COVID-19 pandemic dynamics in India,the SARS-CoV-2 Delta variant and implications for vaccination

The Delta variant is a major SARS-CoV-2 variant of concern first identified in India. To better understand COVID-19 pandemic dynamics and Delta, we use multiple datasets and model-inference to reconstruct COVID-19 pandemic dynamics in India during March 2020–June 2021. We further use the large discrepancy in one- and two-dose vaccination coverage in India (53% versus 23% by end of October 2021) to examine the impact of vaccination and whether prior non-Delta infection can boost vaccine effectiveness (VE). We estimate that Delta escaped immunity in 34.6% (95% CI: 0–64.2%) of individuals with prior wild-type infection and was 57.0% (95% CI: 37.9–75.6%) more infectious than wild-type SARS-CoV-2. Models assuming higher VE among non-Delta infection recoverees, particularly after the first dose, generated more accurate predictions than those assuming no such increases (best-performing VE setting: 90/95% versus 30/67% baseline for the first/second dose). Counterfactual modelling indicates that high vaccination coverage for first vaccine dose in India combined with the boosting of VE among recoverees averted around 60% of infections during July–mid-October 2021. These findings provide support to prioritizing first-dose vaccination in regions with high underlying infection rates, given continued vaccine shortages and new variant emergence.     

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’ complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.     

Particulate Alum via Pickering Emulsion for an Enhanced COVID-19 Vaccine Adjuvant

For rapid response against the prevailing COVID-19 (coronavirus disease 19), it is a global imperative to exploit the immunogenicity of existing formulations for safe and efficient vaccines. As the most accessible adjuvant, aluminum hydroxide (alum) is still the sole employed adjuvant in most countries. However, alum tends to attach on the membrane rather than entering the dendritic cells (DCs), leading to the absence of intracellular transfer and process of the antigens, and thus limits T-cell-mediated immunity. To address this, alum is packed on the squalene/water interphase is packed, forming an alum-stabilized Pickering emulsion (PAPE). “Inheriting” from alum and squalene, PAPE demonstrates a good biosafety profile. Intriguingly, with the dense array of alum on the oil/water interphase, PAPE not only adsorbs large quantities of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) antigens, but also harbors a higher affinity for DC uptake, which provokes the uptake and cross-presentation of the delivered antigens. Compared with alum-treated groups, more than six times higher antigen-specific antibody titer and three-fold more IFN-γ-secreting T cells are induced, indicating the potent humoral and cellular immune activations. Collectively, the data suggest that PAPE may provide potential insights toward a safe and efficient adjuvant platform for the enhanced COVID-19 vaccinations.     

Inference of COVID-19 epidemiological distributions from Brazilian hospital data

Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreover, the mathematical modelling of the pandemic generally. We determine epidemiological distributions for patients hospitalized with COVID-19 using a large dataset (N = 21 000 − 157 000) from the Brazilian Sistema de Informação de Vigilância Epidemiológica da Gripe database. A joint Bayesian subnational model with partial pooling is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2 and 17.8 days across the different states, and a mean of 15.2 days for Brazil. We find strong evidence in favour of specific probability density function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalized lognormal for onset-to-hospital-admission. Our results show that epidemiological distributions have considerable geographical variation, and provide the first estimates of these distributions in a low and middle-income setting. At the subnational level, variation in COVID-19 outcome timings are found to be correlated with poverty, deprivation and segregation levels, and weaker correlation is observed for mean age, wealth and urbanicity.     

新型冠状病毒肺炎疫情数据可视化设计综述

目的分析、总结新型冠状病毒肺炎疫情可视化设计案例,为疫情数据可视化设计的不断完善提供参考。方法检索、分析国内外相关文献资料,对疫情期间各主要媒体所发布的各阶段疫情数据可视化作品进行收集、分析和总结。结果得出疫情可视化设计的用户分类、数据类型、特点、演化过程等信息。结论收集到疫情可视化设计方案针对大众关心的疫情信息作了形式多样的表达,并随着疫情发展不断调整改进。同时,需要提高设计方案的用户针对性,加强设计评价以提升可用性及用户体验。也需要完善人流、物流、信息流等数据的可视化设计,并且达到系统化、规范化、实时化。     

Automated contact tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.     

Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19

COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations’ governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper’s initial research shows that the countries with a long-term compulsory BCG vaccination system are less affected by COVID-19 than those without a BCG vaccination system. This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates. The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data. An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only, such as age groups affected, other diseases, and stringency index. The data of Spain, Portugal, and Germany have been taken for a case study of BCG impact analysis.     

Clinical Characteristics and Outcomes of Severe or Critical COVID-19 Patients Presenting No Respiratory Symptoms or Fever at Onset

