Help | Advanced Search

Computer Science > Social and Information Networks

Title: sentiment analysis on youtube: a brief survey.

Abstract: Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.

Submission history

Access paper:.

  • Download PDF

magnt research report journal

References & Citations

  • Google Scholar
  • Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Identifiers

Linking ISSN (ISSN-L): 1444-8939

Google https://www.google.com/search?q=ISSN+%221444-8939%22

Bing https://www.bing.com/search?q=ISSN+%221444-8939%22

Yahoo https://search.yahoo.com/search?p=ISSN%20%221444-8939%22

NLA catalogue https://catalogue.nla.gov.au/Search/Home?lookfor=isn%3A1444-8939%3A&type=isn;limit[]=submit=Find

Resource information

Title proper: MAGNT research report.

Other variant title: Museums and Art Galleries of the Northern Territory research report

Country: Australia

Medium: Print

Record information

Last modification date: 25/01/2009

Type of record: Confirmed

ISSN Center responsible of the record: ISSN National Centre for Australia

downloads requested

Discover all the features of the complete ISSN records

Display mode x.

Labelled view

MARC21 view

UNIMARC view

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A Case Study

Profile image of masoum Farahmandian

Suitable diagnosis and selection of appropriate ways of treatment for people who are afflicted with diabetes are of great importance since ignorance in remedying diabetes can cause other organs to be defected and also can lead to death. Nowadays, there are many different ways for curing this disease, but choosing the appropriate way which has not only lower degree of damaging people but also has good output is a hard task to do. Usually, the effective way for curing it is diagnosing it right on cues. Therefore, designing a system for diagnosing diabetes can help doctors in choosing the remedy on time. Thus, we try to diagnose diabetes in this paper using the algorithms of the data which are crucial in diagnosis and prediction. Data mining on medicinal data is really important and designing prediction systems for helping doctors in diagnosing the type of disease and choosing the kind of cure can contribute a great deal to saving the lives of people. Data mining has various algorithms, but for diagnosing diabetes we have used Support Vector Machine (SVM), K Nearest Neighbors (KNN), Naïve Bayes, ID3, C4.5, C5.0, and CART. Evaluation and conclusion of data mining algorithms which contain 768 records of different patients have been carried out on Pima dataset. Results have shown that the degree of Accuracy in SVM algorithm is equals to 81.77.

Related Papers

Proceedings in Computing, 9th International Research Conference-KDU

Kasun Jinasena , Sanjaya De Silva

Diabetes is one of deadliest diseases in the world. As per the existing system in Sri Lanka, patients have to visit a diagnostic center, consult their doctor and wait for a day or more to get their result. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches, we have been able to find a solution to this problem using data mining. Data mining is one of the key areas of Machine learning. It plays a significant role in diabetes research because It has the ability to extract hidden knowledge from a huge amount of diabetes related data. The aim of this research is to develop a system which can predict whether the patient has diabetes or not. Furthermore, predicting the disease early leads to treatment of the patients before it becomes critical. This research has focused on developing a system based on three classification methods namely, Decision Tree, Naïve Bayes and Support Vector Machine algorithms. Currently, the models give accuracies of 84.6667%, 76.6667%, and 77.3333% for Decision Tree, Naïve Bayes, and SMO Support Vector Machine respectively. These results have been verified using Receiver Operating Characteristic curves in a cost-sensitive manner. The developed ensemble method uses votes given by the other algorithms to produce the final result. This voting mechanism eliminates the algorithm dependent misclassifications. Results show a significant improvement of accuracy of ensemble method compares to other methods.

magnt research report journal

EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021)

Dr. Viswanathan Chakravarthi

IJAR Indexing

Diabetes is one of the most prevalent diseases in the world today with high mortality and morbidity rate, thus one of the biggest health problems in the world. Diagnosis of diseases is a vital role in medical field. The use of data mining on medical data brings important, valuable and effective achievement, which can enhance the medical knowledge to make necessary decision. The paper is organized as follows; it first gives a study done on diabetes and its types. Second it explains the Data Mining techniques and Statistical method used to predict Diabetes. Then the paper ends by concluding with summary of investigated methods.

nandhini nagarajan

The aim of data mining is to extract hidden knowledge from huge amount of data set and generate clear and easy understandable patterns. Diabetes is a group of metabolic disease caused by increased level of blood glucose. Different data mining algorithms are applied in medical research in order to diagnosis large amount of medical dataset. Various data mining algorithms were designed for diagnosing diabetes based on physical and chemical tests. The main data mining algorithms discussed in this paper are EM algorithm, K means, C4.5 algorithm, Genetic algorithm and SVM. EM is the expectation maximization used for sampling, to determine and maximize the expectation in successive iterative cycles. C4.5 is a decision tree induction technique that has been successfully applied for medical data. Genetic algorithm is population based model that uses selection and recombination operators to generate new sample points. Support Vector Machine are set of supervised learning method whose training tech permit to represent complex non linear function. K means is a unsupervised which objects are moved among sets of cluster until the desired set is reached. This paper studies the comparison of various data mining algorithms for prediction of diabetes disease.

Saradha Swami

Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various available traditional methods for diagnosing diabetes are based on physical and chemical tests. These methods can have errors due to different uncertainties. A number of Data mining algorithms were designed to overcome these uncertainties. Among these algorithms, amalgam KNN and ANFIS provides higher classification accuracy than the existing approaches. The main data mining algorithms discussed in this paper are EM algorithm, KNN algorithm, K-means algorithm, amalgam KNN algorithm and ANFIS algorithm. EM algorithm is the expectation-maximization algorithm used for sampling, to determine and maximize the expectation in successive iteration cycles. KNN algorithm is used for classifying the objects and used to predict the labels based on some closest training examples in the feature space. K means algorithm follows partitioning methods based on some input parameters on the datasets of n objects. Amalgam combines both the features of KNN and K means with some additional processing. ANFIS is the Adaptive Neuro Fuzzy Inference System which combines the features of adaptive neural network and Fuzzy Inference System. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases.

E. Karthikeyan

The process of analyzing different aspects of data and aggregating it into useful information is called data mining. The goal is to provide meaningful and useful information for the users about the diabetes. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This research project aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naïve Bayes algorithms. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.

Dawood Saddique

Diabetes is one of the chronic diseases in which the blood sugar or blood glucose level is above a certain amount in the body. It is often known as the silent killer because of its easyto-miss symptoms of the Diabetes Disease (DD). Gestational diabetes is a type of diabetes which occurs in women during their pregnancy and can cause potential health issues for both the mother and the child. The classification of the DD is essential to improve the quality of life of patients suffering from the disease. The primary objective of this research work is to identify the most dominant feature for the DD and to classify the DD for its early diagnosis. Data mining and machine learning (ML) techniques including Naive Bayes, Artificial Neural Network (ANN), Decision Tree (DT), Logistic Regression, and Support Vector Machine (SVM) are used to predict the DD. Pima Indian Diabetes (PID) dataset is used in this experimental investigation, and the performance of the developed models is evaluated usin...

Annals of Emerging Technologies in Computing

Diabetes is one of the chronic diseases in the world, 246 million people are inflicted by this disease and according to a World Health Organisation (WHO) report, this figure will increase to 380 million sufferers by 2025. Many other debilitating and critical health issues may further develop if this disease is not diagnosed or remain unidentified. Machine Learning (ML) techniques are now being used in various fields like education, healthcare, business, recommendation system, etc. Healthcare data is complex and high in dimensionality and contains irrelevant information - due to this, the prediction accuracy is low. The Pima Indians Diabetes Dataset was used in this research, it consisted of 768 records. Firstly, the missing values are replaced by the median followed by Linear Discriminant Analysis. Using the Python programming language, feature selection techniques is applied in combination with five classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (ML...

Diabetes is specified as the most chronic and deadliest disease that results in increasing blood sugar. The medical data mining approaches were utilized for detectingun observed patterns in the medical field sof sets of data for medical diagnosis and treatment. Data classification for diabetes mellitus is quite significant. Where utilizing two types of data sets, the first is local, collected from consulting laboratories at Baqubah General Hospital, and the second is global, which

RELATED PAPERS

Biotechnology and Bioengineering

Spiros Agathos

The Medical Journal of Australia

Pat Dudgeon

Environmental Entomology

Ma Marcos-García

Acta Neurochirurgica

Ahmad Agung

Whitney J. Autin

Bulletin- Veterinary Institute in Pulawy

Recai Kulaksız

Journal of Physics: Condensed Matter

Sudhindra Rayaprol

Qualitative Inquiry

Kelly Clark/Keefe

Procedia - Social and Behavioral Sciences

Adela Moraru

Michael Wilson

Optics & Laser Technology

Filimon Zacharatos

Scientific Reports

Jonathan Boxman

Algorithmic Operations Research

ROBERTO CALVO

Journal of Mathematical Chemistry

Tomislav Došlić

Clinical Rheumatology

Simona D'Amore

American Journal of Obstetrics and Gynecology

Chaur-Dong Hsu

NMC Case Report Journal

Erwin Maulana

Handbook of Research on the Education of Young Children

Kerry Hofer

Energy Policy

María-José Prados

Assets: Jurnal Akuntansi dan Pendidikan

keumala hayati

IFIP Advances in Information and Communication Technology

Angela Zinnai

Journal of environment and earth science

Yemisi Ajisafe

RELATED TOPICS

  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

figshare

Misuse of Diplomatic Immunities and Law Enforcement of Diplomatic Immunities and International Rights

