The Consistency of the Pandemic Simulations between the SEIR Model and the MAS Model

Toyosaka, Yuki; Hirose, Hideo

doi:10.1587/transfun.e92.a.1558

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Such kinds of simulations are also used for simulations by scenario. In these two methods, a consistency between the SIR model and the agent model is observed [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza

Hirose

Wang

2012

2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming

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Nowadays, detecting the disaster phenomena and predicting the final stage become very important in the risk analysis view-point. The statistical methods provide accurate estimates of parameters when the data are completely given. However, when the data are incomplete, the accuracy of the estimates becomes poor. Therefore, statistical methods are weak in predicting the future trends. The SIR methods, for infectious disease spread prediction, using the differential equations can sometimes provide accurate estimates for the final stage. These methods, however, require some inspection time, which means the delay of analysis at least one week or so when we want to predict the future trends. To detect the disasters and to predict the future trends much earlier, we can use the social network system (SNS).In this paper, we have proposed a method to predict the future trend of influenza by using Twitter. We have analyzed the possibility of building a regression model by combining Twitter messages and CDC's Influenza-Like Illness (ILI) data, and we have found that the multiple linear regression model with ridge regularization outperforms the single linear regression model and other un-regularized least squared methods. The model of multiple linear regression with ridge can notably improve the prediction accuracy.

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“…Such kinds of simulations are also used for simulations by scenario. In these two methods, a consistency between the SIR model and the agent model is observed [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza

Hirose

Wang

2012

2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming

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Infectious Disease Spread Analysis Using Stochastic Differential Equations for SIR Model

Maki

Hirose

2013

2013 4th International Conference on Intelligent Systems, Modelling and Simulation

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Pandemic simulation is considered to be crucial as a scenario simulation and it is performed by many kinds of methods; the classical ordinary differential models (SIR model), agent-based models, internet-based models, and etc are among them. The SIR model is one of the fundamental methods to see the behavior of the pandemic with easy computation. However, there are no stochastic variation in the equations. The stochastic differential equations (SDE) can provide such kind of variations. Although the SDE are applied to many fields such as economics, less attention has been paid to the SIR simulations. In this paper, we propose a SDE version of the SIR simulation model with application to SARS (Severe Acute Respiratory Syndrome) case in 2003 in Hong Kong.

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Prediction of Spread of Damage Due to Disasters

Hirose¹

2017

IEICE Fundamentals Review

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We consider how soon the final condition of a disaster can be predicted while the disaster is ongoing. Assuming stages when the damage of a disaster increasing, appropriate prediction methodologies are introduced. In the midst of a disaster, predicting when the peak will be over, which is one of the most crucial issues, after the peak has occurred is meaningless. To circumvent such a condition, a variety of methods that can predict the final condition early are demonstrated. In this paper, the spread of infectious disease is mainly dealt with, and effective prediction methods at an early stage and the predicted results are discussed.

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The Consistency of the Pandemic Simulations between the SEIR Model and the MAS Model

Cited by 3 publications

References 13 publications

Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza

Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza

Infectious Disease Spread Analysis Using Stochastic Differential Equations for SIR Model

Prediction of Spread of Damage Due to Disasters

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