origin: Origin Estimation for Propagation Processes on Complex...

In NetOrigin: Origin Estimation for Propagation Processes on Complex Networks

Origin Estimation for Propagation Processes on Complex Networks

Description

This is the main function for origin estimation for propagation processes on complex networks. Different methods are available: effective distance median ('edm'), recursive backtracking ('backtracking'), and centrality-based source estimation ('centrality'). For details on the methodological background, we refer to the corresponding publications.

origin_edm for effective distance-median origin estimation (Manitz et al., 2016)

Usage

origin(events, type = c("edm", "backtracking", "centrality", "bayesian"), ...)

origin_edm(events, distance, silent = TRUE)

origin_backtracking(events, graph, start_with_event_node = TRUE, silent = TRUE)

origin_centrality(events, graph, silent = TRUE)

origin_bayesian(
  events,
  thres.vec,
  obs.vec,
  mu.mat,
  lambda.list,
  poss.candidate.vec,
  prior,
  use.prior = TRUE
)

Arguments

events	numeric vector of event counts at a specific time point; if type is 'bayesian', 'events' is a matrix, number of nodes x time points; entries represent number of cases
type	character specifying the method, 'edm', 'backtracking', 'centrality' and 'bayesian' are available.
...	parameters to be passed to origin methods origin_edm, origin_backtracking, origin_centrality or origin_centrality
distance	numeric matrix specifying the distance matrix (for type='edm')
silent	locigal, should the messages be suppressed?
graph	igraph object specifying the underlying network graph (for type='backtracking' and type='centrality')
start_with_event_node	logical specifying whether backtracking only starts from nodes that experienced events (for type='backtracking')
thres.vec	vector, length represents number of cities/nodes, representing thresholds for cities/nodes that they are infected
obs.vec	list of cities ids used as observers
mu.mat	matrix- number of cities/nodes x number of observers, each row represents - if this node is the source, the mean of arrival time vector
lambda.list	a length-number of cities/nodes list, each element is a number of observers x number of observers matrix - if a node is the source, the covariance matrix for arrival time vector
poss.candidate.vec	a boolean vector indicating if a node has the potential to be the source
prior	vector, length - number of cities/nodes, prior for cities
use.prior	boolean, TRUE or FALSE, if use prior, default TRUE

Value

origin_edm returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude,

  • wmean for weighted mean,

  • wvar for weighted variance, and

  • mdist mean distance from a node to all other nodes.

• type = 'edm' effective distance median origin estimation

origin_backtracking returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude, and

  • bcount for backtracking counts, how often backtracking identifies this source node.

• type = 'backtracking' backtracking origin estimation

origin_centrality returns an object of class origin, list with

• est origin estimate

• aux data.frame with auxiliary variables

  • id as node identifier,

  • events for event magnitude, and

  • cent for node centrality (betweenness divided degree).

• type = 'centrality' centrality-based origin estimation

a dataframe with columns 'nodes' and 'probab', indicating nodes indices and their posteriors

Author(s)

Juliane Manitz with contributions by Jonas Harbering

Jun Li

References

• Comin, C. H. and da Fontoura Costa, L. (2011). Identifying the starting point of a spreading process in complex networks. Physical Review E, 84. <doi: 10.1103/PhysRevE.84.056105>

• Manitz, J., J. Harbering, M. Schmidt, T. Kneib, and A. Schoebel (2017): Source Estimation for Propagation Processes on Complex Networks with an Application to Delays in Public Transportation Systems. Journal of Royal Statistical Society C (Applied Statistics), 66: 521-536. <doi: 10.1111/rssc.12176>

• Manitz, J. (2014). Statistical Inference for Propagation Processes on Complex Networks. Ph.D. thesis, Georg-August-University Goettingen. Verlag Dr.~Hut, ISBN 978-3-8439-1668-4. Available online: https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0022-5F38-B.

• Manitz, J., Kneib, T., Schlather, M., Helbing, D. and Brockmann, D. (2014). Origin detection during food-borne disease outbreaks - a case study of the 2011 EHEC/HUS outbreak in Germany. PLoS Currents Outbreaks, 1. <doi: 10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2>

• Li, J., Manitz, J., Bertuzzo, E. and Kolaczyk, E.D. (2020). Sensor-based localization of epidemic sources on human mobility networks. arXiv preprint Available online: https://arxiv.org/abs/2011.00138.

See Also

Other origin-est: origin_multiple()

Examples

data(delayGoe)

# compute effective distance
data(ptnGoe)
goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE)
p <- goenet/rowSums(goenet)
eff <- eff_dist(p)
# apply effective distance median source estimation
om <- origin(events=delayGoe[10,-c(1:2)], type='edm', distance=eff)
summary(om)
plot(om, 'mdist',start=1)
plot(om, 'wvar',start=1)
performance(om, start=1, graph=ptnGoe)

# backtracking origin estimation (Manitz et al., 2016)
ob <- origin(events=delayGoe[10,-c(1:2)], type='backtracking', graph=ptnGoe)
summary(ob)
plot(ob, start=1)
performance(ob, start=1, graph=ptnGoe)

# centrality-based origin estimation (Comin et al., 2011)
oc <- origin(events=delayGoe[10,-c(1:2)], type='centrality', graph=ptnGoe)
summary(oc)
plot(oc, start=1)
performance(oc, start=1, graph=ptnGoe)

# fake training data, indicating format
nnodes <- 851
max.day <- 1312
nsimu <- 20
max.case.per.day <- 10
train.data.fake <- list()
for (j in 1:nnodes) {
  train.data.fake[[j]] <- matrix(sample.int(max.day, 
    size = nsimu*nnodes, replace = TRUE), nrow = nsimu, ncol = nnodes)
}
obs.vec <- (1:9)
candidate.thres <- 0.3
mu.lambda.list <- compute_mu_lambda(train.data.fake, obs.vec, candidate.thres)
# matrix representing number of cases per node per day
cases.node.day <- matrix(sample.int(max.case.per.day, 
  size = nnodes*max.day, replace = TRUE), nrow = nnodes, ncol = max.day)
nnodes <- dim(cases.node.day)[1] # number of nodes
# fixed threshold for all nodes - 10 infected people
thres.vec <- rep(10, nnodes)
# flat/non-informative prior
prior <- rep(1, nnodes) 
result2.df <- origin(events = cases.node.day, type = "bayesian",
                     thres.vec = thres.vec,
                     obs.vec = obs.vec,
                     mu.mat=mu.lambda.list$mu.mat, lambda.list = mu.lambda.list$lambda.list, 
                     poss.candidate.vec=mu.lambda.list$poss.candidate.vec,
                     prior=prior, use.prior=TRUE)

NetOrigin documentation built on Sept. 8, 2023, 5:58 p.m.