The dynamics of transmission and the dynamics of networks

Abstract

A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]

Transmission risk predicts avoidance of infected conspecifics in Trinidadian guppies

“…Modelling approaches provide one solution to this issue by incorporating the uncertainty associated with the codynamics of network structure and infection into static models, offering insight where the interplay is an empirical unknown (Silk et al., ). However, we have shown that disease can have a quantitative, nonlinear effect on the contact behaviour of social animals, indicating that using dynamic models explicitly incorporating this feedback between infection and behaviour will likely improve predictions (Farine, ). The relationship between β c and β p may also drive evolutionary change in both host and parasite.…”

Dynamic Network of Interactions in the Wildlife-Livestock Interface in Mediterranean Spain: An Epidemiological Point of View

“…One of the most useful applications of dynamic networks in epidemiology is the possibility to simulate and explore the transmission of pathogens on the basis of the different parameters that characterise them like, the probability of transmission, the first individual/s infected or transmission within a specific period of time [24]. In the dynamic network we simulated the transmission of a theoretical pathogen transmitted through both direct and indirect pathways, according to our spatio-temporal definition of direct and indirect interaction, to explore the potential role of wild species in the transmission to cattle.…”

“…Nevertheless, the temporal changes in the interactions structure inevitably affect the dynamic of infection [21,22], by changing the properties of the network, that result in speeding up or slowing down the transmission rates [23]. Thus, to better determine how an infection spreads, moving from a static network to a dynamic one is required [24].…”

Sickness behaviour reduces network centrality in wild vampire bats

“…This sickness-induced ‘social distancing’ can be important for modelling pathogen transmission as a social network changes over time (i.e. a dynamic social network [4]). Tracking the effects of sickness behaviour on a dynamic social network requires large datasets with temporal and spatial resolutions that are high enough to be ecologically useful.…”