Trends In Spread Research Articles

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.

The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R2. Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS

The novel Coronavirus (COVID-19) has significantly affected millions of people around the world since the first notification until nowadays. The rapid spread of the virus has dramatically increased the workload of healthcare systems in many countries. Therefore, the need for efficient use of the healthcare system leads researchers to forecast the trend of virus spread. For this purpose, Machine Learning (ML) and Artificial Intelligence (AI) applications have intensively used to struggle against the coronavirus outbreak. In this study, Temporal Convolutional Network (TCN) is applied for modeling the cumulative confirmed COVID-19 cases and forecasting the spread of it in various European countries using time series data. It is also presented that numerical examples for comparing performances of TCN against Long-Short Term Memory (LSTM) and Gates Recurrent Units(GRU) in terms of computation time, root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE). Simulation results indicate that the Temporal Convolutional Networks used in this manuscript performs better than other models for forecasting the cumulative confirmed COVID-19 cases.

Using Network Analysis and Machine Learning to Identify Virus Spread Trends in COVID-19

The outbreak of Coronavirus Disease 2019 (COVID-19) has infected and killed millions of people globally, resulting in a pandemic with enormous global impact. This disease affects the respiratory system, and the viral agent that causes it, SARS-CoV-2, spreads through droplets of saliva, as well as through coughing and sneezing. As an extremely transmissible viral infection, COVID-19 is causing significant damage to the economies of both developed and lower- and middle-income countries because of its direct impact on the health of citizens and the containment measures taken to curtail the virus. Methods to reduce or control the spread of the virus and protect the global population are needed to avoid further deaths, long-term health issues, and prolonged economic impact. The most effective approach to reduce viral spread and avoid a substantial collapse of the health system, in the absence of vaccines, is nonpharmaceutical interventions (NPI) such as enforcing social containment restrictions, monitoring overall population mobility, implementing widespread viral testing, and increasing hygiene measures. Our approach consists of combining network analytics with machine learning models by using a combination of anonymized health and telecommunications data to better understand the correlation between population movements and virus spread. This approach, called location network analysis (LNA), allows for accurate prediction of possible new outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health metrics and can be used either to intensify social containment policies to avoid further spread or to ease them to reopen the economy. LNA can also help to retrospectively evaluate the effectiveness of policy responses to COVID-19.

ANALYSIS OF VARIATIONS IN TREND IN NOVEL CORONAVIRUS DISEASE IN SOUTHERN TAMIL NADU, INDIA

Corona Virus Disease (COVID) is the newly identified communicable disease which originally found in China first and its spread was found all over the world. The spread made a huge impact among people around the world. In this study, the variation of trends in novel COVID spread is determined in the southern region of Tamilnadu state of India. Tamilnadu ranks in the seventh position in the total population among the 29 states in India. This can be one of the reasons for the spread of the disease. In this study, the trend patterns in the spread of disease are determined from May to November for the year 2020 in southern districts of Tamilnadu. The results of the CUSUM test indicated that all the districts had a statistically significant step jump in June. The Mann-Kendall test indicated that there is an increasing trend in June in all the districts and decreasing trend in October and November. From the results, it is suggested that the first phase vaccination can be implemented in Madurai, Virudhunagar, Kanyakumari and Thoothukudi districts.

Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena

The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.

Mathematical modelling for decision making of lockdown during COVID-19.

Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.

Europe's Farm to Fork Strategy and Its Commitment to Biotechnology and Organic Farming: Conflicting or Complementary Goals?

The European Commission's Farm to Fork (F2F) strategy, under the European Green Deal, acknowledges that innovative techniques, including biotechnology, may play a role in increasing sustainability. At the same time, organic farming will be promoted, and at least 25% of the EU's agricultural land shall be under organic farming by 2030. How can both biotechnology and organic farming be developed and promoted simultaneously to contribute to achieving the Sustainable Development Goals (SDGs)? We illustrate that achieving the SDGs benefits from the inclusion of recent innovations in biotechnology in organic farming. This requires a change in the law. Otherwise, the planned increase of organic production in the F2F strategy may result in less sustainable, not more sustainable, food systems.

