"Social factors...regulate the patterns of interpersonal contact and thus the structure within which transmission is channeled." -- Martina Morris in "Epistemology and Social Networks: Modeling Structured Diffusion" (1994) in Stanley Wasserman and Joseph Galaskiewicz's "Advances in Social Network Analysis: Research in the Social and Behavioral Sciences" (Sage Publications, Inc.)
(Fine, Eames, & Heymann, 2011, pp. 911 - 916)
A number of features of diseases and the biologic agents that lead to disease are of concern to epidemiologists. One of these is how virulent a disease agent is in terms of its destructive potential. Another aspect is how contagious that agent is and its various potential routes of transmission. The initial prevalence of a disease in specific parts of a population affects how it disseminates and moves through the population. Epidemiologists and those who work in public health must pay attention to the specific indicators of health in the subnetwork level because that is where first indicators of a disease outbreak tend to show up.
If the reproduction rate of a disease (R) is more than one, that is the mathematical condition for an epidemic. People known as "super spreaders" tend to have a much higher reproduction rate for a disease than others. They are highly infective.
Too often, people assume that certain types of infectious diseases are "distant dangers," when in fact the spread of a pathogen in a population is much wider. (This is a social network insight.) Contagious diseases in small-world networks (and the global connected world) cuts across boundaries and sub-groups. From a social network point-of-view, the spread of disease among an already infected population ("infecteds") is less risky (network-wise) than the spread of that disease into susceptible other populations. Research into scale-free networks that are based on power-law structures means that much greater volatility is possible. Sub-networks or sub-clusters may act as incubators to disease which may then jump into a larger population. It is in the interests of society to keep the "stable mixing groups" as healthy as possible but also to keep nodes that would bridge infections and disease to other parts of the population as inactive as possible.
If unchallenged, such infections may reach a percolation stage and become an epidemic or pandemic. In theory, the effort is to control the "disease front" where the infective individuals might interface with susceptible populations. (Efforts at international airports to stop people with high fevers and potential signs of avian flu in the 2003 outbreak and occasionally thereafter are an effort at limiting just such weak links that bridge infected populations with non-infected. Airborne pathogens like influenza viruses, which can also transfer via fomite, are of especial concern because of how highly contagious they are. There are debates on how effective this effort is. The risks of so-called "global cascades" are real and are threats.) The general strategy is to use a variety of tools--law enforcement, medical interventions, behavioral interventions, informational and public relations work--to slow the growth of infectious diseases, to isolate them, and to keep them in control. If it is possible to eradicate the pathogenic agent, then that is done (although humanity has only been able to really stamp out smallpox). Cascades are of especial concern in scale-free networks (whose distributions display power laws) because in such networks, there are particular nodes that are highly influential on other members of the social network. If one such influential node is converted or infected, that node may result in the infections of many others in "cascades."
The reverse concept is the concept of developing "herd immunities." This concept involves the vaccination of a large proportion of a population (or herd) as a measure of protection for those who have not developed immunity. If a sufficient number of a population are immunized, that lowers the probability of an infection cutting a wide swath into the population and sparking a potential epidemic or pandemic. Further, having an immunized population may lead to a decline in the incidence of infection.
social networks testing (to understand diseases)
"Social Networks Testing": Using Social Networks to Reach People for Healthcare (Centers for Disease Control and Prevention / CDC)
With some diseases that may spread through casual contact, like the common flu, the assumptions of social "random mixing" may be used to model the spread. Unintentional contacts (and even unnoticed contacts) are sufficient conditions for influenza to find a foothold in a population and burn through the individuals. However, this model does not apply to diseases that require a different form of close contact for transmission.
Vega and Ghanem (2007) observe: "Sexual behavior, in particular, has strong social components that involve a web of social relations, expectations, issues regarding confidence in one's abilities, beliefs about risk, and the perceived severity of STIs and their sequelae." (p. 142) What that means is that health interventions have to consider bio-behavioral interventions to decrease disease transmission.
When HIV-AIDS first came to the awareness of the general public in the early 1980's, there was research done that showed that one index patient was linked to spreading HIV to hundreds of others (through social contact-tracing). Epidemiologists will track-back various interconnections between people to try to understand how disease entered a population and spread. The spread of sexually transmitted diseases has been modeled using "pure drive" models in which individuals (nodes) have a certain number of quota to achieve. If a certain target group is less available, they will substitute others from a different group to meet the partner need to fulfill the quota (Morris, 1994, p. 39). People respond to the availability of others in the social environment.
The following is a non-narrated simulation of a fictitious virus burning through a population. Please note the parameters set at the left. This model was created using the open-source NetLogo software. There are citations below. This is included here to show a more dynamic visualization than a cross-sectional node-link still.
For more about the above model (about which you saw just a small snippet), please see the attached Word or .pdf files from the open-source NetLogo Model Library.
Virus on a Network References
(Section) References
Fine, P., Eames, K., & Heymann, D.L. (2011). "'Herd immunity': A rough guide." Clinical Infectious Diseases: 52(7), 911 - 916. Oxford Journals.
doi: 10.1093/cid/cir007."Herd immunity." Wikipedia.
Morris, M. (1994). "Epidemiology and social networks: Modeling structured diffusion." In Stanley Wasserman and Joseph Galaskiewicz's "Advances in Social Network Analysis." Thousand Oaks: Sage Publications. 26 - 52.
Vega, M.Y. & Ghanem, K.G. (2007). STD prevention communication: Using social marketing techniques with an eye on behavioral change. In Behavioral Interventions for Prevention and Control of Sexually Transmitted Diseases. S.O. Aral & J.M. Douglas, Eds. J.A. Lipshutz, Assoc. Ed. New York: Springer Science + Business Media, LLC. 142. - 169.
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