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Understanding and Exploiting Information Spreading and Integrating Technologies

Journal of Computer Science & Technology 2011年第5期26卷 829-836页

作者： Petter Holme Mikael Huss IceLab Department of PhysicsUme a University901 87 Ume aSweden Department of Energy Science Sungkyunkwan University Science of Life Laboratory Stockholm Karolinska Institute Science ParkBox 1031171 21 SolnaSweden Department of Biochemistry and Biophysics The Arrhenius Laboratories for Natural SciencesStockholm University 106 91 StockholmSweden

Our daily life leaves an increasing amount of digital traces,footprints that are improving our lives.Data-mining tools,like recommender systems,convert these traces to information for aiding decisions in an ever-increasing number of areas in our lives.The feedback loop from what we do,to the information this produces,to decisions what to do next,will likely be an increasingly important factor in human behavior on all levels from individuals to societies.In this essay,we review some effects of this feedback and discuss how to understand and exploit them beyond mapping them on more well-understood phenomena.We take examples from models of spreading phenomena in social media to argue that analogies can be deceptive,instead we need to fresh approaches to the new types of data,something we exemplify with promising applications in medicine.

关键词： modeling and prediction computer-supported collaborative work health care

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Model Versions and Fast Algorithms for Network Epidemiology

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后勤工程学院学报 2014年第3期30卷 1-7页

作者： Petter Holme Dept.of Energy Science Sungkyunkwan University Dept.of Physics IceLab Ume University Dept.of Sociology Stockholm University

Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreadingof infectious diseases.In network epidemiology,one represents the contact structure as a network of nodes(individuals)connected bylinks(sometimes as a temporal network where the links are not continuously active)and the disease as a compartmental model(whereindividuals are assigned states with respect to the disease and follow certain transition rules between the states).In this paper,we discussfast algorithms for such simulations and also compare two commonly used versions,one where there is a constant recovery rate(the numberof individuals that stop being infectious per time is proportional to the number of such people);the other where the duration of the diseaseis constant.The results show that,for most practical purposes,these versions are qualitatively the same.

关键词： network theory algorithm stochastic simulation infectious disease

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