Modeling Infectious Diseases Using Global Stochastic Cellular Automata

Mikler, Armin R.; Venkatachalam, Sangeeta; Abbas, Kaja

doi:10.1142/s0218339005001604

Cited by 61 publications

(49 citation statements)

References 15 publications

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“…In general, there are three different states for each individual in epidemic modeling: Epidemic models are usually classified into three categories: deterministic models [53], stochastic models [54], and spatialtemporal models [55], [56].…”

Section: Generic Epidemic Modelingmentioning

confidence: 99%

Smartphone Malware and Its Propagation Modeling: A Survey

Peng

Yang

2014

IEEE Commun. Surv. Tutorials

153

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Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we enumerate the possible damage caused by smartphone malware. In the second part, we focus on smartphone malware propagation modeling. In order to understand the propagation behavior of smartphone malware, we recall generic epidemic models as a foundation for further exploration. We then extensively survey the smartphone malware propagation models. At the end of this paper, we highlight issues of the current smartphone malware propagation models and discuss possible future trends based on our understanding of this topic.

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Section: Generic Epidemic Modelingmentioning

confidence: 99%

Smartphone Malware and Its Propagation Modeling: A Survey

Peng

Yang

2014

IEEE Commun. Surv. Tutorials

153

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“…2 representing the Euclidean norm. This parameter may be defined also for competent/non competent hosts when migration is involved in hosts dynamics.…”

Section: Model Formulationmentioning

confidence: 99%

“…In this context, cellular automata (CA) approach offers more realistic models which incorporate spatial parameters to reflect the heterogeneous real environment [7,10,11,20]. Several works dealing with epidemic CA models have been developped [2,4,11,20]. In [6], a deterministic CA model involving host and vector populations has been developed.…”

Section: Introductionmentioning

confidence: 99%

The Basic Reproduction Number for Chagas Disease Transmission Using Cellular Automata

Cissé

Yacoubi

Gourbière

2014

Lecture Notes in Computer Science

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Abstract. This paper presents mathematical and numerical results for a cellular automaton model describing the transmission dynamics of Chagas disease in both homogeneous and heterogeneous environments. The basic reproduction number R0 which integrates factors that determine whether the pathogen can establish or not will be computed using the next-generation matrix approach. The simulation results show the effect of landscape heterogeneity in the vector transmission.

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“…Naive cellular automata are impeded by a limited neighborhood, and the social interactions based on demographics are not readily incorporated. The authors have introduced the global stochastic cellular automata paradigm, addressing the issue of limited neighborhood in a classical CA (Mikler et al 2005). In order to overcome the limitations posed by naive cellular automata, we introduce GSFM, which incorporates the demographics of location and population density.…”

Section: Approaches To Modeling Epidemicsmentioning

confidence: 99%

Decisions under uncertainty: a computational framework for quantification of policies addressing infectious disease epidemics

Mikler

Venkatachalam

Ramisetty-Mikler

2007

Stoch Environ Res Risk Assess

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Emerging infectious diseases continue to place a strain on the welfare of the population by decreasing the population's general health and increasing the burden on public health infrastructure. This paper addresses these issues through the development of a computational framework for modeling and simulating infectious disease outbreaks in a specific geographic region facilitating the quantification of public health policy decisions. Effectively modeling and simulating past epidemics to project current or future disease outbreaks will lead to improved control and intervention policies and disaster preparedness. In this paper, we introduce a computational framework that brings together spatio-temporal geography and population demographics with specific disease pathology in a novel simulation paradigm termed, global stochastic field simulation (GSFS). The primary aim of this simulation paradigm is to facilitate intelligent what-if-analysis in the event of health crisis, such as an influenza pandemic. The dynamics of any epidemic are intrinsically related to a region's spatio-temporal characteristics and demographic composition and as such, must be considered when developing infectious disease control and intervention strategies. Similarly, comparison of past and current epidemics must include demographic changes into any effective public health policy for control and intervention strategies. GSFS is a hybrid approach to modeling, implicitly combining agent-based modeling with the cellular automata paradigm. Specifically, GSFS is a computational framework that will facilitate the effective identification of risk groups in the population and determine adequate points of control, leading to more effective surveillance and control of infectious diseases epidemics. The analysis of past disease outbreaks in a given population and the projection of current or future epidemics constitutes a significant challenge to Public Health. The corresponding design of computational models and the simulation that facilitates epidemiologists' understanding of the manifestation of diseases represents a challenge to computer and mathematical sciences.

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Modeling Infectious Diseases Using Global Stochastic Cellular Automata

Cited by 61 publications

References 15 publications

Smartphone Malware and Its Propagation Modeling: A Survey

Smartphone Malware and Its Propagation Modeling: A Survey

The Basic Reproduction Number for Chagas Disease Transmission Using Cellular Automata

Decisions under uncertainty: a computational framework for quantification of policies addressing infectious disease epidemics

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