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SpecNet: a spatial network algorithm that generates a wide range of specific structures
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.
Systems Biology Research Centre, Skövde University, Skövde, Sweden.
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.

Place, publisher, year, edition, pages
2012. Vol. 7, no 8
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-76359DOI: 10.1371/journal.pone.0042679ISI: 000307184700057OAI: oai:DiVA.org:liu-76359DiVA: diva2:514087
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
In thesis
1. Network analysis and optimization of animal transports
Open this publication in new window or tab >>Network analysis and optimization of animal transports
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is about animal transports and their effect on animal welfare. Transports are needed in today’s system of livestock farming. Long transports are stressful for animals and infectious diseases can spread via animal transports. With optimization methods transport times can be minimized, but there is a trade-off between short distances for the animals and short distances for the trucks. The risk of disease spread in the transport system and disease occurrence at farms can be studied with models and network analysis.

The animal transport data and the quality of the data in the Swedish national database of cattle and pig transports are investigated in the thesis. The data is analyzed regarding number of transports, number of farms, seasonality, geographical properties, transport distances, network measures of individual farms and network measures of the system. The data can be used as input parameters in epidemic models.

Cattle purchase reports are double reported and we found that there are incorrect and missing reports in the database. The quality is improving over the years i.e. 5% of cattle purchase reports were not correctly double reported in 2006, 3% in 2007 and 1% in 2008. In the reports of births and deaths of cattle we detected date preferences; more cattle births and deaths are reported on the 1st, 10th and 20th each month. This is because when we humans don’t remember the exact number we tend to pick nice numbers (like 1, 10 and 20). This implies that the correct date is not always reported.

Network analysis and network measures are suggested as tools to estimate risk for disease spread in transport systems and risk of disease introduction to individual holdings. Network generation algorithms can be used together with epidemic models to test the ability of network measures to predict disease risks. I have developed, and improved, a network generation algorithm that generates a large variety of structures.

In my thesis I also suggest a method, the good choice heuristic, for generating non-optimal routes. Today coordination of animal transports is neither optimal nor random. In epidemic simulations we need to model routes as close to the actual driven routes as possible and the good choice heuristic can model that. The heuristic is tuned by two parameters and creates coordination of routes from completely random to almost as good as the Clarke and Wright heuristic. I also used the method to make the rough estimate that transport distances for cattle can be reduced by 2-24% with route-coordination optimization of transports-to-slaughter.

Different optimization methods can be used to minimize the transport times for animal-transports in Sweden. For transports-to-slaughter the strategic planning of “which animals to send where” is the first step to optimize. I investigated data from 2008 and found that with strategic planning, given the slaughterhouse capacity, transport distances can be decreased by about 25% for pigs and 40% for cattle. The slaughterhouse capacity and placement are limiting the possibility to minimize transport times for the animals. The transport distances could be decreased by 60% if all animals were sent to the closest slaughterhouse 2008. Small-scale and mobile slaughterhouses have small effect on total transport work (total transport distance for all the animals) but are important for the transport distances of the animals that travel the longest.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 34 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1434
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-76361 (URN)
Public defence
2012-04-04, Schrödinger, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2012-04-05 Created: 2012-04-05 Last updated: 2012-04-05Bibliographically approved
2. 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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