Literature DB >> 23692588

Characterization of contact structures for the spread of infectious diseases in a pork supply chain in northern Germany by dynamic network analysis of yearly and monthly networks.

K Büttner1, J Krieter, I Traulsen.   

Abstract

A major risk factor in the spread of diseases between holdings is the transport of live animals. This study analysed the animal movements of the pork supply chain of a producer group in Northern Germany. The parameters in-degree and out-degree, ingoing and outgoing infection chain, betweenness and ingoing and outgoing closeness were measured using dynamic network analysis to identify holdings with central positions in the network and to characterize the overall network topology. The potential maximum epidemic size was also estimated. All parameters were calculated for three time periods: the 3-yearly network, the yearly and the monthly networks. The yearly and the monthly networks were more fragmented than the 3-yearly network. On average, one-third of the holdings were isolated in the yearly networks and almost three quarters in the monthly networks. This represented an immense reduction in the number of holdings participating in the trade of the monthly networks. The overall network topology showed right-skewed distributions for all calculated centrality parameters indicating that network resilience was high concerning the random removal of holdings. However, for a targeted removal of holdings according to their centrality, a rapid fragmentation of the trade network could be expected. Furthermore, to capture the real importance of holdings for disease transmission, indirect trade contacts (infection chain) should be considered. In contrast to the parameters regarding direct trade contacts (degree), the infection chain parameter did not underestimate the potential risk of disease transmission. This became more obvious, the longer the observed time period was. For all three time periods, the results for the estimation of the potential maximum epidemic size illustrated that the outgoing infection chain should be chosen. It considers the chronological order and the directed nature of the contacts and has no restrictions such as the strongly connected components of a cyclic network.
© 2013 Blackwell Verlag GmbH.

Entities:  

Keywords:  Germany; animal movements; monthly networks; network analysis; pig trade network; yearly networks

Mesh:

Year:  2013        PMID: 23692588     DOI: 10.1111/tbed.12106

Source DB:  PubMed          Journal:  Transbound Emerg Dis        ISSN: 1865-1674            Impact factor:   5.005


  10 in total

1.  Pig movements in France: Designing network models fitting the transmission route of pathogens.

Authors:  Morgane Salines; Mathieu Andraud; Nicolas Rose
Journal:  PLoS One       Date:  2017-10-19       Impact factor: 3.240

2.  Descriptive and network analyses of the equine contact network at an equestrian show in Ontario, Canada and implications for disease spread.

Authors:  Kelsey L Spence; Terri L O'Sullivan; Zvonimir Poljak; Amy L Greer
Journal:  BMC Vet Res       Date:  2017-06-21       Impact factor: 2.741

3.  Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark.

Authors:  Jana Schulz; Anette Boklund; Tariq H B Halasa; Nils Toft; Hartmut H K Lentz
Journal:  PLoS One       Date:  2017-06-29       Impact factor: 3.240

4.  Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs.

Authors:  Morgane Salines; Mathieu Andraud; Nicolas Rose
Journal:  Prev Vet Med       Date:  2017-11-20       Impact factor: 2.670

5.  Complex network analysis to understand trading partnership in French swine production.

Authors:  Pachka Hammami; Stefan Widgren; Vladimir Grosbois; Andrea Apolloni; Nicolas Rose; Mathieu Andraud
Journal:  PLoS One       Date:  2022-04-07       Impact factor: 3.240

6.  Network analysis of live pig movements in North Macedonia: Pathways for disease spread.

Authors:  Kathleen C O'Hara; Daniel Beltrán-Alcrudo; Mark Hovari; Blagojcho Tabakovski; Beatriz Martínez-López
Journal:  Front Vet Sci       Date:  2022-08-09

7.  Efficient interruption of infection chains by targeted removal of central holdings in an animal trade network.

Authors:  Kathrin Büttner; Joachim Krieter; Arne Traulsen; Imke Traulsen
Journal:  PLoS One       Date:  2013-09-12       Impact factor: 3.240

8.  Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network).

Authors:  Kathrin Büttner; Jennifer Salau; Joachim Krieter
Journal:  Springerplus       Date:  2016-02-24

9.  Temporal correlation coefficient for directed networks.

Authors:  Kathrin Büttner; Jennifer Salau; Joachim Krieter
Journal:  Springerplus       Date:  2016-07-28

10.  Network Analysis of Swine Shipments in China: The First Step to Inform Disease Surveillance and Risk Mitigation Strategies.

Authors:  Kathleen O'Hara; Rui Zhang; Yong-Sam Jung; Xiaobing Zhou; Yingjuan Qian; Beatriz Martínez-López
Journal:  Front Vet Sci       Date:  2020-04-28
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.