Christopher L. Barrett

EpiSimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.

Generation and analysis of large synthetic social contact networks

We describe “first principles” based methods for developing synthetic urban and national scale social contact networks. Unlike simple random graph techniques, these methods use real world data sources and combine them with behavioral and social theories to synthesize networks. We develop a synthetic population for the United States modeling every individual in the population including household structure, demographics and a 24-hour activity sequence. The process involves collecting and manipulating public and proprietary data sets integrated into a common architecture for data exchange and then using these data sets to generate new relations. A social contact network is derived from the synthetic population based on physical co-location of interacting persons. We use graph measures to compare and contrast the structural characteristics of the social networks that span different urban regions. We then simulate diffusion processes on these networks and analyze similarities and differences in the structure of the networks.

Modeling and Simulation of Large Biological, Information and Socio-Technical Systems: An Interaction Based Approach

We describe an interaction based approach for computer modeling and simulation of large integrated biological, information, social and technical (BIST) systems. Examples of such systems are urban regional transportation systems, the national electrical power markets and grids, gene regulatory networks, the World-Wide Internet, infectious diseases, vaccine design and deployment, theater war, etc. These systems are composed of large numbers of interacting human, physical, informational and technological components. These components adapt and learn, exhibit perception, interpretation, reasoning, deception, cooperation and non-cooperation, and have economic motives as well as the usual physical properties of interaction.

Complexity of reachability problems for finite discrete dynamical systems

Sequential Dynamical Systems (SDSs) are a special type of finite discrete dynamical systems that can be used to model simulation systems. We focus on the computational complexity of testing several phase space properties of SDSs. Our main result is a sharp delineation between classes of SDSs whose behavior is easy to predict and those whose behavior is hard to predict. Specifically, we show the following. 1.Several state reachability problems for SDSs are PSPACE-complete, even when restricted to SDSs whose underlying graphs are of bounded bandwidth (and hence of bounded pathwidth and treewidth), and the function associated with each node is symmetric. Moreover, this result holds even when the underlying graph is d-regular for some constant d and all the nodes compute the same symmetric Boolean function. An immediate corollary of this result is a PSPACE-hard lower bound on the complexity of reachability problems for regular generalized 1D-Cellular Automata and undirected systolic networks with Boolean totalistic local transition functions. 2.In contrast, the above reachability problems are solvable in polynomial time for SDSs when the Boolean function associated with each node is symmetric and monotone. The PSPACE-completeness results follow as corollaries of simulation results which show for several classes of SDSs, how one class of SDSs can be efficiently simulated by another (more restricted) class of SDSs. We also prove several structural properties concerning the phase space of an SDS. SDSs are closely related to Cellular Automata (CA), concurrent transition systems, discrete Hopfield networks and systolic networks. This observation in conjunction with our lower bounds for SDSs, yields new PSPACE-hard lower bounds on the complexity of state reachability problems for these models, extending some of the earlier results in [K. Culik II, J. Karhumaki, On totalistic systolic networks, Inform. Process. Lett. 26 (5) (1988) 231-236; P. Floreen, E. Goles, G. Weisbuch, Transient length in sequential iterations of threshold functions, Discrete Appl. Math. 6 (1983) 95-98; P. Floreen, P. Orponen, Complexity issues in discrete Hopfield networks, Research Report No. A-1994-4, Department of Computer Science, University of Helsinki, 1994. Also appears in: I. Parberry (Ed.), Comp. and Learning Complexity of Neural Networks: Advanced Topics, 1999; D. Harel, O. Kupferman, M.Y. Vardi, On the complexity of verifying concurrent transition systems, Inform. and Comput. 173 (2002) 143-161; S.K. Shukla, H.B. Hunt III, D.J. Rosenkrantz, R.E. Stearns, On the complexity of relational problems for finite state processes, in: International Colloquium on Automata Programming and Languages, ICALP, 1996, pp. 466-477; A. Rabinovich, Complexity of equivalence problems for concurrent systems of finite agents, Inform. and Comput. 127 (2) (1997) 164-185].

Cascading failures in multiple infrastructures: From transportation to communication network

This research conducts a systematic study of human-initiated cascading failures in critical inter-dependent societal infrastructures. The focus is on three closely coupled systems: (i) cellular and mesh networks, (ii) transportation networks and (iii) social phone call networks. We analyze cascades that occur in inter-dependent infrastructures due to behavioral adaptations in response to a crisis. During crises, changes in individual behavioral lead to altered calling patterns and activities, which influence the urban transport network. This, in turn, affects the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. We develop interaction-based models in which individuals and infrastructure elements are placed in a common geographic coordinate system. The goal is to study the impact of a chemical plume in a densely populated urban region. Authorities order evacuation of the affected area which leads to change in peoples activity patterns as they are forced to drive home or to evacuation shelters. They also use the wireless networks for coordination among family members and information sharing. These two behavioral adaptations, cause flash-congestion in the urban transport network and the wireless network. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. Finally, we study the criticality and robustness of the various base stations and measure how congestion in the transportation network impacts communication infrastructure.

Engineering Label-Constrained Shortest-Path Algorithms

We consider a generalization of the shortest-path problem: given an alphabet Σ, a graph Gwhose edges are weighted and Σ-labeled, and a regular language L? Σ*, the L-constrained shortest-path problemconsists of finding a shortest path pin Gsuch that the concatenated labels along pform a word of L. This definition allows to model, e. g., many traffic-planning problems. We present extensions of well-known speed-up techniques for the standard shortest-path problem, and conduct an extensive experimental study of their performance with various networks and language constraints. Our results show that depending on the network type, both goal-directed and bidirectional search speed up the search considerably, while combinations of these do not.

Detail in network models of epidemiology: are we there yet?

Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the regions contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the USA.

Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza

This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a persons direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak.

Interactions among human behavior, social networks, and societal infrastructures: A Case Study in Computational Epidemiology

Human behavior, social networks, and the civil infrastructures are closely intertwined. Understanding their co-evolution is critical for designing public policies and decision support for disaster planning. For example, human behaviors and day to day activities of individuals create dense social interactions that are characteristic of modern urban societies. These dense social networks provide a perfect fabric for fast, uncontrolled disease propagation. Conversely, people’s behavior in response to public policies and their perception of how the crisis is unfolding as a result of disease outbreak can dramatically alter the normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. In this chapter, we describe a computer simulation based approach to study these issues using public health and computational epidemiology as an illustrative example. We also formulate game-theoretic and stochastic optimization problems that capture many of the problems that we study empirically.

Modeling the interaction between emergency communications and behavior in the aftermath of a disaster

We describe results from a computer simulation-based study of a large-scale, human-initiated crisis in a densely populated urban setting. We focus on the interaction between human behavior and the communication infrastructure in the aftermath of the crisis. We study the effects of sending emergency broadcasts immediately after the event, advising people to shelter in place, and show that this relatively mild intervention can have a large beneficial impact.