vaccination. The objective of this work was to evaluate the impact of these prevention measures on routine vaccination in hospitals since the advent of the pandemic in Senegal. Methodology: This is a retrospective cross-sectional study carried out in August 2020 in the vaccination unit of the Abass NDAO hospital centre. We compared data from the vaccination unit during the period from March to August of the last three years (2018, 2019 and 2020). The parameter studied was the number of vaccine doses administered for the different periods according to the expanded programme of immunization. Results: For the vaccines administered in the sixth week in April, the number of doses was 36 in 2018, 29 in 2019 and 15 in 2020, i.e. a 50% drop compared to March. In July the number of doses administered was 40 in 2018, 35 in 2019 and 15 in 2020, a reduction of 42% compared to 2019. Conclusion: Measures to fight this pandemic should not make us forget routine vaccination, especially in our resource-constrained countries. It is essential to continue vaccination for children and to identify children who have missed vaccine doses for catch-up.

外语类在线课程思政的建构及路径研究——以Covid19为例

以Covid 19为背景,以课程思政为主线,结合专业教学实践,围绕外语类在线课程思政培养什么人,为谁培养人、怎么培养人以及如何评价等问题展开深入研究,通过深度挖掘专业课程中的思政资源,对疫情常态化背景下外语类在线课程思政的建构及路径研究进行了详细阐述,以期提升全员化、全过程、全方位“三全育人”的实效性,充分发挥课程思政立德树人、铸魂育人的关键性作用.

Mesenchymal stem cells therapy and COVID19:a narrative review

At the end of 2019,a new type of coronavirus pneumonia broke out in China and swept the world,and the World Health Organization named it COVID-19.Mesenchymal stem cells(MSCs)are cells with high differentiation potential and their ability to regenerate.The therapeutic effects of mesenchymal stem cells are mediated by the secretion of various agents,including conventional secretory proteins such as cytokines and growth factors,as well as exosomes.MSC injection in patients with COVID-19 was found to have potent anti-inflammatory activity of mesenchymal stem cells.Such processes were evident from numerous beneficial outcomes,including an increase in peripheral lymphocyte counts,a decrease in C-reactive protein,and a decrease in active cytokine-secreting immune cells in the circulating blood for 3 to 6 days.It is suggested that due to the favorable results of studies in this field,more studies be done on this treatment method.

Novel SARS-CoV-2 outbreak and COVID19 disease;a systemic review on the global pandemic

Since the beginning of the 21st century,several viral outbreaks have threatened humankind and posed a new challenge to the modern healthcare system.The recent outbreak in Wuhan(December 2019),China,represents a beta coronavirus classified as novel Severe Acute Respiratory Syndrome Corona Virus-2(SARS-CoV-2)which belongs to the Coronaviridae family.Novel SARS-CoV-2 represents a significant similarity with previous coronaviruses such as SARS-CoV in 2002,China and MERS-CoV in 2015,Middle East.However,preliminary research investigations have shown the novel SARS-CoV-2 evolved with several mutations and developed the capacity to cross the species,i.e.,animal to human.The initial findings have shown that spike proteins are vital molecules target hACE2 receptor for its attachment and entry into cells.After successful entry virus primarily focuses on respiratory airway cell lines and triggers a massive immune response leading to mucus generation.In severe conditions,the virus is capable of forcing viral pneumonia leading to the collapse of the respiratory system,i.e.,COVID19.So far,there is a lack of immunity against the virus in humans.At the same in the absence of therapeutic interventions,many countries experienced high mortality,such as the United States,European countries,i.e.,Italy,Spain,France,and the United Kingdom.The vaccine development is underway and experiencing challenges,as many reports demonstrated genetic variations in viral genome and proteins as well.The present study provides a complete comprehensive overview of the novel SARS-CoV-2 outbreak,human transmission,and global spread.

