Li-Chiou Chen

BioWar: scalable agent-based model of bioattacks

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.

The impact of countermeasure propagation on the prevalence of computer viruses

Countermeasures such as software patches or warnings can be effective in helping organizations avert virus infection problems. However, current strategies for disseminating such countermeasures have limited their effectiveness. We propose a new approach, called the countermeasure competing (CMC) strategy, and use computer simulation to formally compare its relative effectiveness with three antivirus strategies currently under consideration. CMC is based on the idea that computer viruses and countermeasures spread through two separate but interlinked complex networks - the virus-spreading network and the countermeasure-propagation network, in which a countermeasure acts as a competing species against the computer virus. Our results show that CMC is more effective than other strategies based on the empirical virus data. The proposed CMC reduces the size of virus infection significantly when the countermeasure-propagation network has properties that favor countermeasures over viruses, or when the countermeasure-propagation rate is higher than the virus-spreading rate. In addition, our work reveals that CMC can be flexibly adapted to different uncertainties in the real world, enabling it to be tuned to a greater variety of situations than other strategies.

Characterization of defense mechanisms against distributed denial of service attacks

We propose a characterization of distributed denial of service (DDOS) defenses where reaction points are network-based and attack responses are active. The purpose is to provide a framework for comparing the performance and deployment of DDOS defenses. We identify the characteristics in attack detection algorithms and attack responses by reviewing defenses that have appeared in the literature. We expect that this characterization will provide practitioners and academia insights into deploying DDOS defense as network services.

Model alignment of anthrax attack simulations

This paper describes our experience aligning two simulation models of disease progression after biological attacks. The first model is the Incubation-Prodromal-Fulminant (IPF) model, a variation of the Susceptible-Infected-Recovered (SIR) epidemiological model, and the second is an agent-based model called BioWar. We run Bio War simulations to see whether the results will, at the population level, match the IPF results. We showed that Bio War can generate population level results that are close to IPF. In addition, BioWar outputs emergent properties that cannot be simulated in IPF. This study provides insights for modelers who are developing simulation tools for investigating bioterrorism attacks and for decision makers who use these tools.

Aligning Simulation Models of Smallpox Outbreaks

We aligned two fundamentally different models of smallpox transmission after a bioterrorist attack: A location-explicit multi-agent model (BioWar) and the conventional epidemiological box model, called a SIR model for Susceptible- Infected-Recovered. The purpose of this alignment is part of a greater validation process for BioWar. From this study we were able to contribute to the overall validation of the complex agent based model, showing that, at the minimum, the epidemiological curves produced by the two models were approximately equivalent, both in overall and the time course of infection and mortality. Subtle differences on the model results revealed the impact of heterogeneous mixing in the spread of smallpox. Based on this foundation, we will be able to further investigate the policy responses against the outbreaks of contagious diseases by improving heterogeneous properties of agents, which cannot be simulated in a SIR model.

A Model of Biological Attacks on a Realistic Population

The capability to assess the impacts of large-scale biological attacks and the efficacy of containment policies is critical and requires knowledge-intensive reasoning about social response and disease transmission within a complex social system. There is a close linkage among social networks, transportation networks, disease spread, and early detection. Spatial dimensions related to public gathering places such as hospitals, nursing homes, and restaurants, can play a major role in epidemics [Klovdahl et. al. 2001]. Like natural epidemics, bioterrorist attacks unfold within spatially defined, complex social systems, and the societal and networked response can have profound effects on their outcome. This paper focuses on bioterrorist attacks, but the model has been applied to emergent and familiar diseases as well.