Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data

Wang, Yue; Zhao, Yunpeng; Pan, Qing

doi:10.1093/bib/bbab406

Cited by 4 publications

(3 citation statements)

References 122 publications

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“…(2) Social network co-occurrence. We imported the co-word matrix into Ucinet V6.0 and used the Netdraw option to draw a social network map of COVID-19 news information [60]. First, we selected "Degree" in the node type to analyze the co-occurrence of high-frequency words and generate a social network map ( Fig 11).…”

Section: Social Network Analysismentioning

confidence: 99%

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)

Su,

Wang

2024

PLoS ONE

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Background and aims The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. Methods We searched Baidu News using the keywords “Novel Coronavirus” and “COVID-19” from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data. Results We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event. Conclusions The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.

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Section: Social Network Analysismentioning

confidence: 99%

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)

Su,

Wang

2024

PLoS ONE

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“…Gene flow-the exchange of genetic material between species-has been observed in a number of systems (Taylor & Larson, 2019) and plays an important role in shaping Earth's biodiversity (Barton, 2001;Bock, 2010;Mallet et al, 2015). Phylogenetic networks are used to directly infer reticulate histories and are vital in informing our understanding of adaptive radiations (Ottenburghs et al, 2016), mimicry patterns (Edelman et al, 2019), and even the phylodynamics of infectious diseases like COVID-19 (Wang et al, 2021). The field has recently seen an explosion of new methods for estimating phylogenetic networks (Elworth et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Distribution of Phylogenetic Networks Generated Under a Birth-Death-Hybridization Process

Justison,

Heath

2024

BSSB

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Gene-flow processes such as hybridization and introgression play important roles in shaping diversity across the tree of life. Recent studies extending birth-death models have made it possible to investigate patterns of reticulation in a macroevolutionary context. These models allow for different macroevolutionary patterns of gene flow events that can either add, maintain, or remove lineages—with the gene flow itself possibly being dependent on the relatedness between species—thus creating complex diversification scenarios. Further, many reticulate phylogenetic inference methods assume specific reticulation structures or phylogenies belonging to certain network classes. However, the distributions of phylogenetic networks under reticulate birth-death processes are poorly characterized, and it is unknown whether they violate common methodological assumptions. We use simulation techniques to explore phylogenetic network space under a birth-death-hybridization process where the hybridization rate can have a linear dependence on genetic distance. Specifically, we measured the number of lineages through time and role of hybridization in diversification along with the proportion of phylogenetic networks that belong to commonly used network classes (e.g., tree-child, tree-based, or level-1 networks). We find that the growth of phylogenetic networks and class membership are largely affected by assumptions about macroevolutionary patterns of gene flow. In accordance with previous studies, a lower proportion of networks belonged to these classes based on type and density of reticulate events. However, under a birth-death-hybridization process, these factors form an antagonistic relationship; the type of reticulation events that cause high membership proportions also lead to the highest reticulation density, consequently lowering the overall proportion of phylogenies in some classes. Further, we observed that genetic distance–dependent gene flow and incomplete sampling increase the proportion of class membership, primarily due to having fewer reticulate events. Our results can inform studies if their biological expectations of gene flow are associated with evolutionary histories that satisfy the assumptions of current methodology and aid in finding phylogenetic classes that are relevant for methods development.

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“…Gene flow—the exchange of genetic material between species—has been observed in a number of systems (Taylor and Larson 2019) and plays an important role in shaping Earth’s biodiversity (Stebbins 1959; Bock 2010; Mallet et al 2016). Phylogenetic networks are used to directly infer reticulate histories and are vital in informing our understanding of adaptive radiations (Ottenburghs et al 2016), mimicry patterns (Edelman et al 2019), and even the phylodynamics of infectious diseases like COVID-19 (Wang et al 2022). The field has recently seen an explosion of new methods for estimating phylogenetic networks (Elworth et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring the distribution of phylogenetic networks generated under a birth-death-hybridization process

Justison

Heath

2022

Preprint

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Gene-flow processes such as hybridization and introgression play important roles in shaping diversity across the tree of life. Recent studies extending birth-death models have made it possible to investigate patterns of reticulation in a macroevolutionary context. These models allow for different macroevolutionary patterns of gene flow events that can either add, maintain, or remove lineages, thus creating complex diversification scenarios. Further, many reticulate phylogenetic inference methods assume specific reticulation structures or phylogenies belonging to certain network classes. However, the distributions of phylogenetic networks under reticulate birth-death processes are poorly characterized and it is unknown whether they violate common methodological assumptions. We use a combination of analytical and simulation techniques to explore phylogenetic network space under a birth-death-hybridization process. Specifically, we measured the number of lineages through time and role of hybridization in diversification along with the proportion of phylogenetic networks that belong to commonly used network classes (e.g., tree-child, tree-based, or level-1 networks). We find that the growth of phylogenetic networks and class membership is largely affected by the assumptions about macroevolutionary patterns of gene flow. In accordance with previous studies, a lower proportion of networks belonged to these classes based on type and density of reticulate events. However, under a birth-death-hybridization process, these factors form an antagonistic relationship; the type of reticulation events that cause high membership proportions also lead to the highest reticulation density, consequently lowering the overall proportion of phylogenies in some classes. Further, we observed that genetic distance-dependent gene flow and incomplete sampling increase the proportion of class membership, primarily due to having fewer reticulate events. Our results can inform studies if their biological expectations of gene flow are associated with evolutionary histories that satisfy the assumptions of current methodology and aid in finding phylogenetic classes that are relevant for methods development.

show abstract

Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data

Cited by 4 publications

References 122 publications

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)

Exploring the Distribution of Phylogenetic Networks Generated Under a Birth-Death-Hybridization Process

Exploring the distribution of phylogenetic networks generated under a birth-death-hybridization process

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