Rosanna C. Barnard

TL;DR: It is shown that, for a given E/I ratio, networks with high levels of inter-inhibitory clustering are more likely to experience oscillatory behaviour than networks with low levels, and the network attributes which characterise each global behaviour type produced by the model are investigated.

TL;DR: The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread and here, heterogeneities are incorporated into a model by considering a dual-layer static–dynamic multiplex network.

TL;DR: In this paper, the authors estimate the health impact of COVID-19 vaccination alongside benefit-risk assessment of different dosing intervals for low and middle-income countries of Europe.

TL;DR: This chapter uses a published model of activity-dependent growth to show that the ratio between the number of excitatory and inhibitory neurons (E/I ratio) alone cannot accurately predict system behaviour but rather it is the combination of this ratio and the underlying spatial arrangement of neurons that determine both activity and structure of the resulting network.

TL;DR: In this paper, the authors focus on a class of models known as pairwise models and exploit the presence of fast variables and using some standard techniques from perturbation theory to obtain the epidemic threshold analytically.