Information networks for disease: commonalities in human management networks and within-host signalling networks

Abstract

Network models of human epidemics can often be improved by including the effects of behaviour modification in response to information about the approach of epidemics. Similarly, there are opportunities to incorporate the flow of information and its effects in plant disease epidemics in network models at multiple scales. (1) In the case of human management networks for plant disease, each node of a network has four main components: plant communities, microbial communities, human information (among researchers, extension agents, farmers, and other stakeholders), and environmental conditions, along with their interactions. The links between nodes, representing the rate of movement between them, have three parts: the rates for plant materials, the rates for microbes, and the rates for information. Network resilience for information flow is an important goal for such systems. Game theory can provide insights into how human agents decide how to invest their efforts in strengthening information networks, and how policies can support more resilient networks. (2) For the case of withinplant signalling networks, each node has a comparable set of four main components: plant signals (often in the form of phytohormones) and development status, microbial communities and plant disease status, microbial signals (often in the form of quorum sensing molecules), and micro-environmental conditions, along with their interactions. In effect, human information is replaced by plant signals and microbial signals in this second model. The links between nodes have three parts: the rates for microbes, the rates for microbial signals (which may move separately from the microbes, themselves), and the rates for plant signals. Understanding how to enhance adaptive plant signalling networks and microbial signalling networks that support plant productivity, and disrupt microbial signalling networks that contribute to pathogenicity, will be an important step for improved disease management.

The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management

“…We did not explore the effects of neighboring locations on yield loss at a particular location. This could be evaluated using more mechanistic network models of pathogen or pest movement through space (Moslonka-Lefebvre et al, 2011), and potentially network models for the movement of opinion that modifies decision-making (Garrett, 2012).…”

Laurel Wilt in Natural and Agricultural Ecosystems: Understanding the Drivers and Scales of Complex Pathosystems

“…Multilayer networks integrate linked processes, such as networks of the spread of information about disease and linked networks for the spread of the disease and/or its vectors [180,[184][185][186]. Multilayer networks can provide analyses about how networks interact to produce system level outcomes.…”

Climate change and plant health: designing research spillover from plant genomics for understanding the role of microbial communities

“…They may alter plant uptake of nutrients, potentially affecting nutrient stress as well as susceptibility to infectious disease. They may alter plant susceptibility through processes such as induced systemic resistance and systemic acquired resistance (Vallad & Goodman, 2004;van Loon, 2007), with potentially complex network structures for information related to microbial associations (Garrett, 2012). The many ways in which microbial communities can influence these systems are an active area of study, but much remains to be known about what optimal levels of induction may be for plants, and when 'overstimulation' of these systems may have a net cost to plants.…”