Hasan Guclu

Modelling disease outbreaks in realistic urban social networks

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.

Suppressing Roughness of Virtual Times in Parallel Discrete-Event Simulations

In a parallel discrete-event simulation (PDES) scheme, tasks are distributed among processing elements (PEs) whose progress is controlled by a synchronization scheme. For lattice systems with short-range interactions, the progress of the conservative PDES scheme is governed by the Kardar-Parisi-Zhang equation from the theory of nonequilibrium surface growth. Although the simulated (virtual) times of the PEs progress at a nonzero rate, their standard deviation (spread) diverges with the number of PEs, hindering efficient data collection. We show that weak random interactions among the PEs can make this spread nondivergent. The PEs then progress at a nonzero, near-uniform rate without requiring global synchronizations.

Proximity networks and epidemics

Disease spread in most biological populations requires the proximity of agents. In populations where the individuals have spatial mobility, the contact graph is generated by the “collision dynamics” of the agents, and thus the evolution of epidemics couples directly to the spatial dynamics of the population. We first briefly review the properties and the methodology of an agent-based simulation (EPISIMS) to model disease spread in realistic urban dynamic contact networks. Using the data generated by this simulation, we introduce the notion of dynamic proximity networks which takes into account the relevant time-scales for disease spread: contact duration, infectivity period, and rate of contact creation. This approach promises to be a good candidate for a unified treatment of epidemic types that are driven by agent collision dynamics. In particular, using a simple model, we show that it can account for the observed qualitative differences between the degree distributions of contact graphs of diseases with short infectivity period (such as air-transmitted diseases) or long infectivity periods (such as HIV).

Scale-Free Overlay Topologies with Hard Cutoffs for Unstructured Peer-to-Peer Networks

In unstructured peer-to-peer (P2P) networks, the overlay topology (or connectivity graph) among peers is a crucial component in addition to the peer/data organization and search. Topological characteristics have profound impact on the efficiency of search on such unstructured P2P networks as well as other networks. A key limitation of scale- free (power-law) topologies is the high load (i.e. high degree) on very few number of hub nodes. In a typical unstructured P2P network, peers are not willing to maintain high degrees/loads as they may not want to store large number of entries for construction of the overlay topology. So, to achieve fairness and practicality among all peers, hard cutoffs on the number of entries are imposed by the individual peers, which limits scale-freeness of the overall topology. Thus, it is expected that efficiency of the flooding search reduces as the size of the hard cutoff does. We investigate construction of scale-free topologies with hard cutoffs and effect of these hard cutoffs on the search efficiency.

Network behavior in thin film growth dynamics

We present a network modeling approach for various thin film growth techniques that incorporates reemitted particles due to the nonunity sticking coefficients. We model re-emission of a particle from one surface site to another one as a network link and generate a network model corresponding to the thin film growth. Monte Carlo simulations are used to grow films and dynamically track the trajectories of re-emitted particles. We performed simulations for normal incidence, oblique angle, and chemical vapor deposition CVD techniques. Each deposition method leads to a different dynamic evolution of surface morphology due to different sticking coefficients involved and different strength of shadowing effect originating from the obliquely incident particles. Traditional dynamic-scaling analysis on surface morphology cannot point to any universal behavior. On the other hand, our detailed network analysis reveals that there exist universal behaviors in degree distributions, weighted average degree versus degree, and distance distributions independent of the sticking coefficient used and sometimes even independent of the growth technique. We also observe that network traffic during high-sticking coefficient CVD and oblique-angle deposition occurs mainly among edges of the columnar structures formed while it is more uniform and short range among hills and valleys of small sticking coefficient CVD and normal-angle depositions that produce smoother surfaces.

