Efficient Interruption of Infection Chains by Targeted Removal of Central Holdings in an Animal Trade Network

Centrality parameters in animal trade networks typically have right-skewed distributions, implying that these networks are highly resistant against the random removal of holdings, but vulnerable to the targeted removal of the most central holdings. In the present study, we analysed the structural changes of an animal trade network topology based on the targeted removal of holdings using specific centrality parameters in comparison to the random removal of holdings. Three different time periods were analysed: the three-year network, the yearly and the monthly networks. The aim of this study was to identify appropriate measures for the targeted removal, which lead to a rapid fragmentation of the network. Furthermore, the optimal combination of the removal of three holdings regardless of their centrality was identified. The results showed that centrality parameters based on ingoing trade contacts, e.g. in-degree, ingoing infection chain and ingoing closeness, were not suitable for a rapid fragmentation in all three time periods. More efficient was the removal based on parameters considering the outgoing trade contacts. In all networks, a maximum percentage of 7.0% (on average 5.2%) of the holdings had to be removed to reduce the size of the largest component by more than 75%. The smallest difference from the optimal combination for all three time periods was obtained by the removal based on out-degree with on average 1.4% removed holdings, followed by outgoing infection chain and outgoing closeness. The targeted removal using the betweenness centrality differed the most from the optimal combination in comparison to the other parameters which consider the outgoing trade contacts. Due to the pyramidal structure and the directed nature of the pork supply chain the most efficient interruption of the infection chain for all three time periods was obtained by using the targeted removal based on out-degree.

[1]  C R Webb,et al.  Investigating the potential spread of infectious diseases of sheep via agricultural shows in Great Britain , 2005, Epidemiology and Infection.

[2]  M. Newman,et al.  Network theory and SARS: predicting outbreak diversity , 2004, Journal of Theoretical Biology.

[3]  P. E. Kopp,et al.  Superspreading and the effect of individual variation on disease emergence , 2005, Nature.

[4]  Martina Morris,et al.  Epidemiology and Social Networks: , 1993 .

[5]  M. Keeling,et al.  Contact structure and Salmonella control in the network of pig movements in France. , 2011, Preventive veterinary medicine.

[6]  Joachim Krieter,et al.  Static network analysis of a pork supply chain in Northern Germany-Characterisation of the potential spread of infectious diseases via animal movements. , 2013, Preventive veterinary medicine.

[7]  Joachim Krieter,et al.  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. , 2015, Transboundary and emerging diseases.

[8]  N. Masuda,et al.  Controlling nosocomial infection based on structure of hospital social networks , 2008, Journal of Theoretical Biology.

[9]  I. Kiss,et al.  The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread , 2006, Journal of The Royal Society Interface.

[10]  B. Martínez-López,et al.  Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain. , 2009, Preventive Veterinary Medicine.

[11]  S. Havlin,et al.  Breakdown of the internet under intentional attack. , 2000, Physical review letters.

[12]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008 .

[13]  C Dubé,et al.  Estimating potential epidemic size following introduction of a long-incubation disease in scale-free connected networks of milking-cow movements in Ontario, Canada. , 2011, Preventive veterinary medicine.

[14]  Douglas D. Heckathorn,et al.  AIDS AND SOCIAL NETWORKS: HIV PREVENTION THROUGH NETWORK MOBILIZATION* , 1999 .

[15]  L. Danon,et al.  Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain , 2006, Proceedings of the Royal Society B: Biological Sciences.

[16]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[17]  I. Kiss,et al.  Infectious disease control using contact tracing in random and scale-free networks , 2006, Journal of The Royal Society Interface.

[18]  M. Sahini,et al.  Applications of Percolation Theory , 2023, Applied Mathematical Sciences.

[19]  Reuven Cohen,et al.  Complex Networks: Structure, Robustness and Function , 2010 .

[20]  M. Bigras-Poulin,et al.  Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. , 2006, Preventive veterinary medicine.

[21]  A. de Kruif,et al.  Type and frequency of contacts between Belgian pig herds. , 2009, Preventive veterinary medicine.

[22]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[23]  R. May,et al.  Infection dynamics on scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  C. Dubé,et al.  A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. , 2009, Transboundary and emerging diseases.

[25]  Matthias Greiner,et al.  Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. , 2007, Preventive veterinary medicine.

[26]  Cohen,et al.  Resilience of the internet to random breakdowns , 2000, Physical review letters.

[27]  Teresa Cabral,et al.  Identification of networks of sexually transmitted infection: a molecular, geographic, and social network analysis. , 2005, The Journal of infectious diseases.

[28]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  A. Barrat,et al.  Dynamical Patterns of Cattle Trade Movements , 2011, PloS one.

[30]  M. Kretzschmar,et al.  Perspective: human contact patterns and the spread of airborne infectious diseases. , 1999, Trends in microbiology.

[31]  Naoki Masuda,et al.  Immunization of networks with community structure , 2009, 0909.1945.

[32]  Thilo Gross,et al.  Epidemic dynamics on an adaptive network. , 2005, Physical review letters.

[33]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[34]  R. Christley,et al.  Infection in social networks: using network analysis to identify high-risk individuals. , 2005, American journal of epidemiology.

[35]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[36]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[37]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[38]  C Dubé,et al.  Comparing network analysis measures to determine potential epidemic size of highly contagious exotic diseases in fragmented monthly networks of dairy cattle movements in Ontario, Canada. , 2008, Transboundary and emerging diseases.

[39]  Gary A. Talbot Applications of Percolation Theory , 1995 .

[40]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[41]  M. Nöremark,et al.  Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. , 2011, Preventive veterinary medicine.

[42]  Marcel Salathé,et al.  Dynamics and Control of Diseases in Networks with Community Structure , 2010, PLoS Comput. Biol..

[43]  B. Durand,et al.  Structural vulnerability of the French swine industry trade network to the spread of infectious diseases. , 2012, Animal : an international journal of animal bioscience.

[44]  P. Holme,et al.  Predicting and controlling infectious disease epidemics using temporal networks , 2013, F1000prime reports.

[45]  F. Natale,et al.  Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. , 2009, Preventive Veterinary Medicine.

[46]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  W. Coffey,et al.  Diffusion and Reactions in Fractals and Disordered Systems , 2002 .