An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres

Various studies have provided a wide variety of mathematical and statistical models for early epidemic prediction of the COVID-19 outbreaks in Mainland China and other epicentres worldwide. In this paper, we present an integrated modelling framework, which incorporates typical exponential growth models, dynamic systems of compartmental models and statistical approaches, to depict the trends of COVID-19 spreading in 33 most heavily suffering countries. The dynamic system of SIR-X plays the main role for estimation and prediction of the epidemic trajectories showing the effectiveness of containment measures, while the other modelling approaches help determine the infectious period and the basic reproduction number. The modelling framework has reproduced the subexponential scaling law in the growth of confirmed cases and adequate fitting of empirical time-series data has facilitated the efficient forecast of the peak in the case counts of asymptomatic or unidentified infected individuals, the plateau that indicates the saturation at the end of the epidemic growth, as well as the number of daily positive cases for an extended period.

Tracking COVID-19 burden in India: A review using SMAART RAPID tracker.

India has seen a rapid rise in COVID-19 cases. Examine spatiotemporal variation of COVID-19 burden Tracker across Indian states and union territories using SMAART RAPID Tracker. We used SMAART RAPID Tracker to visually display COVID-19 spread in space and time across various states and UTs of India. Data gathered from publicly available government information sources. Data analysis on COVID-19 conducted from March 1 2020 to October 1 2020. Variables recorded include COVID-19 cases and fatality, 7-day average change, recovery rate, labs and tests. Spatial and temporal trends of COVID-19 spread across Indian states and UTs is presented. The total number of COVID-19 cases were 63, 12,584 and total fatality was 86,821 (October 1 2020). More than 85,000 new cases of COVID-19 were reported. There were 1,867 total COVID-19 labs throughout India. More than half of them were Government labs. The total number of COVID-19 tests was 76,717,728 and total recovered COVID-19 cases was 5,273,201. Results show an overall decline in the 7-day average change of new COVID-19 cases and new COVID-19 fatality. States such as Maharashtra, Chandigarh, Puducherry, Goa, Karnataka and Andhra Pradesh continue to have high COVID-19 infectivity rate. Findings highlight need for both national guidelines combined with state specific recommendations to help manage the spread of COVD-19. The heterogeneity represented in India in terms of its geography and various population groups highlight the need of state specific approach to monitor and combat the ongoing pandemic. This would further facilitate the tailored approach for each state to mitigate and contain the spread of the disease.

Heat Stress Indicators in CMIP6: Estimating Future Trends and Exceedances of Impact‐Relevant Thresholds

AbstractGlobal warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators (HSIs) has been developed to measure the associated impacts on human health. Here we calculate eight HSIs for global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We compare their future trends as function of global mean temperature, with particular focus on highly populated regions. All analyzed HSIs increase significantly (p < 0.01) in all considered regions. Moreover, the different HSIs reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Trends change considerably when normalizing the HSIs by accounting for the different scales on which they are defined, but the large spread and strong trends remain. Consistently, exceedances of impact‐relevant thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected HSIs. The indicators with the highest exceedance rates vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and the definition of threshold levels. These results highlight the importance of choosing indicators and thresholds that are appropriate for the respective impact under consideration. Additionally, further validation of HSIs regarding their capability to quantify heat impacts on human health on regional‐to‐global scales would be of great value for assessing global impacts of future heat stress more reliably.

COVID-19 outbreak investigation in a quarantine facility: is SARS-COV-2 exhibiting lesser known routes of spread?

