A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity

Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.

Large time behavior of averaged kinetic models on networks

We are interested in flows on general networks and derive a kinetic equation describing general production, social or transportation networks. Corresponding macroscopic transport equations for large time and homogenized behavior are obtained and studied numerically. This work continues a recent discussion [Averaged kinetic models for flows on unstructured networks, Kinetic Related Models 4 (2011) 1081–1096] and provides additionally explicit equilibrium solutions, second-order macroscopic approximations as well as numerical simulations on a large scale homogenized network.

Kinetic part-feeding models for assembly lines in automotive industries

This paper deals with the modeling of production processes in automotive industries by models based on partial differential equations. The basic idea consists on the derivation of kinetic equations to model production flow on an assembly line. Numerical results based on data of an assembly plant are presented. The work implements a recent discussion for general flow on unstructured networks.