Clinical-DNA Correlates of Anxiety in Patients with Ehlers-Danlos Syndrome

Introduction: Anxiety disorders have a lifetime prevalence of 34% with a similar level of heritability (31%) yet lack objective markers that could differentiate patients with underlying conditions. Up to 60%-70% of patients with Ehlers-Danlos syndrome have anxiety that meets criteria of generalized anxiety disorder, their clinical-DNA findings worth examining as biomarkers for patients with generalized anxiety. Method: Of the 1899 patients diagnosed with Ehlers-Danlos syndrome, 1261 were systematically evaluated for 80 history and 40 physical findings and separated into 826 who reported anxiety and 435 who did not. The most consistently reported or management-directing 60 of these clinical findings were, along with variations in genes relevant to these disorders, examined for association with anxiety. Results: Among the 30 anxiety- associated findings judged most predictive of Ehlers-Danlos syndrome in patients with anxiety were expected ones of adrenergic stimulation (difficulty concentrating-87% frequency and 1.26 anxiety/no anxiety ratio;chronic fatigue-84%, 1.17;sleep issues 69%, 1.52 that are criteria for generalized anxiety disorder) or of cholinergic suppression (e.g., frequent nausea 64%, 1.26). Less associated but more discriminating for underlying disease were those reflective of neuromuscular impact (e.g., chronic daily headaches 76%, 1.12);hypermobility (e.g., awareness of flexibility 72%, 1.03), or skin changes (e.g., elasticity around jaw 71%, 1.06). Anxiety-associated DNA variants included 54 of 88 in collagen type I/V/VII/IX genes, 14 of 16 in sodium channel SCN9A/10A/ 11A genes, 59 of 85 in POLG/MT-DNA genes, and 21 of 28 in profilaggrin- FLG genes that respectively impacted tissue laxity, sensory neural, autonomic-mitochondrial, and autonomic-inflammatory functions. Conclusion: Analysis of pathogenetic mechanisms in Ehlers-Danlos syndrome selected some 50 clinical-DNA findings useful for its diagnosis in those with generalized anxiety disorders.

从少阳三焦枢机探讨国医大师王庆国教授运用柴胡桂枝汤辨治长新冠思路

长新冠是指患者新冠感染“康复”过程中出现的多种躯体和精神症状,临床持续时间较长。王庆国教授从枢机理论出发,认为正虚邪恋,枢机不利是长新冠的核心病机;以“和调枢机、通平致和”为总体治疗原则;据证选方,临床善用柴胡桂枝汤为主方化裁,再据证/症灵活加减,标本兼顾,病症同治,临床疗效显著。

Cluster sampling methodology to evaluate immunization coverage

BACKGROUND Immunization is a key component of primary health care and an indisputable human right.Vaccines are critical to the prevention and control of infectious disease outbreaks.The coronavirus disease 2019(COVID-19)pandemic and associated disruptions over the past two years have strained the health systems,with many children missing out on essential childhood vaccines.AIM To evaluate the immunization coverage among 12-23-month-old children in the rural areas of Community Health Centre(CHC)Dighal and to determine the factors influencing the existing immunization coverage.METHODS A coverage evaluation survey was conducted according to the 30-cluster sampling technique,which is the standard methodology for such surveys devised by World Health Organization.A total of 300 children aged 12-23 months were included,whose immunization details were noted from their immunization cards.RESULTS Full immunization rate was noted in 86.7%of the children,with partial and non-immunized children accounting for 9%and 4.3%respectively.The full immunization dropout rate was 4.2%.The common reasons for partial or non-immunization were family problem including illness of mother,vaccine not being available and child being ill.Place of birth(P=0.014)and availability of immunization card(P<0.001)were significant predictors of the immunization status.Since the study was conducted in 2020/2021,health services were disrupted due to the COVID-19 lockdown.CONCLUSION Due to the coverage being higher than the national average,it was concluded that the immunization coverage was optimal and not affected by the COVID-19 pandemic.

Noninvasive Respiratory Strategies in Patients with Acute Hypoxemic Respiratory Failure and COVID-19 in Gabon: A Prospective Observational Study