Background: An institutional quarantine facility in India reported 5 cases in 9 days. The latter 2 cases incited a suspicion of internal outbreak as they were residing in a different room than the index cases and onset of symptoms was after the expiry of quarantine period. Subsequently, 75 more cases tested positive from this facility.Methods: A retrospective-prospective study was conducted. Data regarding the travel history, health-events thereafter, daily activities, layout of the facility and standard procedures followed in the facility was collected. The epidemiological pattern of presentation and evolution of cases was studied.Results: The outbreak pattern and investigation revealed a common bathroom as the cause of intra-floor spread. Inter-floor spread was also observed and 93.75% cases were detected from rooms using their floor bathrooms located vertically below one another and interconnected by plumbing systems. SAR for room contacts of index cases was 90% and SAR was 82.14% for bathroom contacts of index cases. The positivity rate amongst individuals assigned bathroom number 1 on the floors vertically located below the top floor was 64.10%.Conclusions: The investigation and results show local spread of SARS-CoV-2 through usage of common bathroom facility. An interesting trend of inter-floor spread with concentration of cases only in the rooms dedicated to vertically located bathrooms was found which indicates spread through waste-water plumbing system, a phenomenon previously observed in SARS-CoV in 2003. Single floor quarantine facilities are recommended with lesser dependency on common bathrooms.

Ecological risk assessment of coal mine area based on “source-sink” landscape theory – A case study of Pingshuo mining area

Coal mining play a fundamental role in promoting economic and social progress. However, intensive mining operations have brought enormous ecological risks, such as landform destruction, vegetation degradation and biodiversity loss. Based on the “source-sink” landscape and ecological risk assessment theories, this study proposes a new methodology to comprehensively analyze the mechanism of ecological risk and makes quantitative evaluation taking Pingshuo coal mine area as an example from 2000 to 2017. The results prove that the “source-sink” landscape theory is more consistent with the nature of ecological process to distinguish the ecological risk in mining area. Within the study period, the main risk source of mining areas is the environmental disturbance brought by mining production with a spreading trend, while the pollution risk caused by residents’ life is relatively low but the scope is large. The ecological niche of risk sink (a place where ecological risks disappear) in the study area improved as a whole, the ecological vulnerability and exposure coefficient of risk sink shows a decreasing trend from core mine area to outside, and the trend was not obvious at the end stage, testifying the protective effect of environmental governance. The striking increase areas of comprehensive ecological risk are mostly concentrated in the surroundings of mining production expansion, testifying mining practice has a significant adverse impact on the contiguous natural ecosystem. Any kind of pressure from coal mine operation, residents’ production and natural erosion in mining area should not be ignored, and the high risk areas are markedly clustered around the risk sources.

Whole Genome Sequencing and Antimicrobial Resistance of Staphylococcus aureus from Surgical Site Infections in Ghana.

Staphylococcus aureus (S. aureus) is a common cause of surgical site infections (SSIs) globally. Data on the occurrence of methicillin-susceptible S. aureus (MSSA) as well as methicillin-resistant S. aureus (MRSA) among patients with surgical site infections (SSIs) in sub-Saharan African are scarce. We characterized S. aureus from SSIs in Ghana using molecular methods and antimicrobial susceptibility testing (AST). Wound swabs or aspirate samples were collected from subjects with SSIs. S. aureus was identified by matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF-MS); AST was performed by Kirby-Bauer disk diffusion, and results were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) guideline. Detection of spa, mecA, and pvl genes was performed by polymerase chain reaction (PCR). Whole-genome sequencing (WGS) was done using the Illumina MiSeq platform. Samples were collected from 112 subjects, with 13 S. aureus isolates recovered. Of these, 92% were sensitive to co-trimoxazole, 77% to clindamycin, and 54% to erythromycin. Multi-drug resistance was detected in 5 (38%) isolates. The four mecA gene-positive MRSA isolates detected belonged to ST152 (n = 3) and ST5 (n = 1). In total, 62% of the isolates were positive for the Panton-Valentine leukocidin (pvl) toxin gene. This study reports, for the first time, a pvl-positive ST152-t355 MRSA clone from SSIs in Ghana. The occurrence of multi-drug-resistant S. aureus epidemic clones suggests that continuous surveillance is required to monitor the spread and resistance trends of S. aureus in hospital settings in the country.

