findContactChain function

Finding elements in contact chains of a dynamic network.

Parallel function to find outgoing and ingoing contact chain elements.

findContactChain(
  Data,
  from,
  to,
  Time,
  selected.nodes,
  type = "size",
  numberOfcores = NULL
)

Arguments

• Data: data.frame with network information: node ID, origin node, destination node, and the time in which the link was established.
• from: character, variable name (column name) for origin node.
• to: character, variable name (column name) for destination node.
• Time: character, variable name (column name) for the time in which the link was established between two nodes.
• selected.nodes: vector, the function will find the contact chain of the nodes present in the selected.nodes vector.
• type: character, of returned result. type = 'size' (default), will return the size of 'outgoing' and 'ingoing' contact chains. Type = 'chain' will return also the nodes in each chain (might be slow for big data sets).
• numberOfcores: integer, number of cores used to calculate the contact chain (default is NULL, that will lead the algorithm to use the max number of cores).

Returns

setting type = 'size', it returns a data.frame with ingoing and outgoing contact chains size, add 1 to include the selected.nodes. Setting type = 'chain', it returns a list with the data frame and elements of ingoing and outgoing chains.

Details

This is a function that find elements of a contact chain from a dynamic network.

Examples

# Loading data
data(networkSample) # help("networkSample"), for more info.
 
# contact chain function
selected.nodes <- c(37501, 36811, 36812)
contact.chain <- findContactChain(Data = networkSample, from = 'originID',
                                  to = 'destinationID', Time = 'Day', selected.nodes,
                                  type = 'chain', numberOfcores = 2)

References

[1] C Dube, C Ribble, D Kelton, 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. In: Transboundary and emerging diseases 55.9-10 (Dec. 2008), pp. 382-392.

[2] C Dube, C Ribble, D Kelton, et al. A review of network analysis terminology and its application to foot-and-mouth disease modeling and policy development. In: Transboundary and emerging diseases 56.3 (Apr. 2009), pp. 73-85.

[3] Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Network. In: Jounal of Statistical Software Volume 94, Issue 6 doi:10.18637/jss.v094.i06.

[4] Jenny Frossling, Anna Ohlson, Camilla Bjorkman, et al. Application of network analysis parameters in risk-based surveillance - Examples based on cattle trade data and bovine infections in Sweden. In: Preventive veterinary medicine 105.3 (July 2012), pp. 202-208. doi:10.1016/j.prevetmed.2011.12.011.

[5] K Buttner, J Krieter, and I Traulsen. 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. In: Transboundary and emerging diseases 2000 (May 2013), pp. 1-12.

[6] Maria Noremark, Nina Ha kansson, Susanna Sternberg Lewerin, et al. Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. In: Preventive veterinary medicine 99.2-4 (2011), pp. 78-90. doi:10.1016/j.prevetmed.2010.12.009.