Modeling infectious diseases using global stochastic field simulation

Venkatachalam, Sangeeta; Mikler, Armin R.

doi:10.1109/grc.2006.1635909

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…To address some of the limitations of the SIR model, Venkatachalam and Mikler (2006) have proposed the Global Stochastic Field Simulation (GSFS) model, which takes into account heterogeneous populations, demographic constraints, contact structure, and disease dynamics in order to model the spread of disease. This approach assigns a geographic region to a grid representation and overlays a field encompassing the spatial distribution of population and interaction distributions.…”

Section: Field Simulation Modelingmentioning

confidence: 99%

Modeling the Spread of Infectious Diseases: A Review

Chen¹

2014

Wiley Series in Probability and Statistics

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Section: Field Simulation Modelingmentioning

confidence: 99%

Modeling the Spread of Infectious Diseases: A Review

Chen¹

2014

Wiley Series in Probability and Statistics

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“…However, they are impractical when large populations need to be simulated due to space required for information about each agent in the simulation. Metapopulation models, on the other hand, break the population into subpopulations and then simulate the interactions between and within the subpopulations [10,23]. Such models sacrifice some of the precision offered by agent-based modeling in exchange for the ability to simulate large populations.…”

Section: Computational Simulation Of Epidemicsmentioning

confidence: 99%

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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“…Shikha (Singh et al, 2005) studied the effects of migrations of people for the increasing number of malaria cases and they proposed and analyzed SIS and SIRS epidemic models. Sangeeta (Venkatachalam et al, 2006) put forward a global stochastic field simulation paradigm (GSFS) for the modeling and simulation of infectious disease epidemics. (Johnson et al, 2009) analyzed the mathematical modeling of infectious disease using the SIR model.…”

Section: Introductionmentioning

confidence: 99%

Analysis of SIR Mathematical Model for Malaria Disease: A Study in Assam, India

Devi

Choudhury²

2023

J. I. Dasar

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The global outbreak of covid-19 pandemic is still affecting people around the globe very badly. Before the covid-19 pandemic outbreak, several research works were done for the detection and prevention of various infectious diseases using different mathematical modeling. Implementing mathematical modeling to resolve problems in Biology and physiology is generally called Mathematical Biology, an extremely interdisciplinary area. The applications of mathematical modeling in the analysis of infectious diseases help to concentrate on the necessary processes associated with forming the infectious disease epidemiology and specifications estimation. The compartmental mathematical model can be either SI, SIS, SIR, SIRS, or SEIR where S, I, R, and E denote susceptible, infected, recovered, and exposed respectively. Malaria is an infectious disease that has a large economic and health impact on society. This study aims to predict the estimation of suspected, infected and recovered people using the SIR mathematical model of the Barama area of Baksa District in Assam, India. Here we analyzed the Basic Reproductive Ratio of the SIR model for malaria disease and examined if malaria is epidemic or endemic in that area.

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Modeling infectious diseases using global stochastic field simulation

Cited by 5 publications

References 7 publications

Modeling the Spread of Infectious Diseases: A Review

Modeling the Spread of Infectious Diseases: A Review

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

Analysis of SIR Mathematical Model for Malaria Disease: A Study in Assam, India

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