Impact of Media-Induced Fear on the Control of COVID-19 Outbreak: A Mathematical Study

The COVID-19 pandemic has put the world in threat for a long time. It was first identified in Wuhan, China, in December 2019 and has been declared a pandemic by the WHO. This disease is mainly caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). So far, no vaccine or medicine has been developed for the proper treatment of this disease, so people are afraid of getting infected. The pandemic has placed many nations at the door of socioeconomic emergencies. Therefore, it is very important to predict the development trend of this epidemic, and we know mathematical modelling is a basic tool to research the dynamic behaviour of disease and predict the spreading trend of the disease. In this study, we have formulated a mathematical model for the COVID-19 outbreak by introducing a quarantine class with media-induced fear in the disease transmission rate to analyze the dynamic behaviour of this epidemic. We have calculated the basic reproduction number R0, and we observed that when R0 < 1, disease-free equilibrium is globally stable whereas if R0> 1, then the system is permanent and there exists a unique endemic equilibrium point. Global stability of the endemic equilibrium point is developed by using Li and Muldowney’s high-dimensional Bendixson criterion. Finally, some numerical simulations are performed using MATLAB to verify our analytical results.

Spatiotemporal and driving forces of Ecological Carrying Capacity for high-quality development of 286 cities in China

As an important measuring of social sustainable development, Ecological Carrying Capacity (ECC) has a pivotal role in the high-quality development of economy and society. It has great scientific and practical significance to evaluate ECC of cities. Based on component elements and structural characteristics of urban ecosystem, we use Geographically and Temporally Weighted Regression (GTWR) to analyze spatial and temporal pattern and influencing factors of ECC of 286 cities in China during 2008–2017. The results are: (1) Areas with high ECC are mainly concentrated in the provincial capital cities, showing a diffusion trend of “from central to periphery”, (2) The medium-level cities show a spreading trend of “from east to west”; (3) The number of cities in low-level areas is steadily decreasing, showing a gradual upward trend of ECC; (4) Factors such as population density, GDP, unemployment, wage, education expenditure, harmless disposal rate of household waste, industrial structure, have significant impacts on the spatial pattern of urban ECC, with different impact intensity. The novelty of this paper lies in: (1) this paper comprehensive evaluation of ECC index by multi-dimensional and diversified evaluation; (2) this paper discusses the influencing factors of ECC from three aspects of nature, society and economy, and explains the change trend of each variable from two dimensions of time and space based on GTWR model.

Pramogų pasaulio „teatras“ kaip medijuotos visuomenės problema

The article deals with the “theater” of entertainment world as issue of mediated society. Modern Western society can be called a mediated society for a strong impact of new, global media. Under these conditions, society, culture and everyday life is increasingly interpreted using a model of theatre and theatrical metaphors. Theatre is becoming one of the key concepts of the modern Western social categories, such as the use of concepts as simulation or society of the spectacle. Regarding to the wide spread of the new – digital media, especially the Internet, in recent decades of Western society, has increased the consumption of popular culture, one might say, began the era of the entertainment world. This study – one of the first attempts to analyze the philosophical Lithuania entertainment world phenomenon: the genesis, structure, impact on society. This article analyzes the phenomenon of entertainment world: its genesis, structure, impact on society. The objective: to explore the causes and trends of mass spread of the phenomenon of entertainment world in contemporary Western culture as an integral part of a technological breakthrough result of new media. The analysis of the phenomenon of entertainment world aims to: 1) explore genesis, causes and objectives of the world of entertainment as a cultural phenomenon and to reflect critically its forms of thinking and lifestyle; 2) reveal the structure of the entertainment world in twenty-first century society of the spectacle; 3) analyze the influence of the media impact to the entertainment world.