Abstract: from the newest discussions in field of diplomatic rights, is misuse of diplomatic immunities. In this paper, at first the concept of immunity is detected and diplomatic rights documents is stated. And then philosophic and legal basis of immunity endowment to government representatives are analyzed. By this the goal of diplomatic immunities is revealed and two obligated principles stated by board of the deputies are allowed to state: 1- principle of not interfering. 2- Respect to laws and principle of receptive state. And then it continues with some abuse samples of diplomatic immunities and at last law enforcement of immunity abuse is stated. Key Words: political diplomat, political immunities, diplomatic rights, international rights

Usage metrics

  • Other law and legal studies not elsewhere classified
  • Sociology not elsewhere classified
  • Other education not elsewhere classified

CC BY 4.0

  • Latest Headlines
  • English Edition Edition English 中文 (Chinese) 日本語 (Japanese)
  • Print Edition
  • More More Other Products from WSJ Buy Side from WSJ WSJ Shop WSJ Wine

This copy is for your personal, non-commercial use only. Distribution and use of this material are governed by our Subscriber Agreement and by copyright law. For non-personal use or to order multiple copies, please contact Dow Jones Reprints at 1-800-843-0008 or visit www.djreprints.com.

https://www.wsj.com/articles/claudine-gay-and-why-academic-honesty-matters-plagiarism-research-science-98a441c1

Claudine Gay and Why Academic Honesty Matters

Politicization and lower standards jeopardize harvard’s standing as a great research institution..

James Hankins

Dec. 27, 2023 5:36 pm ET

image

Claudine Gay , the president of my university, is under attack for academic dishonesty. She is charged with several instances of plagiarism, in her dissertation and other published work, in addition to data falsification. As of this writing it seems not unlikely that she may be fired or asked to resign.

Copyright © 2024 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8

  • Top Resume : Top Resume Coupon: 10% Off professional resume writing
  • Walmart : $20 Off Walmart Promo Code - Any $50+ Order
  • Groupon : Groupon New Year coupon: Up to 75% off all local deals + extra 30% off at checkout
  • eBay : Unlock 10% Off orders With this eBay coupon
  • AliExpress : AliExpress $6 Off coupon
  • JCPenney : Get 25% off your Online Purchases w/ JCPenney Coupon Code

Most Popular news

Most popular opinion, most popular opinion, recommended videos.

Copyright © 2024 Dow Jones & Company, Inc. All Rights Reserved

This paper is in the following e-collection/theme issue:

Published on 4.1.2024 in Vol 26 (2024)

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study

Authors of this article:

Author Orcid Image

Original Paper

  • Ming Kei Chung 1, 2, 3 * , PhD   ; 
  • Brian Hart 4 , PhD   ; 
  • Mauricio Santillana 5 , PhD   ; 
  • Chirag J Patel 1 * , PhD  

1 Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States

2 Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

3 Institute of Environment, Energy, and Sustainability, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

4 Optum Labs, Eden Prairie, MN, United States

5 Machine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, MA, United States

*these authors contributed equally

Corresponding Author:

Chirag J Patel, PhD

Department of Biomedical Informatics

Harvard Medical School

Harvard University

10 Shattuck Street, Room 302

Boston, MA, 02115

United States

Phone: 1 617 432 1195

Email: [email protected]

Background: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth.

Objective: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic.

Methods: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases.

Results: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74).

Conclusions: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

Introduction

Since the outbreak of COVID-19 in early 2020, over 500 million cases and 6 million deaths have been recorded by the World Health Organization [ 1 ]. In the United States alone, over 81 million people have been infected, and almost 1 million people have died due to SARS-CoV-2 [ 1 ]. Characterizing the transmission dynamics of the evolving virus provides important evidence to assist in formulating effective public health interventions to fight the pandemic. For example, recent research utilizing data from over 80, 000 household contacts estimated the infectivity of the Omicron and Delta variants of SARS-CoV-2 [ 2 ].

The Centers for Disease Control and Prevention (CDC) is responsible for keeping track of the COVID-19 pandemic and providing vital national statistics about COVID-19 in the United States, including new cases, deaths, hospitalizations, and vaccination rates [ 3 ]. However, location and family-related information is typically not collected; thus, it is difficult to estimate infectivity, such as the household secondary attack rate (SAR) and odds of SARS-CoV-2 transmission to family members. In European countries, such as England and Denmark, it is possible to link individual SARS-CoV-2 test results to the national administrative records in centralized health care systems and registers and thereby obtain address information [ 4 - 6 ]. Currently, the National COVID Cohort Collaborative is one of the largest United States counterparts with data from over 13 million patients for COVID research [ 7 ]. Nevertheless, because of privacy regulations, location information is not readily accessible for estimating the infectivity of SARS-CoV-2 to family members.

There are limited reports on household transmission of SARS-CoV-2 in the United States, especially during the first year of the COVID-19 pandemic. Most of the studies were small in sample size (between 100 and 400), sampled in a particular state, and spanned 1 month to a few months [ 8 - 11 ]. Therefore, the estimated transmission rate of SARS-CoV-2 had a large variation with reported values of SARs between 25% and 38%, and the source of the variation is a challenge to pinpoint. In addition, the relationship between transmission risk and family size has been inconsistent [ 4 , 12 - 14 ]. A few studies compared the SARs between young adult (age 18 years or older) and child (age below 18 years) contacts and reported that adults were generally more susceptible to infection (between 28% and 48%) than children (between 16% and 28%) [ 8 - 10 ]. Currently, little is known about the relative infectivity of children and young adult index cases in a household setting in the United States [ 12 , 13 ]. Therefore, a full understanding of the baseline transmissibility in the initial waves of SARS-CoV-2 (ie, pre–SARS-CoV-2 Delta variant) in the United States can fill an important knowledge gap. This understanding serves as a crucial complement, enabling informed assessments of the infectivity and effectiveness of vaccination against other variants of concern, such as Omicron and Delta [ 15 ].

In this study, we hypothesized that the pediatric household transmission of SARS-CoV-2 in the United States was higher in the first outbreak than in the subsequent outbreaks. We aimed to repurpose administrative data to estimate pediatric household transmission of SARS-CoV-2 in the United States between January 2020 and February 2021, a critical time in the trajectory of the pandemic. We leveraged a large nationwide administrative claims data source and estimated the temporal trend of SARs. We also compared the relative infectivity (as the odds ratios [ORs] of SARS-CoV-2 transmission) between 4 pediatric index case age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for other factors such as family size, quarter of infection, and residential regions to understand the early SARS-CoV-2 transmission dynamics.

Data Source

We conducted a retrospective cohort analysis on the household transmission of SARS-CoV-2 using deidentified administrative claims. The data source contained comprehensive inpatient and outpatient claims records, laboratory test results, and individual and various aggregated demographic information. In this study, we leveraged the outpatient laboratory data set for SARS-CoV-2 to assemble the cohort.

Ethical Considerations

The Harvard Medical School Institutional Review Board waived the approval requirement for this study as our database analysis is not regarded as human participants research (IRB20-0935).

Study Population

Identification of households.

We identified familial relationships through a family identification variable available in the demographic data table. Because of the nature of the commercial administrative data, only residential private households were included. Since household identification was based on the relationships between employee subscribers and the beneficiary members, most of the households were single- or 2-generation families.

SARS-CoV-2 Infection

We used the Logical Observation Identifiers Names and Codes (LOINC) information submitted by the laboratory vendors to populate the SARS-CoV-2 records, including test dates, types, and results in the outpatient laboratory data set. An individual could be tested at multiple time points with different test results. We defined a SARS-CoV-2 case as an individual tested positive with polymerase chain reaction (PCR) assay. Among the 19,028,401 rows of records, over 78.30% (n=14,899,238) were PCR tested and 10.22% (n=1,522,923) were positive.

We used SAR to measure household transmission of SARS-CoV-2, which can be interpreted as the probability of new infection among susceptible household members caused by a single case. We defined pediatric index cases (children and young adults) as those aged between 0 and 25 years who had the earliest record of SARS-CoV-2 positives in the households. We defined household contacts as all the individuals within the same household excluding the index cases. For secondary cases, we classified those individuals with SARS-CoV-2 positive 2 to 14 days after the index cases [ 4 ].

Assembling the Cohort

We created the analysis cohort as described in Figure 1 . We first started with all records tested between January 1, 2020, and February 28, 2021, in the laboratory data set because we were only able to obtain complete data within this period at the time of the analysis. We did not restrict subscribers to have continuous enrollment in their health plans because the window of our analysis was short (2 weeks). There were 19,028,401 rows of records, of which we excluded 11,633,630 (61.14%) mostly because of missing family information to identify the corresponding household members. We tentatively identified 4,270,130 households and further excluded (1) 3,781,308 (88.55%) households because only a single member could be found, (2) 367,961 (8.62%) households because of missing SARS-CoV-2 cases, and (3) 15,208 (0.36%) households with more than 1 index case to avoid complicating the estimation of the relationship between index case and household transmission. After including only households with pediatric index cases, we were able to retain 36,241 effective households for the analysis (0.85% of the 4,270,130 households). We found that 31,177 (86.02%) of the households had no secondary case and 5,064 (13.97%) had at least 1 secondary case.

magnt research report journal

Study Variables

We derived the age variable based on the date of birth information in the administrative claims. We derived the month and quarter of the year of SARS-CoV-2 positive variables based on the date of the laboratory test records. Next, we estimated the family size in each household by counting individuals with the same family identification number. We categorized the date of the first SARS-CoV-2 positive into 5 quarters (between 2020 to 2021). Finally, we used the Census Bureau regions and divisions classification and the residential state information to create the regional division variable with 10 categories.