Extreme fluctuations in small-world networks with relaxational dynamics

We study the distribution and scaling of the extreme height fluctuations for Edwards-Wilkinson-type relaxation on small-world substrates. When random links are added to a one-dimensional lattice, the average size of the fluctuations becomes finite (synchronized state) and the extreme height diverges only logarithmically in the large system-size limit. This latter property ensures synchronization in a practical sense in small-world coupled multi-component autonomous systems. The statistics of the extreme heights is governed by the Fisher-Tippett-Gumbel distribution.

Extreme fluctuations in noisy task-completion landscapes on scale-free networks

We study the statistics and scaling of extreme fluctuations in noisy task-completion landscapes, such as those emerging in synchronized distributed-computing networks, or generic causally constrained queuing networks, with scale-free topology. In these networks the average size of the fluctuations becomes finite (synchronized state) and the extreme fluctuations typically diverge only logarithmically in the large system-size limit ensuring synchronization in a practical sense. Provided that local fluctuations in the network are short tailed, the statistics of the extremes are governed by the Gumbel distribution. We present large-scale simulation results using the exact algorithmic rules, supported by mean-field arguments based on a coarse-grained description.

Social network analysis: a novel approach to legal research on emergency public health systems.

T he public health system (PHS) involves many agencies and organizations working together. Systems function to connect, coordinate, and provide feedback among separate agents to fulfill specific purposes. On the basis of previous consensus reporting3 and research,4 the PHS is made up of governmental public health agencies, hospitals, communitybased health care providers, law enforcement, faith institutions, emergency medical services, and others. A critical purpose of the PHS is to prevent, protect against, quickly respond to, and recover from emergencies with public health consequences. Statutes, regulations, plans, and protocols define emergency PHSs— for example, by outlining scopes of authority for governmental agencies, mandating mutual responsibilities (such as, communication and reporting) among agencies and community organizations, and establishing when emergency conditions warrant the departure from routine practices. In the effort to understand how laws and policies define the PHS for emergency purposes, traditional legal researchers can borrow from the methods of social network analysts. Previous studies have applied network analysis to communication patterns of workers within local health departments and interrelationships within public health agencies, cooccurrence of words and citation patterns in the US Code, and citation patterns in French legal codes. The network analysis tool might equally well provide a way to analyze the qualities of emergency PHS networks. In preliminary studies, we have combined legal analysis of statutes and policies with network analysis to explore the relationships among agents in state PHSs for emergency purposes. Eleven states were selected for national geographic diversity, variation of population density, and risk profile variation: Alaska, California,

Ad Hoc Limited Scale-Free Models for Unstructured Peer-to-Peer Networks

Several protocol efficiency metrics (e.g., scalability, search success rate, routing reachability and stability) depend on the capability of preserving structure even over the churn caused by the ad hoc nodes joining or leaving the network. Preserving the structure becomes more prohibitive due to the distributed and potentially uncooperative nature of such networks, as in the peer-to-peer (P2P) networks. Thus, most practical solutions involve unstructured approaches while attempting to maintain the structure at various levels of protocol stack. The primary focus of this paper is to investigate construction and maintenance of scale-free topologies in a distributed manner without requiring global topology information at the time when nodes join or leave. We consider the uncooperative behavior of peers by limiting the number of neighbors to a pre-defined hard cutoff value (i.e., no peer is a major hub), and the ad hoc behavior of peers by rewiring the neighbors of nodes leaving the network. We also investigate the effect of these hard cutoffs and rewiring of ad-hoc nodes on the P2P search efficiency.

Networking behavior in thin film and nanostructure growth dynamics

Thin film coatings have been essential in development of several micro and nano-scale devices. To realize thin film coatings various deposition techniques are employed, each yielding surface morphologies with different characteristics of interest. Therefore, understanding and control of the surface growth is of great interest. In this paper, we devise a novel network-based modeling of the growth dynamics of such thin films and nano-structures. We specifically map dynamic steps taking place during the growth to components (e.g., nodes, links) of a corresponding network. We present initial results showing that this network-based modeling approach to the growth dynamics can simplify our understanding of the fundamental physical dynamics such as shadowing and re-emission effects.