It is difficult to identify suspected cases of atypical patients with coronavirus disease 2019 (COVID-19), and data on severe or critical patients are scanty. This retrospective study presents the clinical, laboratory, and radiological profiles, treatments, and outcomes of atypical COVID-19 patients without respiratory symptoms or fever at onset. The study examined ten atypical patients out of 909 severe or critical patients diagnosed with COVID-19 in Wuhan Union Hospital West Campus between 25 January 2020 and 10 February 2020. Data were obtained from the electronic medical records of severe or critical patients without respiratory symptoms or fever at onset. Outcomes were followed up to discharge or death. Among 943 COVID-19 patients, 909 (96.4%) were severe or critical type. Of the severe or critical patients, ten (1.1%) presented without respiratory symptoms or fever at admission. The median age of the ten participants was 63 years (interquartile range (IQR): 57–72), and seven participants were men. The median time from symptom onset to admission was 14 d (IQR: 7–20). Eight of the ten patients had chronic diseases. The patients had fatigue (n = 5), headache or dizziness (n = 4), diarrhea (n = 5), anorexia (n = 3), nausea or vomiting (n = 3), and eye discomfort (n = 1). Four patients were found to have lymphopenia. Imaging examination revealed that nine patients had bilateral pneumonia and one had unilateral pneumonia. Eventually, two patients died and eight were discharged. In the discharged patients, the median time from admission to discharge lasted 24 d (IQR: 13–43). In summary, some severe or critical COVID-19 patients were found to have no respiratory symptoms or fever at onset. All such atypical cases should be identified and quarantined as early as possible, since they tend to have a prolonged hospital stay or fatal outcomes. Chest computed tomography (CT) scan and nucleic acid detection should be performed immediately on close contacts of COVID-19 patients to screen out those with atypical infections, even if the contacts present without respiratory symptoms or fever at onset.     

Socio-economic determinants of mobility responses during the first wave of COVID-19 in Italy: from provinces to neighbourhoods

After more than 1 year into the COVID-19 pandemic, governments worldwide still face the challenge of adopting non-pharmaceutical interventions to mitigate the risks posed by the emergence of new SARS-CoV-2 variants and the lack of a worldwide equitable vaccine allocation. Thus, it becomes crucial to identify the drivers of mobility responses to mitigation efforts during different restriction regimes, for planning interventions that are both economically and socially sustainable while effective in controlling an outbreak. Here, using anonymous and privacy-enhanced cell phone data from Italy, we investigate the determinants of spatial variations of reductions in mobility and co-location in response to the adoption and the lift of restrictions, considering both provinces and city neighbourhoods. In large urban areas, our analysis uncovers the desertification of historic city centres, which persisted after the end of the lockdown. Such centre-periphery gradient was mainly associated with differences in educational attainment. At the province level, the local structure of the labour market mainly explained the variations in mobility responses, together with other demographic factors, such as the population’s age and sex composition. In the future, targeted interventions should take into account how the ability to comply with restrictions varies across geographical areas and socio-demographic groups.     

新型冠状病毒肺炎疫情视域下植物景观设计的治愈功能分析

思考新型冠状病毒肺炎疫情背景下健康社区景观的功能复位,探究植物景观设计中治愈性的主体功能本质与构筑方法,普及治愈性景观在非医疗场所空间的应用。通过梳理治愈性景观的相关概念与发展历史,分析景观在治愈性功能上的本质与特征,并从物态性、时空性、通感性3个层面对景观的治愈性构建方式进行设计方法上的阐述。相较于传统药物治疗与心理治疗,植物景观设计的治愈性功能是依靠自然的非侵入性疗法,通过五感沉浸与行为互动来达到身心健康的目标,对于走出新型冠状病毒肺炎疫情阴霾,恢复积极乐观心态有着良好的促进作用。     

The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.     

Rethinking the history of plague in the time of COVID-19

We are currently experiencing one of the most disruptive pandemics in modern history. The outbreak of COVID-19 that was first recorded in Wuhan, China and quickly spread across the globe has resulted in nearly 5 million confirmed cases to date and more than 300,000 deaths. Where we stand now, it is still uncertain how many it will infect or kill worldwide, how long it will continue, and when—if ever—life will return to normal. What we know for sure is that this is a pivotal moment and that we are experiencing a historic event that will transform our societies both profoundly and irreversibly. As we wade into this new age of pandemics, it is critical to rethink how we write the history of pandemics. With a conviction that the past helps us to understand the present and that the present should help us to rethink the past, I turn to the legacy of past plagues. In this essay, I take stock of the lasting legacies of past plagues because they continue to shape the way we think about new pandemics. In particular, I address persistent problems, such as European exceptionalism, triumphalism, and epidemiological Orientalism, that are not only ubiquitous in plague studies, but also staples of public opinion about pandemics, past and present.     

Experimental Treatment with Favipiravir for COVID-19: An Open-Label Control Study

There is currently an outbreak of respiratory disease caused by a novel coronavirus. The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it causes has been named coronavirus disease 2019 (COVID-19). More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was 1%–2%. No specific treatment has been reported. Herein, we examined the effects of favipiravir (FPV) versus lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million international unit (IU) twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million IU twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance median time was found for the FPV arm versus the control arm (4 d (interquartile range (IQR): 2.5–9) versus 11 d (IQR: 8–13), P < 0.001). The FPV arm also showed significant improvement in chest CT compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest CT. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.     

On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning

Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights from some of the top layers. We also develop an ensemble model where the predictions from all chosen pre-trained models are combined to generate a single output. The models are evaluated through 5-fold cross validation using two publicly available data repositories containing healthy and infected (both COVID-19 and other pneumonia) chest X-ray images. We also leverage two different visualization techniques to observe how efficiently the models extract important features related to the detection of COVID- 19 patients. The models show high degree of accuracy, precision, and sensitivity. We believe that the models will aid medical professionals with improved and faster patient screening and pave a way to further COVID-19 research.     

Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it is helpful to enhance the working productivity of radiologists. It could take the curve down with the downright contribution of radiologists, rapid detection of COVID-19, and to overcome this current pandemic with the proven efficacy.     

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.