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  • 1.
    Håkansson, Nina
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Jonsson, Annie
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Lennartsson, Jenny
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Lindström, T.
    Linköping University.
    Wennergren, U.
    Linköping University.
    Generating Structure Specific Networks2010In: Advances in Complex Systems, ISSN 0219-5259, Vol. 13, no 2, p. 239-250Article in journal (Refereed)
    Abstract [en]

    Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribu-tion function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes.We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (γ, σ, κ). The structure is measured by fragmentation, degree assortativity, clusteringand group betweenness of the networks. The parameter γ regulates the aggregation in the spatial node pattern and σ and κ regulates the probability of link forming.

  • 2.
    Lennartsson, Jenny
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Networks and epidemics - impact of network structure on disease transmission2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The spread of infectious diseases, between animals as well as between humans, is a topic often in focus. Outbreaks of diseases like for example foot-and-mouth disease, avian influenza, and swine influenza have in the last decades led to an increasing interest in modelling of infectious diseases since such models can be used to elucidate disease transmission and to evaluate the impact of different control strategies. Different kind of modelling techniques can be used, e.g. individual based disease modelling, Bayesian analysis, Markov Chain Monte Carlo simulations, and network analysis. The topic in this thesis is network analysis, since this is a useful method when studying spread of infectious diseases. The usefulness lies in the fact that a network describes potential transmission routes, and to have knowledge about the structure of them is valuable in predicting the spread of diseases. This thesis contains both a method for generating a wide range of different theoretical networks, and also examination and discussion about the usefulness of network analysis as a tool for analysing transmission of infectious animal diseases between farms in a spatial context. In addition to the theoretical networks, Swedish animal transport networks are used as empirical examples.

    To be able to answer questions about the effect of the proportion of contacts in networks, the effect of missing links and about the usefulness of network measures, there was a need to manage to generate networks with a wide range of different structures. Therefore, it was necessary to develop a network generating algorithm. Papers I and II describes that network generating algorithm, SpecNet, which creates spatial networks. The aim was to develop an algorithm that managed to generate a wide range of network structures. The performance of the algorithm was evaluated by some network measures. In the first study, Paper I, the algorithm succeeded to generate a wide range of most of the investigated network measures. Paper II is an improvement of the algorithm to produce networks with low negative assortativity by adding two classes of nodes instead of one. Except to generate theoretical networks from scratch, it is also relevant that a network generating algorithm has the potential to regenerate a network with given specific structures. Therefore, we tested to regenerate two Swedish animal transport networks according to their structures. SpecNet managed to mimic the two empirical networks well in comparison with a non-spatial network generating algorithm that was not equally successful in regenerating the requested structures.

    Sampled empirical networks are rarely complete, since contacts are often missing during sampling, e.g. due to difficulties to sample or due to too short time window during sampling. In Paper III, the focus is on the effect on disease transmission, due to number of contacts in the network, as well as on the reliability of making predictions from networks with a small proportion of missing links. In addition, attention is also given to the spatial distribution of animal holdings in the landscape and on what effect this distribution has on the resulting disease transmission between the holdings. Our results indicate that, assuming weighted contacts, it is maybe risky to make predictions about disease transmission from one single network replicate with as low proportion of contacts as in most empirical animal transport networks.

    In case of a disease outbreak, it would be valuable to use network measures as predictors for the progress and the extent of the disease transmission. Then a reliable network is required, and also that the used network measures has the potential to make reasonable predictions about the epidemic. In Paper IV we investigate if network measures are useful as predictors for eventual disease transmissions. Moreover, we also analyse if there is some measure that correlates better with disease transmission than others. Disease transmission simulations are performed in networks with different structures to mimic diverse spatial conditions, thereafter are the simulation results compared to the values of the network structures.

  • 3.
    Lennartsson, Jenny
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Håkansson, Nina
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences. Department of Physics and Measurement Technology, Biology and Chemistry, Theory and Modelling, Linköping University, Linköping, Sweden.
    Wennergren, Uno
    Department of Physics and Measurement Technology, Biology and Chemistry, Theory and Modelling, Linköping University, Linköping, Sweden.
    Jonsson, Annie
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 8, article id e42679Article in journal (Refereed)
    Abstract [en]

    Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral networkgenerating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range.

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