Infectious Diseases Have A Great Impact On Morbidity And Mortality Of People Living

(2) A network of friends, colleagues, and other personal contacts. (3) An online community of people with a common interest who use a website or other technologies to communicate with each other and share information or resources. Social Network sites is defined as web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with who they share a connections, and (3) view and navigate their list of connections and those made by others within the system. These social network sites are named as such but not limited to: Blogs, Facebook, Twitter, Myspace, and

In our first unit of sociology, I felt I could relate with the term social network, " The term social network refers to the people who are linked to one another(Henslin,116.)"Social networks include everyone you know. I can relate to social networking because I 'm on a social salsa team. When I joined my dance team , we all had the same passion, the love of dance. After a few years of performing and hanging with the same clique all the time for they have the same interests as me. After a while we all seemed to cluster together and they are now more than just a clique to me they are my fiends.

The epidemilogic triangle describes the relationships and interactions among the infectious agent, the host, and

Recent advancements in data collection is producing big complex data in many areas which can be modeled as graphs - entities (vertices/nodes) and their relationships (edges). They are increasingly being encountered in various fields of study. With this burst in data creation and collection, effective analysis of medium to large graphs is becoming increasingly popular in many fields such as social network analysis, biology, education, etc. With this increasing demand in large graph analytics, visual analysis of graphs has gained a lot of attention.

Brian V. Carolan. (2014). 1: The Social Network Perspective and Educational Research Introduction. In Social Network Analysis and Education: Theory, Methods & Applications. (pp. 3-23). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org.library.gcu.edu:2048/10.4135/9781452270104.n1

As a Registered Nurse, I have the opportunity to collaborate with different people from various departments. My job does not end at bedside; it extends as far as interacting with family members, physicians, pharmacists, nursing supervisor, security officers, etc. My social network map (Figure 1) reflects my interactions with fellow employees at the hospital. This map reflects two-days time sample. My map includes a legend that explains each interaction. My interactions include phone, face-to-face, e-mail, electronic (IT), as well as individuals that I interact with both on the phone and face-to-face basis. On this diagram, I used job titles instead of actual names in

Contact network is inherently dynamic in nature, and several factors including seasonal, environmental and anthropogenic variables may influence the structure of contact networks (and consequently, the dynamics of infectious diseases that spread through these networks).

According to Burt, strategic “structural holes” within networks can diversify networks to include not only the strong connections between homogenous nodes, but weaker ties to more heterogenous nodes and networks. Structural holes are defined as “the separation between nonredundant contacts… a relationship of nonredundancy between two contacts… the two contacts provide network benefits that are in some degree additive rather than overlapping” (Burt 1992, 18). In other words, strategic weak ties in a network have the possibility of connecting one to a relatively heterogeneous set of contacts. Burt provides a mechanism through which mostly White primary networks of faculty members can be transformed to reach other, potentially “hidden” networks of scholars of

Epidemiologists use a tool called, epidemiologic triangle to help understand infectious disease. “The epidemiologic triangle is a traditional model that characterizes infectious disease causation” (Merrill, 2017, p. 178). The triangle consists of an external agent, a susceptible host, and an environment that brings the host and the agent together. The agent, or the “what” of the Triangle, is the cause of the disease. The host or the “who” of the Triangle, can be the organism that gets sick, as well as any animal carrier (including insects and worms) that may or may not get sick. The environment, or the “where” of the Triangle, is the favorable surroundings and conditions external to the host that cause or allow the disease to be transmitted.

In a social environment, individuals can be both products and creators. More specifically, individuals are part of a complex system of social relations, which determine their socio-economic choices, and affect the market outcomes. People’s opinions, which products to buy, how much to invest in their retirement funds, commit crimes, etc. can all be influenced by friends and acquaintances. The density, segregation and the central positions of a relationship network are the key factors determining how information spreads and how people behave. Over the last few decades, economists and social science scholars have contributed to the research on how social interactions shape individuals’ behavior and how social networks strengthen policy-related goals with its unique mechanisms.

