scholarly journals Contact network analysis of patients with Novel Coronavirus Pneumonia - Based on 237 cases in Shaanxi Province

2020 ◽  
Author(s):  
Yang Zhangbo
Keyword(s):  
Network Analysis ◽  
Dynamic Contact ◽  
Shaanxi Province ◽  
Contact Network ◽  
Degree Centrality ◽  
Local Infection ◽  
Strong Ties ◽  
Infected People ◽  
Imported Cases ◽  
Novel Coronavirus

Abstract The spread of novel coronavirus is closely related to the structure of human social networks. Based on 237 cases in Shaanxi Province, using epidemiological retrospective statistics, data visualization, and social network analysis methods, this paper summarized characteristics of patients with new coronary pneumonia in Shaanxi Province, and analyzes these patients’ dynamic contact network structure. The study found that there are many clustered infections through strong ties, about one-third cases are caused by relatives' infection. In the early stages of the epidemic, it was mainly imported cases, and in the later stages, it was mainly local infection cases. The infected people were mainly middle-aged men. Symptoms of imported cases occurred on average 3 days after they arrived, and medical measures were taken on average 5 days later. All cases showed symptoms in less than 2 days on average and were then taken to medical treatment. The virus contact network can be divided into multiple disconnected components. The largest component has 12 patients. The average degree centrality in the network is 0.987. The average betweenness degree is 0. The average closeness degree is 0.452. The average PageRank index is 0.0042. The number of contacts of patients is unevenly distributed in the network.

scholarly journals Analysis of dynamic contact network of patients with COVID-19 in Shaanxi Province of China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhangbo Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Dynamic Contact ◽  
Shaanxi Province ◽  
Contact Network ◽  
Degree Centrality ◽  
Local Infection ◽  
Strong Ties ◽  
Infected People ◽  
Imported Cases

AbstractThe spread of COVID-19 is closely related to the structure of human social networks. Based on 237 cases, by using epidemiological retrospective statistics, data visualization, and social network analysis methods, this paper summarized characteristics of patients with COVID-19 in Shaanxi, China, and analyzed these patients’ dynamic contact network structure. The study found that there are many clustered infections through strong ties, about one-third of cases are caused by relatives' infection. In early stages of the epidemic, imported cases were the most, and in the later stages, local infection cases were the most. The infected people were mostly middle-aged men. Symptoms of imported cases occurred on average of 3 days after they arrived, and medical measures were taken 5 days later on average. All cases showed symptoms in less than 2 days on average and were then taken to medical treatment. The contact network can be divided into multiple disconnected components. The largest component has 12 patients. The average degree centrality in the network is 0.987, average betweenness degree is 0, average closeness degree is 0.452, and average PageRank index is 0.0042. The number of contacts of patients is unevenly distributed in the network.

scholarly journals Contact network analysis of Covid-19 in tourist areas——Based on 333 confirmed cases in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261335
Author(s):  
Zhangbo Yang ◽  
Jingen Song ◽  
Shanxing Gao ◽  
Hui Wang ◽  
Yingfei Du ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Southern China ◽  
Virus Transmission ◽  
Dynamic Contact ◽  
Contact Network ◽  
Degree Centrality ◽  
Imported Cases ◽  
Epidemiological Characteristics ◽  
Human Networks

The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.

scholarly journals On the optimal control of coronavirus (2019-nCov) mathematical model; a numerical approach

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
N. H. Sweilam ◽  
S. M. Al-Mekhlafi ◽  
A. O. Albalawi ◽  
D. Baleanu
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Weighted Average ◽  
Necessary Optimality Conditions ◽  
Numerical Approach ◽  
Population Number ◽  
Infected People ◽  
The Stability ◽  
Novel Coronavirus ◽  
Theoretical Results

Abstract In this paper, a novel coronavirus (2019-nCov) mathematical model with modified parameters is presented. This model consists of six nonlinear fractional order differential equations. Optimal control of the suggested model is the main objective of this work. Two control variables are presented in this model to minimize the population number of infected and asymptotically infected people. Necessary optimality conditions are derived. The Grünwald–Letnikov nonstandard weighted average finite difference method is constructed for simulating the proposed optimal control system. The stability of the proposed method is proved. In order to validate the theoretical results, numerical simulations and comparative studies are given.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gerardo Chowell ◽  
Sushma Dahal ◽  
Raquel Bono ◽  
Kenji Mizumoto
Keyword(s):  
Public Health ◽  
Cost Effective ◽  
Contact Network ◽  
Social Distancing ◽  
Individual Level ◽  
Health Measures ◽  
Testing Strategies ◽  
Novel Coronavirus ◽  
The Individual ◽  
The Impact