Statistical Analysis

We calculated SAR as the total number of secondary cases divided by the total number of household contacts in the population. To estimate monthly SARs, we grouped households based on the monthly SARS-CoV-2 test records of the pediatric index case (ie, the first to the last day of the corresponding months). We estimated the standard errors by bootstrapping and calculated the corresponding 95% CIs of the mean SAR.

To understand the dynamic of household transmissions in the pediatric population, we conducted a stratified analysis to estimate the SARs in the age groups of pediatric index cases. Specifically, we created 4 pediatric age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) and calculated the mean SARs and 95% CIs as described previously.

We conducted a descriptive analysis of the pediatric index cases by the 4 age groups. To quantify the associations between pediatric index case age groups and the household transmission of SARS-CoV-2 to contacts, we used logistic multiple regression to obtain the ORs using the oldest pediatric group (18 to 25 years) as the reference. We modeled the outcome as a binary variable of whether the index case is linked with any secondary cases in the corresponding household. We ran three different regression models to gauge the robustness of our analysis [ 16 ]: (1) a crude model with only the pediatric index case age groups as the predictors; (2) an adjusted model that accounted for the influences of sex, season of SARS-CoV-2 test of the pediatric index cases, and family size; and (3) a model further adjusted with the residential region of the pediatric index cases. We set a 2-sided statistical significance level of P =.05 for all the analyses. We executed our analyses using R software (version 4.0.2; R Foundation for Statistical Computing) and used the boot package for the bootstrapping step.

We started with over 19 million SARS-CoV-2 test records, which were collected in the first 16 months of the COVID-19 pandemic in the United States We filtered the records and identified 36, 241 eligible households with pediatric index cases for the study. Most of these households (n=31,177, 86.02%) did not report any secondary cases ( Figure 1 ).

We outline the major demographic and associated characteristics of the index case age groups in Table 1 . The number of individuals in each age group is correlated with the order of the age groups. The oldest age group (18 to 25 years) had 21.9 times the individuals found in the youngest one (0 to 4 years). In each age category, we found roughly equal numbers of female and male individuals. However, the difference was larger in the oldest age group, which comprised 54.1% (n=13,394) female individuals versus 45.9% (n=11,363) male individuals. The majority of the SARS-CoV-2 cases were detected in the fourth quarter of 2020, followed by the first quarter of 2021. Over half the index cases were found in these 2 quarters. The median household size was 2 across all the age groups. The number of index cases varied greatly across 10 regional divisions, from around 2% in the East South-Central states (Alabama, Kentucky, Mississippi, and Tennessee) to 25% in the South Atlantic states (Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, Washington, DC, and West Virginia).

We plot the temporal changes of monthly SARs in Figure 2 . Detailed numeric values for the graph can be found in Multimedia Appendix 1 . Although our data source contained SARS-CoV-2 test records from January 1, 2020, to March 2021, we could only identify valid households between March 2020 and February 2021. The overall SAR in this period was 23.04% (95% CI 21.88-24.19). Monthly SARs varied from the lowest in September 2020 at 15.6% (95% CI 13.9-17.3) to the highest in April 2020 at 38.3% (95% CI 31.6-45). We observed 3 distinctive trends in the studied period, in that the SAR first generally decreased from April to September 2020, followed by a rebound to a peak SAR of 29% (95% CI 27.8-30.2) in December 2020 before starting to drop again until February 2021.

We show the household transmission rate of SARS-CoV-2 in the pediatric index case age groups in Multimedia Appendix 2 . We found a decreasing trend of household transmission, from a SAR of 32.0% to 21.1%, as the age of the index groups increased. Specifically, the SARs were 24.5% for the 12 to 17 years age group (95% CI 23.4-25.6) and 21.1% for the 18 to 25 years age group (95% CI 20.5-21.7). These percentages were significantly lower compared to the SAR of 32.0% for the age group of 0 to 4 years (95% CI 28.8-35.2)

We presented the logistic regression results of the associations between household transmission and pediatric age groups in Table 2 . First, we compared the absolute and percentage counts of index cases with and without linkage to any secondary cases by each of the predictors. All the predictors had less than 5% differences between 2 groups other than “2020 third quarter,” “2020 fourth quarter,” and “0-4 years” index age. Our analyses showed that the relationships between the age of the pediatric index cases and household SARS-CoV-2 transmission were consistent across all the investigated models—the younger the index cases were, the higher the odds of transmission. In model 2, we adjusted for all the available relevant predictors and found that the youngest index case group (0 to 4 years) had 1.69 times the odds of transmitting the SARS-CoV-2 to other household contacts (95% CI 1.42-2.00) compared to the oldest reference group (0 to 18 years).

a States included for the regional divisions are New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont); Mid-Atlantic (New Jersey, New York, and Pennsylvania); East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin); West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota); South Atlantic (Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, Washington, DC, and West Virginia); East South Central (Alabama, Kentucky, Mississippi, and Tennessee); West South Central (Arkansas, Louisiana, Oklahoma, and Texas); Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming); Pacific (Alaska, California, Hawaii, Oregon, and Washington).

magnt research report journal

a Variables adjusted in the model are characteristics of the index cases.

b Indicates that only the age variable is included in the crude model and regional division is not included in model 1.

c States included for the regional divisions are New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont); Mid-Atlantic (New Jersey, New York, and Pennsylvania); East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin); West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota); South Atlantic (Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, Washington, DC, and West Virginia); East South Central (Alabama, Kentucky, Mississippi, and Tennessee); West South Central (Arkansas, Louisiana, Oklahoma, and Texas); Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming); Pacific (Alaska, California, Hawaii, Oregon, and Washington).

Principal Findings

In this study, we leveraged a large deidentified claims database to investigate the COVID-19 pandemic in the United States from January 2020 to March 2021. We identified valid households in the data and estimated the household transmission of SARS-CoV-2 by pediatric to young adult index cases aged 0 to 25 years. We calculated the overall SAR as 23.04% (95% CI 21.88-24.19). In a separate analysis of the same period, we found the overall SAR for all index ages (0 to 65+ years) to be 32.4% (95% CI 32.1-32.8). We observed infection peaks in April and December 2020. In the stratified analysis with pediatric index case age groups, we observed an inverse linear trend between the age groups and SARs—the oldest index case age group (18 to 25 years) had the lowest household transmission, which was statistically significant compared with the youngest group (0 to 4 years). When we analyzed the odds of household transmission of SARS-CoV-2 with a logistic multiple regression model, we obtained results similar to those found in the SAR analysis. Using the pediatric age group 18 to 25 years as the reference, we found that household transmission of SARS-CoV-2 in the 0 to 4 years age group was 1.69 times the odds of that in the reference group (95% CI 1.42-2.00). The differences between these 2 comparison groups were statistically significant across the 3 investigated models.

Comparison to Prior Work

We found that pediatric-aged individuals did not make up the majority of the household index cases in the early waves of the COVID-19 pandemic. Only 10.9% (11,484/105,672) had pediatric index cases (age 0 to 17 years), which increased to 34% (36,241/105,672) if we included individuals aged 0 to 25 years. Similar proportions were also reported by others worldwide. A United States study reported that 14% (14/101) of the households investigated had pediatric index cases [ 12 ], while studies in Asia generally reported a lower percentage of pediatric index cases (age 0 to 20 years) in infected households: between 1% and 5% in China [ 17 , 18 ] and 3% in South Korea [ 19 ]. In Europe, the proportions were more comparable to those found in the United States: Denmark reported 5% (index age 0 to 20 years [ 20 ]); Greece reported 9% (index age 0 to 18 years [ 21 ]), and Switzerland reported 8%, (index age 0 to 16 years [ 22 ]). A comprehensive meta-analysis study suggested that the proportion was between 3% and 19% [ 23 ]. We emphasize that the SAR of 32.4% estimated for all age groups (0 to 65+ years) was larger than the overall SAR for the 0-to-25-year-old population. Additionally, it was more than 10% higher than the SAR for the young adult (18 to 25 years) population.

The reported household transmission of SARS-CoV-2 by pediatric and young adult index cases in the first year of the pandemic varied. In the United States, SARs in Tennessee and Wisconsin were 53% (95% CI 31-47) and 38% (95% CI 23-56) for index cases aged 0 to 12 years and 12 to 17 years, respectively [ 12 ]. The SAR was even higher in Los Angeles, California, at 81.9% (95% CI 72.1-91.9) for index children aged 0 to 18 years [ 24 ], which we speculate could be due to the inclusion of households with multiple index cases in the study. In a Denmark study, the SARs for young index case groups were lower, at 19% for the age group 0 to 10 years and 20% for the age group 10 to 20 years). Moreover, the ORs of viral transmission increased significantly as the age of the children index cases decreased [ 5 ]: age 0 to 5 years had an OR of 2.17 (95% CI 1.87-2.51), age 5 to 10 years had an OR of 1.66 (95% CI 1.50-1.83), and age 10 to 15 years had an OR of 1.37 (95% CI 1.26-1.49), with the reference age group being 15 to 20 years. Similar inverse relationships between the age of the pediatric index and SARS-CoV-2 transmission were reported in Norway [ 25 ], where the SARs were as follows: age 0 to 6 years, 24% (95% CI 20-28); age 7 to 12 years, 14% (95% CI 12-15); age 13 to 16 years, 14% (95% CI 13-16); and age 17 to 20 years, 11% (95% CI 10-13). Meanwhile, in Canada, the ORs were as follows: age 0 to 3 years, 1.43 (95% CI 1.17-1.75); age 4 to 8 years, 1.4 (95% CI 1.18-1.67); and age 9 to 13 years, 1.13 (95% CI 0.97-1.32), with the reference age group being 14 to 17 years.

