Global Stochastic Contact Modeling of Infectious Diseases

Mikler, Armin R.; Bravo-Salgado, Angel; Corley, Courtney D.

doi:10.1109/ijcbs.2009.84

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Several methods for simulating epidemics computationally are based on the SEIR model of epidemics [15,19,16,10]. In the SEIR model, the population is divided into four classes: susceptible individuals, who can become infected; exposed or latent individuals, who have been infected but are not capable of spreading the infection; infectious individuals, who can spread the disease to susceptible individuals; and recovered individuals, who can no longer be infected.…”

Section: Computational Simulation Of Epidemicsmentioning

confidence: 99%

“…In agent-based models, each individual within the population is simulated. Such models, which are often implemented using cellular automata [16,15,8], are useful because they provide insights into the progression of the disease. However, they are impractical when large populations need to be simulated due to space required for information about each agent in the simulation.…”

Section: Computational Simulation Of Epidemicsmentioning

confidence: 99%

“…al. have developed multiple stochastic models of disease spread in a population, which use the standard SEIR(S) model [15,19]. Some of these models, such as their Global Stochastic Cellular Automata model [16], can be adapted to the problem of vaccine allocation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A method for reducing the severity of epidemics by allocating vaccines according to centrality

Drewniak

Helsing

Mikler

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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One long-standing question in epidemiological research is how best to allocate limited amounts of vaccine or similar preventative measures in order to minimize the severity of an epidemic. Much of the literature on the problem of vaccine allocation has focused on influenza epidemics and used mathematical models of epidemic spread to determine the effectiveness of proposed methods. Our work applies computational models of epidemics to the problem of geographically allocating a limited number of vaccines within several Texas counties. We developed a graph-based, stochastic model for epidemics that is based on the SEIR model, and tested vaccine allocation methods based on multiple centrality measures. This approach provides an alternative method for addressing the vaccine allocation problem, which can be combined with more conventional approaches to yield more effective epidemic suppression strategies. We found that allocation methods based on in-degree and inverse betweenness centralities tended to be the most effective at mitigating epidemics.

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Section: Computational Simulation Of Epidemicsmentioning

confidence: 99%

Section: Computational Simulation Of Epidemicsmentioning

confidence: 99%

See 1 more Smart Citation

A method for reducing the severity of epidemics by allocating vaccines according to centrality

Drewniak

Helsing

Mikler

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Modeling malaria

Bravo-Salgado

Beckham

Mikler

2011

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Autómata Celular Estocástico paralelizado por GPU aplicado a la simulación de enfermedades infecciosas en grandes poblaciones

2012

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Un gran número de áreas de la ciencia están siendo beneficiadas por la reducción de tiempo de cómputo gracias al uso de las Unidades Gráficas de Proceso (GPU). En el caso de la Epidemiología, tales unidades agilizan la simulación de escenarios con poblaciones grandes, escenarios en los que el tiempo de procesamiento es muy significativo. El presente artículo introduce la simulación de eventos epidemiológicos basado en un modelo de Autómatas Celulares Estocásticos (AC), el cual ofrece la implementación de las características principales de una enfermedad infecciosa a gran escala: contacto, vecindario, trayectorias y transmisibilidad. Un caso de estudio es simulado en una implementación del algoritmo AC para una enfermedad infecciosa de tipo SEIRS (Susceptible, Expuesto, Infectado, Recuperado y Susceptible). Una población de 1 000 000 de individuos es paralelizada a través de un algoritmo de balanceo de procesos implementado en el lenguaje de programación C-CUDA. El resultado dado por el software paralelizado por GPU es comparado contra un análisis hecho del modelo paralelizado por multi-hilos CPU. Los resultados demuestran que el tiempo de cómputo puede ser reducido significativamente gracias al uso de C-CUDA.

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Global Stochastic Contact Modeling of Infectious Diseases

Cited by 4 publications

References 4 publications

A method for reducing the severity of epidemics by allocating vaccines according to centrality

A method for reducing the severity of epidemics by allocating vaccines according to centrality

Modeling malaria

Autómata Celular Estocástico paralelizado por GPU aplicado a la simulación de enfermedades infecciosas en grandes poblaciones

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