Bayesian detection of event spreading pattern from multivariate binary time series

This paper concerns the inference of the event spreading path from noisy event occurrence time series. We propose a probabilistic tree model for the event spreading path, and provide a full Bayesian approach with a Metropolis-Hastings (MH) algorithm to detect the tree path from multivariate binary time series. Our approach allows a node to spread the event in a probabilistic way to an arbitrary number of downstream nodes, the input data to contain an unknown number of nodes which are not inside the event spreading path, and the algorithm to sample only tree spreading structures. Efficient MH moves and iterative procedures guarantee the detection even if none or multiple event spreading paths are presented in the data. Simulations on synthesized data sets showed that our method can accurately detect the event spreading topologies, has good one-step-ahead prediction accuracy and outperforms existing approaches. We also analyzed the influence of model parameters on estimation accuracy in depth and obtained some interesting conclusions. Furthermore, we applied our method to the PM2.5 data in provincial capitals of north and east China. The inferred path is largely consistent with the known pollutant spreading trend.

ARTIFICIAL INTELLIGENCE APPLICATION IN EDUCATION: FINANCIAL IMPLICATIONS AND PROSPECTS

Modern education systems face new possibilities of application of innovative technological decisions in education that promote adaptive learning systems. Nowadays artificial intelligence becomes the central element of these systems and the basic tool to obtain competitive advantages on education services market. Thus, the aim of our research is to determine main trends, challenges and opportunities in application of artificial intelligence in education, exploring financial and social benefits, prospects and threats of that process. In order to achieve main aim of the research, some general and specific methods, such as content analysis, synthesis, abstraction and logical-graphic structuring, were used. Main trends are presented from three points of view: new horizons of teaching and learning opportunities, new threats for teaching and learning opportunities, and new financial and economic opportunities. Despite the social threats of AI usage, such as risks of human teacher unemployment and social interaction destruction within the learning process, market of AI for education systems steep increase. The highest rates of growth are typical for Asia-Pacific region, particularly for fast-growing economies such as China, and South Korea; North America market for AI in education still maintains the lead. Comparing trends in AI spread in education with financial successes of these countries we conclude that AI use in education systems can mitigate social drawbacks via greater accessibility for knowledge, higher quality of the educational process, individual and country competitiveness increase. Consequently, investments in innovation and promising AI programs in education remain a priority for economic growth and demand further support of educational policy makers.

2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV.

Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.

Trends of Mortalities and Morbidities due to COVID-19, from Explosiveness to Aggressiveness, Gaps in System Response, and Transmission Chain

Background: Due to the high prevalence and wide distribution of coronaviruses, their large genetic diversity, frequent recombination of their genomes, and increasing human-animal interface activities, novel coronaviruses are likely to emerge periodically in humans owing to frequent cross-species infections and occasional spillover event. Objectives: The aim of this research is to study the short-term morbidities, mortalities and trends of international spread of the COVID-19 outbreak. In addition, to study the explosiveness and aggressiveness of the outbreak and the gaps in response. Methodology: Outbreak events were observed and followed up over two months. This study used daily statistical reports issued by the World Health Organization, as well as data from different national authorities, regarding mortalities and morbidities from all over the world, with a specific focus on Chinese statistics as the main source of the outbreak. Results: The current study showed that the number of confirmed COVID-19 infected cases started with 41 cases in early January 2020. The number of cases with COVID-19 kept rising to reach 128000 on March 12, 2020. Moreover, the current study has revealed that the number of deaths was only (1) case in early January 2020 and increased to 25 deaths on January 23, and end up with 170 deaths by the end of January. Deaths have dramatically increased to reach 2462 on Feb 23 and 4717 on March 12. In regards to the geographical spread of the COVID-19 infection outbreak, the number of affected countries increased to 100 by mid- March 2020. The number of fatality cases was 0.0368, compared to 0.533 in recovered cases within two months. Conclusion: The natural history and course of the current outbreak revealed a highly explosive nature linked to considerable aggressiveness in terms of complications and mortalities, within only two months, and spread to 100 countries worldwide with 80000 cases within only two months. The study forecasted that the peak of the outbreak is not yet attained, and events that are more dramatic may occur worldwide.