However, this relationship was not evident in a South Korean investigation [ 19 ], where SARs were reported as 5.3% for ages 0 to 9 years (95% CI 1.3-13.7) and 18.6% for ages 10 to 19 years (95% CI 14-24). Upon further investigation, we posit that the SAR in South Korea for the age group spanning 0 to 9 years may not be reliable as it was calculated based on only 3 secondary cases (3/57). Recently, a meta-analysis of 45 studies showed that there was no significant difference in SARs between the age 0 to 19 years and age 20+ years index cases [ 23 ].

We found greater household infectivity of SARS-CoV-2 by the youngest index cases relative to their older counterparts (18 to 25 years) in this study. Although viral load is correlated with the spread of COVID-19 [ 26 ], several studies have reported that the amount of viral RNA in children is similar, or even higher, than that measured in adults [ 5 , 27 , 28 ]. Children are generally more likely to be asymptomatic after infection than adults [ 21 , 29 ], and asymptomatic cases are less capable of infecting susceptible individuals than symptomatic cases [ 18 , 23 , 30 ]. These observations contradict the results from our study and others [ 5 , 13 , 25 ]. Behavioral factors could play a bigger role in household SARS-CoV-2 transmission. Younger children require frequent and close contact care from their parents, and it may be difficult to quarantine children alone. Together, these factors could increase the infectivity of young children to parents and caregivers significantly when compared to the older, more independent, and self-caring index age group, such as those aged 18 to 25 years.

SARS-CoV-2 transmission is influenced by many factors, such as individuals’ demographic profiles and geographic locations [ 31 ]. Our regression analysis showed strong temporal and regional variations of the viral transmission in the United States SARs were high and over 30% in March and April 2020. It remained below 20% from May and September and rose to a peak value of approximately 30% in December 2020. We believe that these patterns could be explained partially by a few key events. Initially, the public was not prepared for the SARS-CoV-2, which caused high SARs. Later, national efforts were engaged, including state-level lockdowns and mask-wearing guidelines, to mitigate the risk of SARS-CoV-2 infection and transmission in the community [ 32 ]. From September onward to February 2021, the SAR pattern resembled that of the 7-day average of the national new cases [ 3 ]. During this period, changes in temperature, an increase in social activities related to the presidential election, and the availability of COVID-19 vaccines could be important factors explaining the observed trends. Among all the United States regions, New England had the lowest household transmission. This could be attributed to its high level of educational attainment, which also generally correlates with socioeconomic status [ 33 ].

Household size is a proxy for the number of susceptible contacts in estimating viral transmission. The association between household size and household transmission of SARS-CoV-2 is not consistent in the literature. Like our results, a study conducted in Canada reported a positive relationship (OR 1.63, 95% CI 1.43-1.86) [ 13 ]. However, investigations in the United States [ 12 ], England [ 4 ], and a meta-analysis of 6 international studies reported an inverse trend between the two [ 14 ]. Apart from the geographical and temporal factors, variations in the statistical modeling of the transmission outcome could also play a role in the discrepancy. Positive associations between household size and SARS-CoV-2 transmission were reported for studies defining the outcome as “any secondary cases in the household,” whereas a negative relationship was found in those studies modeling the outcome at the individual level.

In addition to the predictors investigated in our analysis, a wide range of factors are known to affect COVID-19 incidence. These include socioeconomic status, ethnicity, household composition, and numerous environmental factors like air pollution and meteorological conditions. For instance, the incidence rate ratios of the Social Vulnerability Index (SVI) to COVID-19 incidence and mortality were 1.14 (95% CI 1.13-1.16) and 1.14 (95% CI 1.12-1.16), respectively [ 34 ]. Similar findings were also observed for the subindexes of SVI. A meta-analysis also showed a weak correlation (0.36, 95% CI 0.02-0.62) between the average temperature and daily COVID-19 cases across cities from different countries [ 35 ].

Strengths and Limitations

We acknowledge that there are 4 major limitations in our analysis of the administrative claims data. First, because we used data from a private health insurer, we were only able to include working individuals in the United States with insurance coverage. Additionally, adding to the potential bias, only a small proportion of valid households for estimating household SAR were filtered from the raw data (n=35,241, 0.85%; Figure 1 ). It may not be appropriate to compare the SARs obtained only from the excluded populations, such as families with self-employed and unemployed members, because there could be unique factors affecting SARs in those populations. Nevertheless, prior studies and our preliminary comparisons between the trends of national statistics estimated by claims data and those reported by the CDC are comparable [ 36 , 37 ]. Second, ethnicity is a risk factor for SARS-CoV-2 infection [ 38 , 39 ], and we were unable to account for its influence in estimating the SAR and modeling the household transmission in the logistic regression. Our estimations can only be interpreted as national averages and substantial differences between different ethnic groups are possible. Third, although we used a family identification variable to identify household members, we lacked the capability to fully identify multigenerational households (eg, with at least 1 adult aged 65 years or above, 1 adult in the working group aged 26 to 64 years, and 1 pediatric member aged 0 to 25 years). Commercial insurance subscribers are mostly working adults with spouses and children as dependents in their health plans. However, it is known that older adults (age 65 years and above) are more susceptible to SARS-CoV-2 infection and complications, and the SARs and regression estimates of transmission will likely only be larger and stronger than our study if such information is available. Finally, it is difficult to assess why individuals in a household might seek testing. One reason might be symptoms of COVID-19 among individuals; therefore, the rates will be biased toward estimating the SARs for symptomatic index cases.

Our study also has 2 strengths. First, we used data from the first year of the COVID-19 pandemic, and it is reasonable to assume that most of the contacts were not previously infected with SARS-CoV-2. Therefore, we can obtain an estimation of SARS-CoV-2 household transmission without information about prior history of infection or antibody test results. In addition, compared with the transmission estimations from meta-analysis, we used a single large cohort and were able to minimize the influence of analytic variation such as using a case definition, data filtering scheme, and model with the same set of predictors to estimate the range and heterogeneity of SAR across the time of the pandemic and various age groups of index cases.

Conclusions

Our retrospective administrative claims study of viral transmission with 45,749 household contacts showed that large variations existed across months and regions during the initial waves of SARS-CoV-2 in the United States. The pediatric household SAR decreased from 32.0% to 21.1% as the age of the index cases increased and the SAR for all age groups was higher than the SAR for ages 0 to 25 years. There was also a positive association between family size and household transmission rate.

Acknowledgments

We thank Sarah Daugherty, Caleb Kennedy, Amy Gilmer, Teodi Enrik Racho, Callahan Clark, Michael Lahm, and Elizabeth Erickson for assisting with accessing the administrative claims data. This study was supported by grants from the United States National Institutes of Health through the National Institute for Environmental Health Sciences (ES032470, P30ES000002) and from the United States National Science Foundation through the Northeast Big Data Innovation Hub.

Data Availability

In keeping with the user agreement with the data provider concerning the access to and use of the health research database, individual participant data used in this study and the data dictionary will not be available to the public. Researchers who are interested in the study and able to provide a scientifically sound proposal should contact the corresponding author (CJP) for collaboration. For reproducibility purposes, all analytic code will be provided upon request.

Conflicts of Interest

None declared.

Graph and table of monthly household secondary attack rates of SARS-CoV-2 for pediatric index cases in the United States between March 2020 and February 2021.

Household secondary attack rates of SARS-CoV-2 stratified by the pediatric index age groups.

