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Networks and Epidemics: Impact of Network Structure on Disease Transmission
Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-76376ISBN: 978-91-7519-940-5 (print)OAI: oai:DiVA.org:liu-76376DiVA: diva2:514152
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
List of papers
1. Generating structure specific networks
Open this publication in new window or tab >>Generating structure specific networks
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2010 (English)In: Advances in Complex Systems, ISSN 0219-5259, Vol. 13, no 2, 239-250 p.Article in journal (Refereed) Published
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 distribution 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 (gamma, sigma, kappa). The structure is measured by fragmentation, degree assortativity, clustering and group betweenness of the networks. The parameter gamma regulates the aggregation in the spatial node pattern and sigma and kappa regulates the probability of link forming.

Keyword
Network; spectral; assortativity; fragmentation; clustering; betweenness centralization; spatial network; network algorithm
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-58246 (URN)10.1142/S0219525910002517 (DOI)000279727100006 ()
Available from: 2010-08-10 Created: 2010-08-09 Last updated: 2017-12-12Bibliographically approved
2. SpecNet: a spatial network algorithm that generates a wide range of specific structures
Open this publication in new window or tab >>SpecNet: a spatial network algorithm that generates a wide range of specific structures
2012 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 8Article in journal (Refereed) Published
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 network-generating 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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-76359 (URN)10.1371/journal.pone.0042679 (DOI)000307184700057 ()
Note

funding agencies|Swedish Civil Contingencies Agency (MSB)||Foreign Animal Disease Modeling program of the Science and Technology Directorate, Department of Homeland Security|ST-108-000017|

Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2017-12-07Bibliographically approved
3. Is a Sampled Network a Good Enough Descriptor? Missing Links and Appropriate Choice of Representation
Open this publication in new window or tab >>Is a Sampled Network a Good Enough Descriptor? Missing Links and Appropriate Choice of Representation
(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:nbn:se:liu:diva-76374 (URN)
Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2012-04-05Bibliographically approved
4. Network measures efficiency as predictors for disease transmission in spatial farm networks
Open this publication in new window or tab >>Network measures efficiency as predictors for disease transmission in spatial farm networks
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Networks can be categorised using different measures of connectivity and topology of the network. We have examined if such network measures can be used as predictors of disease transmission in networks. In this study, virtual networks with a wide range of different structures are generated using the SpecNet algorithm. Measures are calculated for both the network as a whole and for individual nodes. The virtual networks generated a large variation in number of infected farms. In general, a large variation was still present for networks with equal value of a measure which implies that single network measures may not be sufficient as predictor for spread of disease. Yet, mean degree and the average clustering coefficient were the global network measures that could best explain the variation in the number of infected farms of a network. At the local level the degree and the clustering coefficient of the initially infected farm explain most of the variation in the number of infected farms. Hence, our results also points out that one should also consider the characteristics of the initially infected farm when predicting  the spread of a disease.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-76375 (URN)
Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2012-04-05Bibliographically approved

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