Importance: The best respiratory support technique to reduce intubation and mortality in patients with respiratory failure due to COVID-19 is controversial. Objective: To determine the respiratory support technique that could reduce the need for tracheal intubation and mortality in patients with respiratory failure due to COVID-19 admitted to intensive care units (ICUs) of Military’s Hospital (HIAOBO) in Gabon. Design, Setting, and Participants-Methodology: Prospective observational study over 10 months (January 2021-October 2021). We included patients admitted to intensive care for SARS Cov2 pneumonia who had benefited from available ventilatory support: high concentration face mask, High Flow Nasal cannula (HFNC), NIV (Non Invasive Ventilation), Continuous Positive Airway Pressure (CPAP). The choice was guided by the clinical condition, and the choice of the prescribing physicians. Recourse to mechanical ventilation was decided when faced with a Glasgow score of less than 13, an SpO2/FiO2 ratio ≤ 300, a FR ≥ 35/min, the impossibility of drainage of secretions. Main Outcomes and Measures: The primary outcome was the proportion of patients requiring intubation. The secondary outcomes were mortality in ICU. Results: The sample included 97 patients, the average age was55.6 years, hypertension was the main comorbidity (51.1%). Mean respiratory rate (RR) was 30.8 cycles/min, admission SpO2 was 83%, respiratory alkalosis was present in 63% of patients, mean CT involvement was 51%.Respiratory support was NIV (56.7%), CPAP (21.65%), high concentration face mask (18.55%). Sixteen percent (16%) of patients were intubated, 93% of them following failure of NIV. Mortality was 30%, mechanical ventilation was an independent risk factor for mortality. Conclusions: Non Invasive Ventilation, CPAP, and high-concentration face mask were frequently used in patients with COVID-related acute respiratory failure. The CPAP has reduced the need for intubation. Mechanical ventilation is a risk factor for death.

新型冠状病毒肺炎疫情前后急性呼吸道感染儿童支原体、衣原体和常见病毒谱分析

背景目前尚不清楚新型冠状病毒肺炎(COVID⁃19)流行暴发以来急性呼吸道感染(ARTI)的病原谱是否发生了重要变化。目的基于深圳地区COVID⁃19疫情前后ARTI住院患儿的病原学特征和病原谱的变化,为本地区ARTI的临床诊治及防控提供依据。设计横断面调查。方法纳入深圳市儿童医院2019年9月2日至2021年2月28日非新生儿临床诊断ARTI住院后首次送检的咽拭子标本。采用多重PCR与毛细电泳联用技术检测13种常见病原体。以2020年3月至2021年2月的数据进行不同季节的ARTI病原谱比较,春季为3~5月,夏季为~8月,秋季为~11月,冬季为12月至次年2月。以2019年9月2日至2020年1月24日的数据作为COVID⁃19疫情前、2020年9月2日至2021年1月24日的数据作为COVID⁃19疫情后进行比较。主要结局指标支原体、衣原体和常见病毒谱。结果12022例ARTI住院患儿的咽拭子标本进入分析,病原体阳性(至少检出1种)7589例(63.13%),其中鼻病毒检出率最高(24.83%),其次是呼吸道合胞病毒(14.12%)、副流感病毒(8.33%)、偏肺病毒(7.01%)、肺炎支原体(5.37%)。混合感染(检出≥2种病原体)1329例(11.05%),以鼻病毒+肺炎支原体最常见。①秋季病原体检出率最高(66.99%,1924/2874),以呼吸道合胞病毒(25.89%)和鼻病毒(24.01%)最高;春季病原体检出率最低(16.20%,132/815),以鼻病毒(5.03%)和肺炎支原体(3.80%)最高。②6月龄至6岁患儿病原体检出率高于总体检出率,随年龄增长,呼吸道合胞病毒和副流感病毒检出率呈下降趋势,肺炎支原体检出率呈上升趋势。不同性别患儿病原体检出率差异无统计学意义。③COVID⁃19疫情前病原体检出率为78.57%(2592/3299),以鼻病毒(36.37%)、肺炎支原体(15.40%)、呼吸道合胞病毒(10.03%)、腺病毒(9.03%)为主,混合感染764例(23.16%);疫情后同期病原体检出率为65.89%(3500/5312),以鼻病毒(23.78%)、呼吸道合胞病毒(14.74%)、偏肺病毒(12.65%)、副流感病毒(12.12%)为主,混合感染459例(8.64%)。结论COVID⁃19疫情后ARTI病原谱发生了明显变化。偏肺病毒、副流感病毒、呼吸道合胞病毒、博卡病毒检出率明显升高;混合感染、支原体、衣原体、甲型流感H1N1病毒、季节性H3N2病毒、乙型流感病毒、鼻病毒、腺病毒检出率明显下降。

一类潜伏期有传染性的传染病模型动力学分析

建立了一类潜伏期具备传染性的传染病传播模型,根据疾病传播规律求解了疾病消失和持续生存的阈值——基本再生数.对系统的稳定性进行了讨论,得到了系统稳定性条件.最后,以COVID⁃19为例,解释了各种举措在疾病控制中的作用,并对疫情传播扩散做了探讨和预测.