  • Kim SY, Yeniova A. Global, regional, and national incidence and mortality of COVID-19 in 237 countries and territories, January 2022: a systematic analysis for World Health Organization COVID-19 Dashboard. Life Cycle. May 12, 2022;2 [ CrossRef ]
  • Jørgensen SB, Nygård K, Kacelnik O, Telle K. Secondary attack rates for Omicron and Delta variants of SARS-CoV-2 in Norwegian households. JAMA. Apr 26, 2022;327(16):1610-1611. [ https://europepmc.org/abstract/MED/35254379 ] [ CrossRef ] [ Medline ]
  • COVID data tracker. Centers for Disease Control and Prevention. URL: https://covid.cdc.gov/covid-data-tracker [accessed 2022-05-09]
  • Harris RJ, Hall JA, Zaidi A, Andrews NJ, Dunbar JK, Dabrera G. Effect of vaccination on household transmission of SARS-CoV-2 in England. N Engl J Med. Aug 19, 2021;385(8):759-760. [ CrossRef ]
  • Lyngse F, Mølbak K, Franck K, Nielsen C, Skov R, Voldstedlund M, et al. Association between SARS-CoV-2 transmissibility, viral load, and age in households. MedRxiv. Preprint posted June 4, 2021. [ https://www.medrxiv.org/content/10.1101/2021.02.28.21252608v2 ] [ CrossRef ]
  • Lyngse F, Kirkeby C, Halasa T, Andreasen V, Skov R, Møller F, et al. Nationwide study on SARS-CoV-2 transmission within households from lockdown to reopening, Denmark, 27 February 2020 to 1 August 2020. Eurosurveillance. Feb 10, 2022;27(6):2001800. [ CrossRef ]
  • Madhusoodanan J. Health data for all. Nature. May 03, 2022;605(7908):182-183. [ CrossRef ] [ Medline ]
  • Rosenberg ES, Dufort EM, Udo T, Wilberschied LA, Kumar J, Tesoriero J, et al. Association of treatment with hydroxychloroquine or azithromycin with in-hospital mortality in patients with COVID-19 in New York State. JAMA. Jun 23, 2020;323(24):2493-2502. [ https://europepmc.org/abstract/MED/32392282 ] [ CrossRef ] [ Medline ]
  • Teherani M, Kao C, Camacho-Gonzalez A, Banskota S, Shane A, Linam W, et al. Burden of illness in households with severe acute respiratory syndrome coronavirus 2-infected children. J Pediatric Infect Dis Soc. Nov 10, 2020;9(5):613-616. [ https://europepmc.org/abstract/MED/32780809 ] [ CrossRef ] [ Medline ]
  • Lewis N, Chu V, Ye D, Conners E, Gharpure R, Laws R, et al. Household transmission of severe acute respiratory syndrome coronavirus-2 in the United States. Clin Infect Dis. Oct 05, 2021;73(7):1805-1813. [ https://europepmc.org/abstract/MED/33185244 ] [ CrossRef ] [ Medline ]
  • Dawson P, Rabold E, Laws R, Conners E, Gharpure R, Yin S, et al. Loss of taste and smell as distinguishing symptoms of coronavirus disease 2019. Clin Infect Dis. Feb 16, 2021;72(4):682-685. [ https://europepmc.org/abstract/MED/32562541 ] [ CrossRef ] [ Medline ]
  • Grijalva CG, Rolfes MA, Zhu Y, McLean HQ, Hanson KE, Belongia EA, et al. Transmission of SARS-COV-2 infections in households - Tennessee and Wisconsin, April-September 2020. MMWR Morb Mortal Wkly Rep. Nov 06, 2020;69(44):1631-1634. [ https://doi.org/10.15585/mmwr.mm6944e1 ] [ CrossRef ] [ Medline ]
  • Paul LA, Daneman N, Schwartz KL, Science M, Brown KA, Whelan M, et al. Association of age and pediatric household transmission of SARS-CoV-2 infection. JAMA Pediatr. Nov 01, 2021;175(11):1151-1158. [ https://europepmc.org/abstract/MED/34398179 ] [ CrossRef ] [ Medline ]
  • Madewell ZJ, Yang Y, Longini IM, Halloran ME, Dean NE. Household transmission of SARS-CoV-2: a systematic review and meta-analysis. JAMA Netw Open. Dec 01, 2020;3(12):e2031756. [ https://europepmc.org/abstract/MED/33315116 ] [ CrossRef ] [ Medline ]
  • Madewell ZJ, Yang Y, Longini IM, Halloran ME, Dean NE. Household secondary attack rates of sars-CoV-2 by variant and vaccination status: an updated systematic review and meta-analysis. JAMA Netw Open. Apr 01, 2022;5(4):e229317. [ https://europepmc.org/abstract/MED/35482308 ] [ CrossRef ] [ Medline ]
  • Lee SW. Regression analysis for continuous independent variables in medical research: statistical standard and guideline of Life Cycle Committee. Life Cycle. Feb 19, 2022;2 [ CrossRef ]
  • Jing Q, Liu M, Zhang Z, Fang L, Yuan J, Zhang A, et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect Dis. Oct 2020;20(10):1141-1150. [ https://europepmc.org/abstract/MED/32562601 ] [ CrossRef ] [ Medline ]
  • Li F, Li Y, Liu M, Fang L, Dean NE, Wong GWK, et al. Household transmission of SARS-CoV-2 and risk factors for susceptibility and infectivity in Wuhan: a retrospective observational study. Lancet Infect Dis. May 2021;21(5):617-628. [ http://europepmc.org/abstract/MED/33476567 ] [ CrossRef ] [ Medline ]
  • Park YJ, Choe YJ, Park O, Park SY, Kim Y, Kim J, et al. COVID-19 National Emergency Response Center‚ EpidemiologyCase Management Team. Contact tracing during coronavirus disease outbreak, South Korea, 2020. Emerg Infect Dis. Oct 2020;26(10):2465-2468. [ https://doi.org/10.3201/eid2610.201315 ] [ CrossRef ] [ Medline ]
  • Lyngse F, Kirkeby C, Halasa T, Andreasen V, Skov R, Møller F, et al. COVID-19 Transmission Within Danish Households: A Nationwide Study from Lockdown to Reopening. MedRxiv. Preprint posted online September 9, 2020. [ https://www.medrxiv.org/content/10.1101/2020.09.09.20191239v1 ] [ CrossRef ]
  • Maltezou HC, Vorou R, Papadima K, Kossyvakis A, Spanakis N, Gioula G, et al. Transmission dynamics of SARS-CoV-2 within families with children in Greece: A study of 23 clusters. J Med Virol. Mar 26, 2021;93(3):1414-1420. [ https://europepmc.org/abstract/MED/32767703 ] [ CrossRef ] [ Medline ]
  • Posfay-Barbe K, Wagner N, Gauthey M, Moussaoui D, Loevy N, Diana A, et al. COVID-19 in children and the dynamics of infection in families. Pediatrics. Aug 2020;146(2):e20201576. [ CrossRef ] [ Medline ]
  • Thompson H, Mousa A, Dighe A, Fu H, Arnedo-Pena A, Barrett P, et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) setting-specific transmission rates: a systematic review and meta-analysis. Clin Infect Dis. Aug 02, 2021;73(3):e754-e764. [ https://europepmc.org/abstract/MED/33560412 ] [ CrossRef ] [ Medline ]
  • Tanaka M, Marentes Ruiz CJ, Malhotra S, Turner L, Peralta A, Lee Y, et al. SARS-CoV-2 transmission dynamics in households with children, Los Angeles, California. Front Pediatr. 2021;9:752993. [ https://europepmc.org/abstract/MED/35071125 ] [ CrossRef ] [ Medline ]
  • Telle K, Jørgensen SB, Hart R, Greve-Isdahl M, Kacelnik O. Secondary attack rates of COVID-19 in Norwegian families: a nation-wide register-based study. Eur J Epidemiol. Jul 25, 2021;36(7):741-748. [ https://europepmc.org/abstract/MED/34036466 ] [ CrossRef ] [ Medline ]
  • Marks M, Millat-Martinez P, Ouchi D, Roberts CH, Alemany A, Corbacho-Monné M, et al. Transmission of COVID-19 in 282 clusters in Catalonia, Spain: a cohort study. Lancet Infect Dis. May 2021;21(5):629-636. [ CrossRef ]
  • Heald-Sargent T, Muller WJ, Zheng X, Rippe J, Patel AB, Kociolek LK. Age-Related differences in nasopharyngeal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) levels in patients with mild to moderate coronavirus disease 2019 (COVID-19). JAMA Pediatr. Jul 30, 2020 [ CrossRef ] [ Medline ]
  • Yonker LM, Neilan AM, Bartsch Y, Patel AB, Regan J, Arya P, et al. Pediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): clinical presentation, infectivity, and immune responses. J Pediatr. Dec 2020;227:45-52.e5. [ https://europepmc.org/abstract/MED/32827525 ] [ CrossRef ] [ Medline ]
  • Soriano-Arandes A, Gatell A, Serrano P, Biosca M, Campillo F, Capdevila R, et al. COVID-19 Pediatric Disease in Catalonia Research Group. Household Severe Acute Respiratory Syndrome Coronavirus 2 Transmission and Children: A Network Prospective Study. Clinical Infectious Diseases 2021 Sep 15. Sep 15, 2021;73(6):e1269. [ CrossRef ]
  • Zhu Y, Bloxham C, Hulme K, Sinclair J, Tong Z, Steele L, et al. A meta-analysis on the role of children in severe acute respiratory syndrome coronavirus 2 in household transmission clusters. Clin Infect Dis. Jun 15, 2021;72(12):e1146-e1153. [ https://europepmc.org/abstract/MED/33283240 ] [ CrossRef ] [ Medline ]
  • Wang D, Wu X, Li C, Han J, Yin J. The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology. Sci Total Environ. Jun 20, 2022;826:154182. [ https://europepmc.org/abstract/MED/35231530 ] [ CrossRef ] [ Medline ]
  • CDC museum COVID-19 timeline. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/museum/time line/covid19.html [accessed 2022-05-09]
  • Education levels for each state and county in the United States. US Department of Agriculture. URL: https://data.ers.usda.gov/reports.aspx?ID=17829 [accessed 2022-05-09]
  • Karmakar M, Lantz PM, Tipirneni R. Association of social and demographic factors with COVID-19 incidence and death rates in the US. JAMA Netw Open. Jan 04, 2021;4(1):e2036462. [ https://europepmc.org/abstract/MED/33512520 ] [ CrossRef ] [ Medline ]
  • Han J, Yin J, Wu X, Wang D, Li C. Environment and COVID-19 incidence: A critical review. J Environ Sci (China). Feb 2023;124:933-951. [ https://europepmc.org/abstract/MED/36182196 ] [ CrossRef ] [ Medline ]
  • Chung MK, Caboni M, Strandwitz P, D'Onofrio A, Lewis K, Patel CJ. Systematic comparisons between Lyme disease and post-treatment Lyme disease syndrome in the US with administrative claims data. EBioMedicine. Apr 2023;90:104524. [ https://linkinghub.elsevier.com/retrieve/pii/S2352-3964(23)00089-0 ] [ CrossRef ] [ Medline ]
  • Schwartz AM, Kugeler KJ, Nelson CA, Marx GE, Hinckley AF. Use of commercial claims data for evaluating trends in Lyme disease diagnoses, United States, 2010-2018. Emerg Infect Dis. 2021;27(2):499-507. [ https://doi.org/10.3201/eid2702.202728 ] [ CrossRef ] [ Medline ]
  • Mackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, et al. Racial and ethnic disparities in COVID-19-related infections, hospitalizations, and deaths: a systematic review. Ann Intern Med. Mar 2021;174(3):362-373. [ https://www.acpjournals.org/doi/abs/10.7326/M20-6306?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed ] [ CrossRef ] [ Medline ]
  • Yang T, Emily Choi S, Sun F. COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation. Ethn Health. Jan 2021;26(1):11-21. [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, T Leung; submitted 11.11.22; peer-reviewed by DK Yon, JR Medina; comments to author 09.05.23; revised version received 18.07.23; accepted 29.10.23; published 04.01.24

