Abstract
The human social world is orders of magnitude smaller than our highly urbanized world might lead us to suppose. In addition, human social networks have a very distinct fractal structure similar to that observed in other primates. In part, this reflects a cognitive constraint, and in part a time constraint, on the capacity for interaction. Structured networks of this kind have a significant effect on the rates of transmission of both disease and information. Because the cognitive mechanism underpinning network structure is based on trust, internal and external threats that undermine trust or constrain interaction inevitably result in the fragmentation and restructuring of networks. In contexts where network sizes are smaller, this is likely to have significant impacts on psychological and physical health risks.
Highlights
The processes whereby contagious diseases or information propagate through communities are directly affected by the way these communities are structured. This has been shown to be the case in primates [1,2,3] and has been well studied in humans in the form of epidemiological [4] and information diffusion models [5]
A 476: 20200446 been the workhorse of most of these models and of many of the models currently used to calculate the value of the R-number used to drive current COVID-19 management strategies
Much of the focus in network dynamics has been on disease propagation
Summary
The processes whereby contagious diseases or information propagate through communities are directly affected by the way these communities are structured This has been shown to be the case in primates [1,2,3] and has been well studied in humans in the form of epidemiological [4] and information diffusion (opinion dynamics or voter) models [5]. Many of the best current models are ‘compartmental models’ which represent structure by the fact that a community consists of households or other small population units [11,12]. In effect, these use spatial structure as a proxy for social structure, which has the advantage of ensuring that the models compute . I explore some of the consequences of this for information and disease propagation in networks, and how networks respond to external threats
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