抗击新型冠状病毒肺炎的医务人员身心体验状况调查

目的研究在新型冠状病毒肺炎(新冠肺炎)防控中医务人员的身心体验,为保障医疗人力资源提供参考。方法采用质性研究中的现象学研究法,对2020年1月28日至2020年1月31日来自深圳市第三人民医院及西安交通大学第一、二附属医院和西安市第四军医大学唐都医院一线接触疫情工作的16名医务人员进行深入访谈,了解医务人员对新冠肺炎防控中的身心感受,并对访谈资料进行归纳与分析。结果医务人员在新冠肺炎防控中的身心感受主要提取为五项主题:①震惊及恐惧心理;②知识缺乏,需要正确认知新冠肺炎;③医务人员需要正确面对,做好自身防护;④保家卫国,刻不容缓;⑤人民群众需要有效的防护及传染病知识宣传与重视度教育。结论医务人员在新冠肺炎的防控中身心感受复杂多样,需要各级领导主管部门高度重视,在保障防护用品及设施的供给外,确保足够的医疗人力资源梯队,以应对国际关注的突发公共卫生事件(PHEIC)。

新冠肺炎临床治疗的探索与启示

自新冠肺炎爆发以来,中医药救治工作普遍开展,取得了确切疗效,已成为医界与广大群众的共识。本文从大量武汉临床验案中选择报导2例,目的在于探讨中医药取得良好疗效的原因之所在,进而分析在治疗过程中所出现的各种问题及应对措施,从而提出作者以中医学三因制宜、辨证论治特色进行临床治疗的观点。本文强调在西医学没有特效治疗药物的前提下,必须坚持中医为主,西医支持的治法,尤其慎用静脉输液以防加重湿邪。

COVID-19疫情常态化防控时期儿童发热门诊的设置与实践

目的:探讨COVID⁃19疫情常态化防控时期开设儿童发热门诊的价值和意义。方法:因发热和/或呼吸道症状就诊的学生急剧增加,我院于5月19日开设儿童发热门诊,将患者分为:5月19日以前组和5月19日以后组。2020年3月30日至5月31日期间,共1703例患者纳入本研究;5月19日以前组449例,5月19日以后组1254例。结果:5月19日以前组和5月19日以后组的人均就诊费用为747.7±298.8元和355.6±294.2元(P<0.001)。随着低年级学生和幼儿园的开学,因发热和/或呼吸道症状就诊的学生人数呈暴增趋势,且年龄呈下降趋势。结论:开设儿童发热门诊不但可以为学生群体提供快捷、高效的就医途径,还可以针对学生群体的特殊性,给予个性化的筛查和诊疗,降低医疗费用。

新冠肺炎疫情网络舆情的特点及其治理启示

移动互联网时代,每逢有重大公共卫生事件发生,必然伴随有网络舆情,有效治理网络舆情是重大公共卫生事件处置的重要内容。新冠肺炎疫情防控是新中国成立以来一场前所未有的战役,网络舆情热度、烈度、扩散度、敏感度和破坏度也远超以往。在事件本身及利益相关者、政府、媒体、网民以及网络环境等因素的综合作用下,新冠肺炎疫情网络舆情呈现出井喷式发展、病毒式传播、舆论场对冲激烈、次生谣言层出不穷等特点。新冠肺炎疫情网络舆情启示我们,必须要将重大公共卫生事件网络舆情治理纳入国家治理层面考虑,采用多元主体协同治理和差异化引导策略。

Challenges of COVID-19 prevention and control: A narrative review

This narrative review aims to highlight some of the factors contributing to challenges faced by many countries in controlling the spread of COVID-19 pandemic that continues to rage around the world,especially after stoppage of official prevention and control activities.A literature search was conducted on PubMed,and Google using search terms“COVID-19”,“challenges”,“prevention”,and“control”in different combinations.COVID-19 prevention and control challenges are related to health-system,vaccines,administration,and society culture.Controlling the spread of COVID-19 necessitates cooperation between community leaders,healthcare professionals,religious leaders,and the public.