©Ming Kei Chung, Brian Hart, Mauricio Santillana, Chirag J Patel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.01.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

HVAC Linesets Market Insights Research Report [2024-2031] | 122 Pages

“ HVAC Linesets Market ” Research Report Provides Detailed Historical Analysis of Global market for HVAC Linesets from 2017-2023, and provides Extensive Market Forecasts From 2024-2031 By Applications (Residential, Commercial, Industrial) ,Types (Rubber Packing, Plastic Packaging) , and By Regional Outlook. The report presents the research and analysis provided within the HVAC Linesets Market Research is meant to benefit stakeholders, vendors, and other participants in the industry. The HVAC Linesets market is expected to grow annually by magnificent (CAGR 2023 – 2031).

Browse Detailed TOC of HVAC Linesets Market report which is spread across 122 + Pages, Tables and Figures with Charts that provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

Who is the largest manufacturers of HVAC Linesets Market worldwide?

Get a Sample PDF of report –  https://www.precisionreports.co/enquiry/request-sample/20811821

Short Description About HVAC Linesets Market:

The Global HVAC Linesets market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2031. In 2022, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

Market Analysis and Insights: Global and United States HVAC Linesets Market

This report focuses on global and United States HVAC Linesets market, also covers the segmentation data of other regions in regional level and county level.

Due to the COVID-19 pandemic, the global HVAC Linesets market size is estimated to be worth USD 7881.6 million in 2022 and is forecast to a readjusted size of USD 13040 million by 2028 with a CAGR of 8.8Percent during the review period. Fully considering the economic change by this health crisis, by Type, Rubber Packing accounting for Percent of the HVAC Linesets global market in 2021, is projected to value USD million by 2028, growing at a revised Percent CAGR in the post-COVID-19 period. While by Application, Residential was the leading segment, accounting for over percent market share in 2021, and altered to an Percent CAGR throughout this forecast period.

In United States the HVAC Linesets market size is expected to grow from USD million in 2021 to USD million by 2028, at a CAGR of Percent during the forecast period.

Global HVAC Linesets Scope and Market Size

HVAC Linesets market is segmented by region (country), players, by Type and by Application. Players, stakeholders, and other participants in the global HVAC Linesets market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by region (country), by Type and by Application for the period 2017-2028.

For United States market, this report focuses on the HVAC Linesets market size by players, by Type and by Application, for the period 2017-2028. The key players include the global and local players, which play important roles in United States.

Get a Sample Copy of the HVAC Linesets Report 2023

What are the factors driving the growth of the HVAC Linesets Market?

Growing demand for below applications around the world has had a direct impact on the growth of the HVAC Linesets

What are the types of HVAC Linesets available in the Market?

Based on Product Types the Market is categorized into Below types that held the largest HVAC Linesets market share In 2023.

Which regions are leading the HVAC Linesets Market?

  • North America (United States, Canada and Mexico)
  • Europe (Germany, UK, France, Italy, Russia and Turkey etc.)
  • Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
  • South America (Brazil, Argentina, Columbia etc.)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Inquire more and share questions if any before the purchase on this report at – https://www.precisionreports.co/enquiry/pre-order-enquiry/20811821

This HVAC Linesets Market Research/Analysis Report Contains Answers to your following Questions

  • What are the global trends in the HVAC Linesets market? Would the market witness an increase or decline in the demand in the coming years?
  • What is the estimated demand for different types of products in HVAC Linesets? What are the upcoming industry applications and trends for HVAC Linesets market?
  • What Are Projections of Global HVAC Linesets Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export?
  • Where will the strategic developments take the industry in the mid to long-term?
  • What are the factors contributing to the final price of HVAC Linesets? What are the raw materials used for HVAC Linesets manufacturing?
  • How big is the opportunity for the HVAC Linesets market? How will the increasing adoption of HVAC Linesets for mining impact the growth rate of the overall market?
  • How much is the global HVAC Linesets market worth? What was the value of the market In 2020?
  • Who are the major players operating in the HVAC Linesets market? Which companies are the front runners?
  • Which are the recent industry trends that can be implemented to generate additional revenue streams?
  • What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for HVAC Linesets Industry?

HVAC Linesets Market – Covid-19 Impact and Recovery Analysis:

We were monitoring the direct impact of covid-19 in this market, further to the indirect impact from different industries. This document analyzes the effect of the pandemic on the HVAC Linesets market from a international and nearby angle. The document outlines the marketplace size, marketplace traits, and market increase for HVAC Linesets industry, categorised with the aid of using kind, utility, and patron sector. Further, it provides a complete evaluation of additives concerned in marketplace improvement in advance than and after the covid-19 pandemic. Report moreover done a pestel evaluation within the business enterprise to study key influencers and boundaries to entry.

Our studies analysts will assist you to get custom designed info to your report, which may be changed in phrases of a particular region, utility or any statistical info. In addition, we’re constantly inclined to conform with the study, which triangulated together along with your very own statistics to make the marketplace studies extra complete for your perspective.

Final Report will add the analysis of the impact of Russia-Ukraine War and COVID-19 on this HVAC Linesets Industry.

TO KNOW HOW COVID-19 PANDEMIC AND RUSSIA UKRAINE WAR WILL IMPACT THIS MARKET – REQUEST SAMPLE

Detailed TOC of Global HVAC Linesets Market Research Report, 2023-2031

1 Market Overview 1.1 Product Overview and Scope of HVAC Linesets 1.2 Classification of HVAC Linesets by Type 1.2.1 Overview: Global HVAC Linesets Market Size by Type: 2017 Versus 2021 Versus 2031 1.2.2 Global HVAC Linesets Revenue Market Share by Type in 2021 1.3 Global HVAC Linesets Market by Application 1.3.1 Overview: Global HVAC Linesets Market Size by Application: 2017 Versus 2021 Versus 2031 1.4 Global HVAC Linesets Market Size and Forecast 1.5 Global HVAC Linesets Market Size and Forecast by Region 1.6 Market Drivers, Restraints and Trends 1.6.1 HVAC Linesets Market Drivers 1.6.2 HVAC Linesets Market Restraints 1.6.3 HVAC Linesets Trends Analysis

2 Company Profiles 2.1 Company 2.1.1 Company Details 2.1.2 Company Major Business 2.1.3 Company HVAC Linesets Product and Solutions 2.1.4 Company HVAC Linesets Revenue, Gross Margin and Market Share (2019, 2020, 2021 and 2023) 2.1.5 Company Recent Developments and Future Plans

3 Market Competition, by Players 3.1 Global HVAC Linesets Revenue and Share by Players (2019,2020,2021, and 2023) 3.2 Market Concentration Rate 3.2.1 Top3 HVAC Linesets Players Market Share in 2021 3.2.2 Top 10 HVAC Linesets Players Market Share in 2021 3.2.3 Market Competition Trend 3.3 HVAC Linesets Players Head Office, Products and Services Provided 3.4 HVAC Linesets Mergers and Acquisitions 3.5 HVAC Linesets New Entrants and Expansion Plans

4 Market Size Segment by Type 4.1 Global HVAC Linesets Revenue and Market Share by Type (2017-2023) 4.2 Global HVAC Linesets Market Forecast by Type (2023-2031)

5 Market Size Segment by Application 5.1 Global HVAC Linesets Revenue Market Share by Application (2017-2023) 5.2 Global HVAC Linesets Market Forecast by Application (2023-2031)

6 Regions by Country, by Type, and by Application 6.1 HVAC Linesets Revenue by Type (2017-2031) 6.2 HVAC Linesets Revenue by Application (2017-2031) 6.3 HVAC Linesets Market Size by Country 6.3.1 HVAC Linesets Revenue by Country (2017-2031) 6.3.2 United States HVAC Linesets Market Size and Forecast (2017-2031) 6.3.3 Canada HVAC Linesets Market Size and Forecast (2017-2031) 6.3.4 Mexico HVAC Linesets Market Size and Forecast (2017-2031)

7 Research Findings and Conclusion

8 Appendix 8.1 Methodology 8.2 Research Process and Data Source 8.3 Disclaimer

9 Research Methodology

10 Conclusion

Continued….

Purchase this report (Price 4350 USD for a single-user license) – https://www.precisionreports.co/purchase/20811821

Precision Reports is the credible source for gaining the market reports that will provide you with the lead your business needs. At Precision Reports, our objective is providing a platform for many top-notch market research firms worldwide to publish their research reports, as well as helping the decision makers in finding most suitable market research solutions under one roof. Our aim is to provide the best solution that matches the exact customer requirements. This drives us to provide you with custom or syndicated research reports.

