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Is a Sampled Network a Good Enough Descriptor? Missing Links and Appropriate Choice of Representation
Systems Biology Research Centre, Skövde University, Skövde, Sweden.
Systems Biology Research Centre, Skövde University, Skövde, Sweden.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, The Institute of Technology.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, The Institute of Technology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Missing links due to sampling difficulties can be a limitation in network analysis. Measurements and analysis of networks with insufficient data may make the actual properties indistinct and thus include too much uncertainty to lead to accurate inferences. In addition, in dynamical networks with low link degrees and high stochasticity one sample of the network structure during a finite time window may not be sufficient for general conclusions. Our interest here is to examine the possible consequences of analysis of networks with insufficient data. We studied how mean link degree in sampled networks affects predictions of the spread of disease. Networks with weighted links were used to run scenarios that assumed distance-dependent probabilities of disease transmission when applying general simulation methodology. These scenarios were compared with scenarios including randomly drawn probabilities of disease transmission. For both types of scenarios, we also tested two link-forming methods, one based on distance-dependence and the other on a random approach. Our findings imply that sampled networks must be improved by using statistical measures before attempting to estimate or predict the spread of disease. We conclude that, under the assumption of weighted links, predictions about the extent of an epidemic can be drawn only at mean degrees that are much higher than found in empirical studies. In reality, neither sampling procedures nor disease transmissions are completely dependent on distance. Our results show how this aspect enforces an even higher level of mean degree to be present in order to achieve reasonable predictions.

National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-76374OAI: oai:DiVA.org:liu-76374DiVA: diva2:514146
Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2012-04-05Bibliographically approved
In thesis
1. Networks and Epidemics: Impact of Network Structure on Disease Transmission
Open this publication in new window or tab >>Networks and Epidemics: Impact of Network Structure on Disease Transmission
2012 (English)Doctoral 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 infect ious 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 31 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1433
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-76376 (URN)978-91-7519-940-5 (ISBN)
Public defence
2012-04-27, E324 Schrödinger, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2017-07-07Bibliographically approved

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