Comparisons of interaction networks in our database revealed differences between the network structures of relatively solitary, fission-fusion, social and socially hierarchical species. Do these differences in network measures also influence the disease costs of social organization? To answer this question, we performed SIR disease simulations through the social networks in our database. Three measures relevant to disease management were used to summarize the simulations: (a) Proportion of individuals infected in the network (outbreak size), that estimates the total burden of mortality (or morbidity), (b) the duration of disease spread (outbreak duration), which estimates how quickly infection spreads through the networks and therefore time duration within which disease control/management interventions have to be employed, and (c) epidemic

One major change in technology has been the increase in international travel. According to Weiss and McMichael (2004), “The speed of modern air travel works wonders for the dispersal of infectious diseases” (p. S74). Church (2004) adds to this by stating, “From an infectious disease perspective, air travel makes the world a global village because ‘microbial traffic’ follows the human host” (p. 564). Smolinski, Hamburg, and Lederberg (2003) explain that international travel now means that infectious diseases have no borders and has played a major role in spreading infectious diseases across the world. They also expand on the fact that the travel process and all the interactions that occur while traveling aid in the spread of diseases globally. Economic globalization and technology has also led to many changes in agriculture that are contributing to the rise of infectious diseases. Smolinski, Hamburg, and Lederberg (2003) state, “International trade in food and animal agriculture has increased markedly as an important aspect of globalization. The United States and other countries now enjoy more goods from more countries than ever before” (p. 68). Church (2004) explains that this is a problem by writing, “Food-borne and waterborne illnesses also may be imported from other countries” (p. 568). He also brings up the problem that the new practice of mass-production means that contamination and outbreaks now affect many more people. Weiss and McMichael (2004) expand on this by explaining that the agricultural practice of intense, single-species farming will also mean larger-scale epidemics when problems occur. Lastly, Lashley (2006) adds to these ideas by discussing how globalization has led to an increased demand of exotic foods year-round and this demand brings with it new infectious diseases. Not only do technological and economic growth contribute to the rise of

I am a Doctor of Nursing Practice (DPN) graduate student at Brandman University located in the city of Irvine, California. I used a three-day sample of my contacts in my workplace, to create a social network map (Figure 1). The data collection time is representative of my daily work time as a Nurse Practitioner at Clinicas de Salud del Pueblo, Inc. My

The roots of theoretical constructs of SNA in graph theory has led to the use of mathematical concepts being borrowed and built upon to suit its needs. As mentioned before, a graph is a collection of nodes, strung together by edges. Hence, to get from one node to the next, one needs to travel along the edges. The number of edges traversed to reach from one node to another forms the notion of distance in graphs. More specifically, the geodesic distance, d(u,v), between two nodes is defined as the length of the shortest path between them (Bouttier et al. 2003). The conceptualization of ‘distance’ as the number of intermediate edges between nodes, capture the way nodes are embedded in the network (Hanneman & Riddle 2005, p.77). In terms of a social systems, being friends with an important person is always beneficial, as it potentially makes you ‘closer’ to many people in the network [Needs explanation?]. In SNA, the edges connecting two vertices are usually un-weighted. However, in certain application scenarios, the edges may have weights associated with them to represent factors like strength of a tie, or probability of forming a tie, or in case of spatial social networks, the geographic distance between the nodes. The weights on the edges adds another layer to the conceptualization of distance, and the geodesic distance calculation then, needs to account for the weights of the edges. If there is no path connecting the two vertices, i.e., if they belong to different connected

It is important to think about network density while managing networks and getting desired results of those. I have been able to keep full network density while building and managing networks with the scholars working in academia. Comparing to that, networks with practitioners, community workers and other professionals are not that much dense. It is quite natural for me to have more density in academic networks as I work in academia and prefer to build cross relationships with other people in