PRWireCenter

  • Pectinase Market Analysis Research Report [2024-2031] | 93 Pages
  • Bike Panniers Market Trends Research Report [2024-2031] | 113 Pages
  • Support Catheters Market Growth Research Report [2024-2031] | 93 Pages
  • Blood Clot Retrieval Devices Market Insights Research Report [2024-2031] | 101 Pages
  • Musical Instruments – String Market Share Research Report [2024-2031] | 135 Pages
  • Tire Recycling Line Market Analysis Research Report [2024-2031] | 111 Pages
  • Ostomy Products Market Size Research Report [2024-2031] | 113 Pages
  • Silencers Market Trends Research Report [2024-2031] | 114 Pages
  • Snowskin Mooncake Market Trends Research Report [2024-2031] | 97 Pages
  • Tobramycin Sulphate Market Insights Research Report [2024-2031] | 93 Pages
  • Let’s waltz! Vienna ball season back in full swing
  • ‘Avatar 2’ success proves cinema in post-pandemic ‘resurgence’: Cameron
  • North of Soledar, Ukrainians yearn for peace
  • Carlo Marks talks about his new Hallmark movie ‘The Wedding Veil Inspiration’
  • Musk faces US fraud trial over Tesla tweet
  • Heat home to be renamed Miami-Dade Arena
  • States with the biggest gun industries
  • 25 successful musicians who retired early
  • French Muslim union sues nation’s biggest literary star Houellebecq
  • Iconic child stars of the ’60s

magnt research report journal

  • Entertainment
  • Photography
  • Press Releases
  • Israel-Hamas War
  • Russia-Ukraine War
  • Latin America
  • Middle East
  • Asia Pacific
  • Election 2024
  • AP Top 25 College Football Poll
  • Movie reviews
  • Book reviews
  • Financial Markets
  • Business Highlights
  • Financial wellness
  • Artificial Intelligence
  • Social Media

China sanctions a US research firm and 2 individuals over reports on human rights abuses in Xinjiang

FILE - Chinese Foreign Ministry spokesperson Mao Ning gestures during a press conference at the Ministry of Foreign Affairs in Beijing, on July 26, 2023. China says it is banning a United States research company and two analysts who have reported extensively on claims of human rights abuses committed against Uyghurs and other Muslim minority groups native to the country’s far northwestern region of Xinjiang. (AP Photo/Ng Han Guan, File)

FILE - Chinese Foreign Ministry spokesperson Mao Ning gestures during a press conference at the Ministry of Foreign Affairs in Beijing, on July 26, 2023. China says it is banning a United States research company and two analysts who have reported extensively on claims of human rights abuses committed against Uyghurs and other Muslim minority groups native to the country’s far northwestern region of Xinjiang. (AP Photo/Ng Han Guan, File)

  • Copy Link copied

BEIJING (AP) — China says it is banning a United States research company and two analysts who have reported extensively on claims of human rights abuses committed against Uyghurs and other Muslim minority groups native to the country’s far northwestern region of Xinjiang.

Foreign Ministry spokesperson Mao Ning was quoted as announcing late Tuesday night that Los Angeles-based research and data analytics firm Kharon, its director of investigations, Edmund Xu, and Nicole Morgret, a human rights analyst affiliated with the Center for Advanced Defense Studies, would be barred from traveling to China. Also, any assets or property they have in China will be frozen and organizations and individuals in China are prohibited from making transactions or otherwise cooperating with them.

In a statement on the Ministry of Foreign Affairs website, Mao said the sanctions against the company, Xu and Morgret were retaliation for a yearly U.S. government report on human rights in Xinjiang. Uyghurs and other natives of the region share religious, linguistic and cultural links with the scattered peoples of Central Asia and have long resented the Chinese Communist Party’s heavy-handed control and attempts to assimilate them with the majority Han ethnic group.

In a paper published in June 2022, Morgret wrote, “The Chinese government is undertaking a concerted drive to industrialize the Xinjiang Uyghur Autonomous Region (XUAR), which has led an increasing number of corporations to establish manufacturing operations there. This centrally-controlled industrial policy is a key tool in the government’s efforts to forcibly assimilate Uyghurs and other Turkic peoples through the institution of a coerced labor regime.”

FILE - American flags are displayed together with Chinese flags on top of a trishaw on Sept. 16, 2018, in Beijing. Over the past four decades, U.S. universities have educated millions of Chinese students, many of whom have stayed in the country and become top researchers and distinguished professors. The number of Chinese students in the United States is down, and U.S.-Chinese research collaboration is shrinking, with academics shying away from potential China projects over fears that seemingly minor missteps could end their careers. (AP Photo/Andy Wong, File)

Such reports draw from a wide range of sources, including independent media, non-governmental organizations and groups that may receive commercial and governmental grants or other outside funding.

China has long denied such allegations, saying the large-scale network of prison-like facilities through which passed hundreds of thousands of Muslim citizens were intended only to rid them of violent, extremist tendencies and teach them job skills. Former inmates describe harsh conditions imposed without legal process and demands that they denounce their culture and sing the praises of President Xi Jinping and the Communist Party daily.

China says the camps are all now closed, but many of their former inmates have reportedly been given lengthy prison sentences elsewhere. Access to the region by journalists, diplomats and others is tightly controlled, as is movement outside the region by Uyghurs, Kazaks and other Muslim minorities.

“By issuing the report, the United States once again spread false stories on Xinjiang and illegally sanctioned Chinese officials and companies citing so-called human rights issues,” Mao was quoted as saying.

“If the United States refuses to change course, China will not flinch and will respond in kind,” Mao was quoted as telling reporters at an earlier news briefing.

The U.S. has slapped visa bans and a wide range of other sanctions on dozens of officials from China and the semi-autonomous city of Hong Kong, including the country’s former defense minister, who disappeared under circumstances China has yet to explain. China’s foreign minister also was replaced this year with no word on his fate, fueling speculation that party leader and head of state for life Xi is carrying out a purge of officials suspected of collaborating with foreign governments or simply showing insufficient loyalty to China’s most authoritarian leader since Mao Zedong.

Hong Kong’s government has cracked down heavily on freedom of speech and democracy since China imposed a sweeping national security law in response to massive anti-government protests in 2019.

Neither Xu or Morgret could immediately be reached for comment, and it wasn’t clear what degree of connection, if any, they had with the U.S. government.

We will keep fighting for all libraries - stand with us!

Internet Archive Audio

magnt research report journal

  • This Just In
  • Grateful Dead
  • Old Time Radio
  • 78 RPMs and Cylinder Recordings
  • Audio Books & Poetry
  • Computers, Technology and Science
  • Music, Arts & Culture
  • News & Public Affairs
  • Spirituality & Religion
  • Radio News Archive

magnt research report journal

  • Flickr Commons
  • Occupy Wall Street Flickr
  • NASA Images
  • Solar System Collection
  • Ames Research Center

magnt research report journal

  • All Software
  • Old School Emulation
  • MS-DOS Games
  • Historical Software
  • Classic PC Games
  • Software Library
  • Kodi Archive and Support File
  • Vintage Software
  • CD-ROM Software
  • CD-ROM Software Library
  • Software Sites
  • Tucows Software Library
  • Shareware CD-ROMs
  • Software Capsules Compilation
  • CD-ROM Images
  • ZX Spectrum
  • DOOM Level CD

magnt research report journal

  • Smithsonian Libraries
  • FEDLINK (US)
  • Lincoln Collection
  • American Libraries
  • Canadian Libraries
  • Universal Library
  • Project Gutenberg
  • Children's Library
  • Biodiversity Heritage Library
  • Books by Language
  • Additional Collections

magnt research report journal

  • Prelinger Archives
  • Democracy Now!
  • Occupy Wall Street
  • TV NSA Clip Library
  • Animation & Cartoons
  • Arts & Music
  • Computers & Technology
  • Cultural & Academic Films
  • Ephemeral Films
  • Sports Videos
  • Videogame Videos
  • Youth Media

Search the history of over 867 billion web pages on the Internet.

Mobile Apps

  • Wayback Machine (iOS)
  • Wayback Machine (Android)

Browser Extensions

Archive-it subscription.

  • Explore the Collections
  • Build Collections

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

  • Donate Donate icon An illustration of a heart shape

MAGNT research report

Bookreader item preview, share or embed this item, flag this item for.

  • Graphic Violence
  • Explicit Sexual Content
  • Hate Speech
  • Misinformation/Disinformation
  • Marketing/Phishing/Advertising
  • Misleading/Inaccurate/Missing Metadata

Creative Commons License

This book is available with additional data at Biodiversity Heritage Library .

plus-circle Add Review comment Reviews

Download options.

For users with print-disabilities

IN COLLECTIONS

Uploaded by BHL Australia on January 14, 2018

SIMILAR ITEMS (based on metadata)

Biodiversity Heritage Library

Search help

Download book

Download citation, text sources.

  • Uncorrected OCR - Machine-generated text. May include inconsistencies with the content of the original page.
  • Error-corrected OCR - Machine-generated, machine-corrected text. Better quality than Uncorrected OCR, but may still include inconsistencies with the content of the original page.
  • Manual Transcription - Human-created and reviewed text. For issues concerning manual transcription text, please contact the original holding institution.
  • Table of Contents

magnt research report journal

Review My PDF

Generate my pdf.

