scholarly journals Social network plasticity decreases disease transmission in a eusocial insect

Science ◽  
2018 ◽  
Vol 362 (6417) ◽  
pp. 941-945 ◽  
Author(s):  
Nathalie Stroeymeyt ◽  
Anna V. Grasse ◽  
Alessandro Crespi ◽  
Danielle P. Mersch ◽  
Sylvia Cremer ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Disease Transmission ◽  
Behavioral Changes ◽  
Lasius Niger ◽  
Contact Network ◽  
Efficient Communication ◽  
Network Plasticity ◽  
Pathogen Exposure ◽  
Epidemic Risk

Animal social networks are shaped by multiple selection pressures, including the need to ensure efficient communication and functioning while simultaneously limiting disease transmission. Social animals could potentially further reduce epidemic risk by altering their social networks in the presence of pathogens, yet there is currently no evidence for such pathogen-triggered responses. We tested this hypothesis experimentally in the antLasius nigerusing a combination of automated tracking, controlled pathogen exposure, transmission quantification, and temporally explicit simulations. Pathogen exposure induced behavioral changes in both exposed ants and their nestmates, which helped contain the disease by reinforcing key transmission-inhibitory properties of the colony’s contact network. This suggests that social network plasticity in response to pathogens is an effective strategy for mitigating the effects of disease in social groups.

scholarly journals The Simultaneous Effects of Spatial and Social Networks on Cholera Transmission

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Sophia Giebultowicz ◽  
Mohammad Ali ◽  
Mohammad Yunus ◽  
Michael Emch
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Disease Transmission ◽  
Environmental Variables ◽  
Social Effects ◽  
Spatial Error ◽  
Spatial Factors ◽  
Rural Bangladesh ◽  
Recent Approach ◽  
Health Studies

This study uses social network and spatial analytical methods simultaneously to understand cholera transmission in rural Bangladesh. Both have been used separately to incorporate context into health studies, but using them together is a new and recent approach. Data include a spatially referenced longitudinal demographic database consisting of approximately 200,000 people and a database of all laboratory-confirmed cholera cases from 1983 to 2003. A complete kinship-based network linking households is created, and distance matrices are also constructed to model spatial relationships. A spatial error-social effects model tested for cholera clustering in socially linked households while accounting for spatial factors. Results show that there was social clustering in five out of twenty-one years while accounting for both known and unknown environmental variables. This suggests that environmental cholera transmission is significant and social networks also influence transmission, but not as consistently. Simultaneous spatial and social network analysis may improve understanding of disease transmission.

scholarly journals Diversity and Consequences of Social Network Structure in Toothed Whales

2021 ◽  
Vol 8 ◽  
Author(s):  
Michael N. Weiss ◽  
Samuel Ellis ◽  
Darren P. Croft
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Disease Transmission ◽  
Disease Risk ◽  
Social Systems ◽  
Large Body ◽  
Future Research ◽  
Social Network Structure ◽  
Survival And Reproduction

Toothed whales (suborder Odontoceti) are highly social, large brained mammals with diverse social systems. In recent decades, a large body of work has begun investigating these dynamic, complex societies using a common set of analytical tools: social network analysis. The application of social network theory to toothed whales enables insight into the factors that underlie variation in social structure in this taxon, and the consequences of these structures for survival, reproduction, disease transmission, and culture. Here, we perform a systematic review of the literature regarding toothed whale social networks to identify broad patterns of social network structure across species, common drivers of individual social position, and the consequences of network structure for individuals and populations. We also identify key knowledge gaps and areas ripe for future research. We recommend that future studies attempt to expand the taxonomic breadth and focus on standardizing methods and reporting as much as possible to allow for comparative analyses to test evolutionary hypotheses. Furthermore, social networks analysis may provide key insights into population dynamics as indicators of population health, predictors of disease risk, and as direct drivers of survival and reproduction.

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

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

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

scholarly journals Emotions as infectious diseases in a large social network: the SISa model

2010 ◽  
Vol 277 (1701) ◽  
pp. 3827-3835 ◽  
Author(s):  
Alison L. Hill ◽  
David G. Rand ◽  
Martin A. Nowak ◽  
Nicholas A. Christakis
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Infectious Diseases ◽  
Disease Transmission ◽  
Disease Model ◽  
Average Lifetime ◽  
Human Populations ◽  
Emotional States ◽  
Health States ◽  
Heart Study

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

Social Networks and Business Performance of Igbo Migrant Traders in Ibadan, South-West Nigeria

2014 ◽  
Vol 12 (2) ◽  
Author(s):  
Deborah O. Obor ◽  
Emeka E. Okafor
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Exchange ◽  
Business Performance ◽  
Exploratory Research ◽  
Business Growth ◽  
South West ◽  
Key Informant ◽  
Depth Interviews

This study focused on social networks and business performance among Igbo businessmen in Ibadan, South-west Nigeria through the exploratory research design. Social exchange, social network and social capital theories were employed as theoretical framework. Twenty-six in-depth interviews, key informant interviews and case studies were conducted with purposively selected respondents in four business locations in Ibadan. The results showed that among the factors that facilitated migration of the Igbo to Ibadan were their interest to learn a trade, their inability to attain higher education, and having a relative in Ibadan. The types of social networks available showed that social network was not location bound, as all the respondents belonged to town progressive unions and mutual benefits/cooperative associations. Social networks played vital roles in business performance, including social support, access to loan, business growth and expansion. The main challenges to maintaining adequate social network in business were distrust, envy, unbridled competition, dishonesty and inability to keep terms of agreement. The study concludes that social networks have positively influenced the business performance of migrant Igbo in Ibadan. There is need for the Igbo to strengthen their social networks through honesty, forthrightness, and transparency in all their dealings.

Networks

2020 ◽  
pp. 534-562
Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Analysis ◽  
Labor Markets ◽  
Network Formation ◽  
Labor Market Outcomes ◽  
Negative Consequences ◽  
Social Scientists ◽  
Market Outcomes

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.

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