Amazoniac
Member
This is regarding "synergistic coinfections", that spread better and faster on closed/less dynamic groups, and from that infected group sucessively to other groups creating an epidemic. On a dynamic environment/community, there are many factors affecting, that can hinder the spread of the disease, including adding enough time to the infected ones to recover before they infect the rest or simply not providing enough exposure for the pathogen to thrive. But on closed communities the contact is constant and it spreads from the weaker people to the robust. Which is why staying in hospitals is so dangerous; people that are there in general are already weak, and it's a closed system. The chaos/dispersing favors individuality (like living on a metropolis) because in grouping you basically incorporate the essence of it, same for infections, you tend to be more susceptible by them. In the case of synergistic infections, one amplifies the other. The guys treat people as nodes [ex.: "arrangement of nodes leads to the aggregation (or clustering) of nodes into well-connected groups, representing for example a person’s family or workplace."] and links as interactions, they attended classes at the tyw Institute of Techmology.
On more thing that it's worth mentioning is that if a susceptible infected person is introduced in a closed group of robust and healthy people, the infection probably won't have much chance.
http://www.pnas.org/content/112/33/10551.full.pdf
"Typically, specific structural properties (average degree, network size, clustering) are explored in isolation. It remains a strong (and potentially dangerous) assumption that results obtained with different models exploring distinct structural properties will give the same results when combined with other models exploring different properties. Disease transmission is a nonlinear problem with features of the propagation itself interacting in complex ways. In this paper, we focus on combining two much studied phenomena — realistic clustering of contact structure and the interaction of respiratory pathogens (influenza and PC pneumonia) — and show that a combination of these two phenomena leads to behavior that is unexpected given previous studies."
"We find that synergistic coinfections can lead to faster disease spread on clustered networks than on an equivalent random network, contrary to previous studies considering single infections (18)."
"We find that although clustering slows down the propagation of noninteracting diseases, it speeds up the propagation of synergistically interacting diseases. This result is important for network models of disease: random networks are considered worst case scenarios for the speed of disease propagation (18, 21), implying that models can justify working in a random network paradigm. However, this is clearly not always the case in the presence of interacting diseases with synergistic effect."
"Both the infector and the infectee can modify transmission rates: a coinfected individual may have increased symptoms, such as coughing or sneezing or higher bacterial or viral loads of each pathogen, which may increase the transmission, and the infectee susceptible to one of the pathogens (disease i) may have a compromised immune response due to infection with the other pathogen (disease j)."
"In the context of diseases that spread heavily in daycares and schools, this means that a small difference in the clustering of contacts could translate to a difference between no outbreak and a complete contagion for interacting pathogens such as influenza and PC pneumonia."
"Here we demonstrated that synergistic coinfections, such as pneumonia caused by S. pneumoniae and influenza, may actually spread faster and farther on clustered networks than on random networks. This result is similar to the recent observation that behaviors or opinions can propagate more rapidly in clustered social networks than in their random equivalent due to social reinforcement (34, 35). Our model thus suggests that we could also expect to see faster transmission on clustered networks in the context of diseases requiring multiple exposures before infection, which can also lead to discontinuous phase transitions (33)."
"We showed that network clustering facilitates synergistically interacting diseases because tight clustering keeps the diseases together."
On more thing that it's worth mentioning is that if a susceptible infected person is introduced in a closed group of robust and healthy people, the infection probably won't have much chance.
http://www.pnas.org/content/112/33/10551.full.pdf
"Typically, specific structural properties (average degree, network size, clustering) are explored in isolation. It remains a strong (and potentially dangerous) assumption that results obtained with different models exploring distinct structural properties will give the same results when combined with other models exploring different properties. Disease transmission is a nonlinear problem with features of the propagation itself interacting in complex ways. In this paper, we focus on combining two much studied phenomena — realistic clustering of contact structure and the interaction of respiratory pathogens (influenza and PC pneumonia) — and show that a combination of these two phenomena leads to behavior that is unexpected given previous studies."
"We find that synergistic coinfections can lead to faster disease spread on clustered networks than on an equivalent random network, contrary to previous studies considering single infections (18)."
"We find that although clustering slows down the propagation of noninteracting diseases, it speeds up the propagation of synergistically interacting diseases. This result is important for network models of disease: random networks are considered worst case scenarios for the speed of disease propagation (18, 21), implying that models can justify working in a random network paradigm. However, this is clearly not always the case in the presence of interacting diseases with synergistic effect."
"Both the infector and the infectee can modify transmission rates: a coinfected individual may have increased symptoms, such as coughing or sneezing or higher bacterial or viral loads of each pathogen, which may increase the transmission, and the infectee susceptible to one of the pathogens (disease i) may have a compromised immune response due to infection with the other pathogen (disease j)."
"In the context of diseases that spread heavily in daycares and schools, this means that a small difference in the clustering of contacts could translate to a difference between no outbreak and a complete contagion for interacting pathogens such as influenza and PC pneumonia."
"Here we demonstrated that synergistic coinfections, such as pneumonia caused by S. pneumoniae and influenza, may actually spread faster and farther on clustered networks than on random networks. This result is similar to the recent observation that behaviors or opinions can propagate more rapidly in clustered social networks than in their random equivalent due to social reinforcement (34, 35). Our model thus suggests that we could also expect to see faster transmission on clustered networks in the context of diseases requiring multiple exposures before infection, which can also lead to discontinuous phase transitions (33)."
"We showed that network clustering facilitates synergistically interacting diseases because tight clustering keeps the diseases together."
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