If you are generating a PDF of a journal article or book chapter, please feel free to enter the title and author information. The information you enter here will be stored in the downloaded file to assist you in managing your downloaded PDFs locally.

Thank you for your request. Please wait for an email containing a link to download the PDF.

For your reference, the confirmation number for this request is .

magnt research report journal

Join Our Mailing List

Sign up to receive the latest BHL news, content highlights, and promotions.

Help Support BHL

BHL relies on donations to provide free PDF downloads and other services. Help keep BHL free and open!

There was an issue with the request. Please try again and if the problem persists, please send us feedback .

Charles Darwin's Library

IMAGES

  1. MAGNT Research Report (ISSN. 1444-8939) Vol.3 (4). PP. 368

    magnt research report journal

  2. Number of Research Guide's

    magnt research report journal

  3. how to write a scientific report ppt

    magnt research report journal

  4. MAGNT Research Report (IS

    magnt research report journal

  5. MAGNT Research Report (ISSN. 1444-8939) .docx

    magnt research report journal

  6. How To Write A Clinical

    magnt research report journal

VIDEO

  1. Efek Sirkuit Mandalika Warga Sulap Rumahnya Jadi Penginapan Tidak Kalah Dengan Hotel

  2. Korean mekup hack #tendingvideo #viralvideo #shotsvideo #korean #mekup hack 😱💄🤯

  3. イベント中にライバルホストの本音が爆発…喧嘩を売られたホストが衝撃発言

  4. Skibidi Toilet Fanmade Vocoded to Miss The Rage

  5. Research Report Video Essay

  6. Research Report Video Essay

COMMENTS

  1. MAGNT research report : Museums and Art Galleries of the Northern

    Description based on: No. 2 (Sept. 1998) Abstract The Museums and Art Galleries of the Northern Territory research report series is a medium for the dissemination of the results of research undertaken by MAGNT staff in the fields of Natural Sciences, History and Culture. Addeddate 2021-05-17 15:15:50 Associated-names

  2. MAGNT research report

    Summary: The Museums and Art Galleries of the Northern Territory research report series is a medium for the dissemination of the results of research undertaken by MAGNT staff in the fields of Natural Sciences, History and Culture Journal, Magazine, English, 199u Edition: View all formats and editions

  3. The Law of Tort and its Impact on Corporations

    MAGNT Research Report (ISSN. 1444-8939) Vol.3 (3). PP: 239-244, Available at SSRN: https://ssrn.com/abstract=2588357 Download This Paper Open PDF in Browser 0 References 0 Citations Law of tort has a comprehensive history and the development of tort law in relation to corporations cannot be comprehended fully without examining the body of c

  4. MAGNT research report

    MAGNT research report - Catalogue | National Library of Australia Catalogue MAGNT research report Request Order a copy Bib ID: 172917 Format: Journal Description: [Darwin] : Museums and Art Galleries of the Northern Territory v. : ill. ; 30 cm. ISSN: 1447-1981 and 1444-8939 Summary:

  5. PDF MAGNT Research Report (ISSN. 1444-8939)

    Abstract In the current time, experiencing contemporary society's faces many challenges imposed itself on the nature of life, and its method of work.

  6. (PDF) Media Globalization

    Authors: Drsameer O.A Baniyassen United Arab Emirates University Abstract Abstract Globalization refers to the dynamic processes that are developed to break down interactions between individuals,...

  7. Details

    Description based on: No. 2 (Sept. 1998) Each issue has a distinctive title. Also available in electronic format. The Museums and Art Galleries of the Northern Territory research report series is a medium for the dissemination of the results of research undertaken by MAGNT staff in the fields of Natural Sciences, History and Culture.

  8. [1511.09142] Sentiment Analysis on YouTube: A Brief Survey

    Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube.

  9. Emergence of the Concept of Company Law: A Case of Pakistan

    MAGNT Research Report (ISSN. 1444-8939) Vol.3 (3). PP: 252-259, 2015, Available at SSRN: https://ssrn.com/abstract=2588376 Download This Paper Open PDF in Browser 0 References 0 Citations The co-operative trading undertakings were started as early as in the 13th century by way of a formal business through partnership and gilds until the 16th cent

  10. ISSN 1444-8939 (Print)

    Record information. Last modification date: 25/01/2009. Type of record: Confirmed. ISSN Center responsible of the record: ISSN National Centre for Australia. ISSN 1444-8939 (Print) | MAGNT research report.

  11. no.12 (2008:Nov)

    MAGNT research report. By. Museums and Art Galleries of the Northern Territory . Publication Details [Darwin], Museums and Art Galleries of the Northern Territory, Year. 2008. Holding Institution ... If you are generating a PDF of a journal article or book chapter, please feel free to enter the title and author information. ...

  12. Abolition of Death Penalty (Case Study of Pakistan)

    Download This Paper Open PDF in Browser Death penalty also known as Capital punishment is a lawful procedure where a person is put to death as a punishment by court of law for committing murder. Capit

  13. presenting a model for effective school culture in high schools

    MAGNT Research Report (ISSN. 1444-8939) Vol.2 (4):PP. 4138-4147 "Presentation a Model of Effective Culture of School in High schools" (Case study: High schools in Ilam Province) Omid vandad *1, Mostafa niknami 2, Ali delavar3 and Parivash jaafari 4 1 Department of Educational Administration, Science and Research branch, Islamic Azad University, Tehran, Iran.

  14. The Mediating Role of Organizational Learning between Knowledge

    MAGNT Research Report (ISSN. 1444-8939) Vol.2 (Special Issue) PP: 771-787 The Mediating Role of Organizational Learning between Knowledge Management Success Factors and Organizational Innovation: A Conceptual Framework Sayyed Mohsen Allameh, Ali Rezaei, Mahdi Mohammad Bagheri Management Department, University of Isfahan, Isfahan, Iran ...

  15. MAGNT Research Report

    MAGNT RESEARCH REPORT Published by BRIS Journal of Advances in S & T (ISSN:1444-8939) http://brisjast.com/ BRIS Journal publishes high quality research articles that ...

  16. Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A

    MAGNT Research Report (ISSN. 1444-8939) Vol.3 (1). PP: 989-997 Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A Case Study Masoum Farahmandian1, Yaghoub Lotfi2 and Isa Maleki3 1 Urmia University of Medical Science, Urmia, Iran 2 Department of Computer, Boukan Branch, Islamic Azad University, Boukan, Iran 3 Young Researchers ...

  17. PDF MAGNT Research Report (ISSN. 1444-8939) Vol.3 (1). PP: 1107-1113

    The sample of this research is used by kukaran formulation that 330 people considered in it. Measuring tool in this research is questioners that included of 19 questions and at first has Demo Graphic variables

  18. Misuse of Diplomatic Immunities and Law Enforcement of ...

    journal contribution. posted on 2015-06-13, ... Magnt research Report Magnt research Report. Abstract: from the newest discussions in field of diplomatic rights, is misuse of diplomatic immunities. In this paper, at first the concept of immunity is detected and diplomatic rights documents is stated. And then philosophic and legal basis of ...

  19. Journal of Medical Internet Research

    Background: Numerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown.

  20. Claudine Gay and Why Academic Honesty Matters

    Cambridge, Mass. Claudine Gay, the president of my university, is under attack for academic dishonesty. She is charged with several instances of plagiarism, in her dissertation and other published ...

  21. Journal of Medical Internet Research

    Background: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points.

  22. HVAC Linesets Market Insights Research Report [2024 ...

    Due to the COVID-19 pandemic, the global HVAC Linesets market size is estimated to be worth USD 7881.6 million in 2022 and is forecast to a readjusted size of USD 13040 million by 2028 with a CAGR ...

  23. China sanctions a US research firm and 2 individuals over reports on

    Updated 1:04 AM PST, December 27, 2023. BEIJING (AP) — China says it is banning a United States research company and two analysts who have reported extensively on claims of human rights abuses committed against Uyghurs and other Muslim minority groups native to the country's far northwestern region of Xinjiang.

  24. no.3 (Jan 1999)

    Download Contents. no.3 (Jan 1999) MAGNT research report. Uncorrected OCR. Machine-generated text. May include inconsistencies with the content of the original page. Error-corrected OCR. Machine-generated, machine-corrected text. Better quality than Uncorrected OCR, but may still include inconsistencies with the content of the original page.

  25. MAGNT research report : Museums and Art Galleries of the Northern

    The Museums and Art Galleries of the Northern Territory research report series is a medium for the dissemination of the results of research undertaken by MAGNT staff in the fields of Natural Sciences, History and Culture. Addeddate 2018-01-14 15:27:25 Call number MAGNT-research-report-no-8 Call-number MAGNT-research-report-no-8 Foldoutcount 0 Genre

  26. no.13 (2010:Jun)

    MAGNT research report. Close Dialog Text Sources. Page text in BHL originates from one of the following sources: Uncorrected OCR Machine-generated text. May include inconsistencies with the content of the original page. ... Journal Title. MAGNT research report. By. Museums and Art Galleries of the Northern Territory

  27. no.4 (1999:Feb)

    The Biodiversity Heritage Library works collaboratively to make biodiversity literature openly available to the world as part of a global biodiversity community.

  28. no.1 (May 1998)

    MAGNT research report. By. Museums and Art Galleries of the Northern Territory . Publication Details [Darwin], Museums and Art Galleries of the Northern Territory, Year. 1998. Holding Institution ... If you are generating a PDF of a journal article or book chapter, please feel free to enter the title and author information. ...