Modelling to contain pandemics

Center on Social and Economic Dynamics at the Brookings Institution, 1775 Massachusetts Avenue, Washington DC 20036, USA.
Nature (Impact Factor: 41.46). 09/2009; 460(7256):687. DOI: 10.1038/460687a
Source: PubMed


Agent-based computational models can capture irrational behaviour, complex social networks and global scale--all essential in confronting H1N1, says Joshua M. Epstein.

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    • "And how could we tease out one piece of significant behavior that can potentially shift the behavior from one state to another when we potentially have millions of agents and enumerable individual interactions? Of course, we do not always need to model an entire population (although it is possible[102]) but for those occasions that we do, would looking towards other disciplines that handle large complex systems with numerous interconnected components—such as physics and meteorology—be useful? While there are many clear reasons to use ABM for simulating complex spatial systems, O'Sullivan et al.[43]raise an interesting point: " Any gain in understanding of the system resulting from the modeling process derives from our ability to analyze the model and experiment with it " ([43], p. 113). "
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    ABSTRACT: Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.
    Full-text · Article · Jan 2016
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    • "Large scale, communications and the emergence phenomena are the objects of agent-based modelling and simulation. The agent oriented platforms such as Biowar [3], GASM [4], and EpiSims [5] to study emergency problems have been proposed in many fields. Biowar developed by Carnegie Mellon University is used to study the bioattacks in city with the ability of scalable agent modelling. "
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    ABSTRACT: This paper addresses the application of a computational theory and related techniques for studying emergency management in social computing. We propose a novel software framework called KD-ACP. The framework provides a systematic and automatic platform for scientists to study the emergency management problems in three aspects: modelling the society in emergency scenario as the artificial society; investigating the emergency management problems by the repeat computational experiments; parallel execution between artificial society and the actual society managed by the decisions from computational experiments. The software framework is composed of a series of tools. These tools are categorized into three parts corresponding to “A,” “C,” and “P,” respectively. Using H1N1 epidemic in Beijing city as the case study, the modelling and data generating of Beijing city, experiments with settings of H1N1, and intervention measures and parallel execution by situation tool are implemented by KD-ACP. The results output by the software framework shows that the emergency response decisions can be tested to find a more optimal one through the computational experiments. In the end, the advantages of the KD-ACP and the future work are summarized in the conclusion.
    Full-text · Article · Oct 2015 · Mathematical Problems in Engineering
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    • "Many of the worlds current problems can be described as complex [1] [2]. Complexity science and complex systems provide new ways to study many natural phenomena, from protein–protein interactions [3] [4] and the spreading of infectious diseases [5] [6], to social interactions and socio-economics of modern megacities [7] [8], all the way to the human brain itself [9] [10]. A complex behavior can occur in any system that consists of large numbers of components, which interact in a non-linear way [11], such as molecular and cellular systems, organisms, ecosystems and human societies. "
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    ABSTRACT: tComplexity and complex systems are all around us: from molecular and cellular systems in biology upto economics and human societies. There is an urgent need for methods that can capture the multi-scalespatio-temporal characteristics of complex systems. Recent emphasis has centered on two methods inparticular, those being complex networks and agent-based models. In this paper we look at the combi-nation of these two methods and identify “Complex Agent Networks”, as a new emerging computationalparadigm for complex system modeling. We argue that complex agent networks are able to capture bothindividual-level dynamics as well as global-level properties of a complex system, and as such may helpto obtain a better understanding of the fundamentals of such systems.
    Full-text · Article · Aug 2015 · Applied Soft Computing
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