AbstractTo ensure the safe operation of schools, workplaces, nursing homes, and other businesses during COVID-19 pandemic there is an urgent need to develop cost-effective public health strategies. Here we focus on the cruise industry which was hit early by the COVID-19 pandemic, with more than 40 cruise ships reporting COVID-19 infections. We apply mathematical modeling to assess the impact of testing strategies together with social distancing protocols on the spread of the novel coronavirus during ocean cruises using an individual-level stochastic model of the transmission dynamics of COVID-19. We model the contact network, the potential importation of cases arising during shore excursions, the temporal course of infectivity at the individual level, the effects of social distancing strategies, different testing scenarios characterized by the test’s sensitivity profile, and testing frequency. Our findings indicate that PCR testing at embarkation and daily testing of all individuals aboard, together with increased social distancing and other public health measures, should allow for rapid detection and isolation of COVID-19 infections and dramatically reducing the probability of onboard COVID-19 community spread. In contrast, relying only on PCR testing at embarkation would not be sufficient to avert outbreaks, even when implementing substantial levels of social distancing measures.

The Challenges of Biomedical Waste Management During the Ongoing Coronavirus Disease-19 (COVID-19) Pandemic: The Current Scenario

2021 ◽  
Vol 13 ◽  
Author(s):  
Sabitha Vadakedath ◽  
Venkataramana Kandi ◽  
Tarun Kumar Suvvari ◽  
L V Simhachalam Kutikuppala ◽  
Vikram Godishala ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Complex Nature ◽  
The Novel ◽  
Waste Collection ◽  
Biomedical Waste ◽  
Infected People ◽  
Life Threatening ◽  
Current Scenario ◽  
Constant Observation ◽  
Novel Coronavirus

: The novel Coronavirus (SARS-CoV-2) that has emerged and spread throughout the world causing CoV disease-19 (COVID-19) has since its discovery affected not only humans and animals but also the environment. Because of the highly infectious nature of the virus, and the respiratory aerosol transmission route, face masks and personal protective equipment have become mandatory for public and healthcare workers, respectively. Also, the complex nature of the pathogenicity of the virus, wherein, it has been associated with mild, moderate, and severe life-threatening infections, has warranted increased laboratory testing and placing the infected people in isolation and under constant observation in quarantine centers or at dedicated hospitals. Some infected people, who are generally healthy, and do not show symptoms have been placed in home quarantines. At this juncture, there has been increased amount of biomedical waste (BMW), and infectious general waste along with plastic disposable recyclable and non-recyclable waste. The increased BMW along with the potentially hazardous plastic waste collection, segregation, transport, and disposal has assumed increased significance during the ongoing pandemic. Therefore, this review attempts to investigate the current scenario of BMW management and strategies to minimize BMW and prevent potential environmental pollution.

Impact of COVID-19 CORONAVIRUS PANDEMIC on sustainable development goals: What we learn from the past and where we are heading?

2020 ◽  
Vol 1 (1) ◽  
pp. 30-47
Author(s):  
Muhammad Imran Qureshi ◽  
Nohman Khan
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Economic Recession ◽  
Historical Background ◽  
Screening Process ◽  
Infected People ◽  
The Past ◽  
Development Goals ◽  
Novel Coronavirus ◽  
Human Infections

The recent deadly outbreak of Novel Coronavirus (2019-COVID) accompanying human to human spread caused severe human infections.  COVID19 initially encountered at the city of Wuhan in Hubei province in China.  It spread rapidly, and the number of infected people, as well as fatality ratio, increased drastically around the globe. This study aims to identify the historical background of the coronavirus family that is already affected the civilization and animals. This study overviewed the overall literature published on the Coronavirus. The Scopus database is selected to analyse the published literature. The research methodology followed a strict screening process recommended in the PRISMA statement framework (2015) for the screening and quality assessment of systematic literature review. Final 41 studies were included for the systematic literature review. A systematic review of the past literature identified severe acute respiratory syndrome coronavirus (SARS), Middle East Respiratory Syndrome Coronavirus (MERS), bovine Coronavirus, canine Coronavirus and feline Coronavirus are the significant classifications of Coronavirus family discuss in the literature. This study contributes to the literature by providing an elaboration of detailed mapping of the existing literature on the reviews of Coronavirus pandemic that is a more significant challenge for humanity in the current circumstances. Finally, the future of the world after the 2019-COVID is more challenging and vital for understanding in terms of economic and social perspective. Social structures will change the current situation is showing based on literature and reports. The economic recession will be prolonged if the researchers are not able to find the solution for the Coronavirus.

scholarly journals A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
Author(s):  
Dongryeul Kim
Keyword(s):  
Middle School ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Middle School Students ◽  
Collaborative Learning ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Science Class ◽  
School Students

  In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

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