An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

Duan, Wei; Cao, Zhidong; Wang, Youzhong; Zhu, Bin; Zeng, Daniel; Wang, Fei‐Yue; Qiu, Xiaogang; Song, Hongbing; Wang, Yong

doi:10.1109/tsmc.2013.2256855

Cited by 41 publications

(25 citation statements)

References 44 publications

(53 reference statements)

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“…In 2013, Zhong et al [23] compared standard as well as modified compartment model of epidemic controlling using H1N1 2009 epidemic data of Arizona state. In 2013, Duan et al [24] applied a triple approach known as artificial societies, computational experiments and parallel execution to study and control epidemics. They used the data of H1N1 2009 outbreak in China University and examined the social network, student behavior, population distribution and contact patterns of the virus.…”

Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

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a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

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Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

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“…We design the system on the basis of the ACP (Artificial societies, Computational experiments, and Parallel execution) approach [131][132][133]. We build an artificial society simulating Beijing city that integrates with census data (a population of 19 million individuals, 8 million households, 3 thousand schools, and 6 thousand hospitals) [134], traffic networks, and district boundaries.…”

Section: Fig 6 a Large Scale Agent-based Simulation Of Influenza Epimentioning

confidence: 99%

Mathematical and computational approaches to epidemic modeling: a comprehensive review

Duan

Fan

Zhang

et al. 2015

Front. Comput. Sci.

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Mathematical and computational approaches are important tools for understanding epidemic spread patterns and evaluating policies of disease control. In recent years, epidemiology has become increasingly integrated with mathematics, sociology, management science, complexity science, and computer science. The cross of multiple disciplines has caused rapid development of mathematical and computational approaches to epidemic modeling. In this article, we carry out a comprehensive review of epidemic models to provide an insight into the literature of epidemic modeling and simulation.We introduce major epidemic models in three directions, including mathematical models, complex network models, and agent-based models. We discuss the principles, applications, advantages, and limitations of these models. Meanwhile, we also propose some future research directions in epidemic modeling. Table 1 Classification of epidemic models Types Names Methods Mathematical models Compartmental models, Reed-Frost models Differential equations, stochastic process, Monte Carlo, Markov chains Complex network models System dynamics models in complex networks, numerical simulations of epidemics in complex networks, metapopulation models, weighted networks, adaptive networks Differential equations, stochastic processes Agent-based models

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“…The duration of contact could also be modeled by the normal random variable as follows [20]:

\begin{matrix} T_{C} = μ_{T} + σ_{T} {(- 2 \ln (γ_{1}))}^{1 / 2} \cos (2 π γ_{2}), \end{matrix}

in which T C is the random variable of duration per contact, μ T is the mean value of normal distribution, σ T is the standard deviation of normal distribution, and γ 1 and γ 2 are the uniform random variables distributed in the interval [0, 1]. In addition, because the time spent in specific location T Location ( A i ) also follows the random distribution like (3) in our work, in order to make the duration of contact T contact ( A i ) shorter than the T Location ( A i ) the mean and standard deviation of (3) are set up as follows [20]:

\begin{matrix} μ_{T} = \frac{T_{Location}}{μ_{F} + σ_{F}}, \\ σ_{T} = \frac{T_{Location}}{10 μ_{F} + 10 σ_{F}}, \end{matrix}

in which T Location is the time of an activity in a specific location set in Table 2. μ T and σ T are the mean and standard deviation of contact frequency.…”

Section: Artificial Campusmentioning

confidence: 99%

A Modeling and Experiment Framework for the Emergency Management in AHC Transmission

Chen

Zhang

et al. 2014

Computational and Mathematical Methods in Medicine

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Emergency management is crucial to finding effective ways to minimize or even eliminate the damage of emergent events, but there still exists no quantified method to study the events by computation. Statistical algorithms, such as susceptible-infected-recovered (SIR) models on epidemic transmission, ignore many details, thus always influencing the spread of emergent events. In this paper, we first propose an agent-based modeling and experiment framework to model the real world with the emergent events. The model of the real world is called artificial society, which is composed of agent model, agent activity model, and environment model, and it employs finite state automata (FSA) as its modeling paradigm. An artificial campus, on which a series of experiments are done to analyze the key factors of the acute hemorrhagic conjunctivitis (AHC) transmission, is then constructed to illustrate how our method works on the emergency management. Intervention measures and optional configurations (such as the isolation period) of them for the emergency management are also given through the evaluations in these experiments.

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An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

Cited by 41 publications

References 44 publications

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Mathematical and computational approaches to epidemic modeling: a comprehensive review

A Modeling and Experiment Framework for the Emergency Management in AHC Transmission

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