hybridModels (version 0.3.7)

findContactChain: Finding elements in contact chains of a dynamic network.

Description

Parallel function to find outgoing and ingoing contact chain elements.

Usage

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).

Value

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.

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>.

Examples

# NOT RUN {
# 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)
# }