Package 'hybridModels' reference manual

Title:	An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks
Description:	Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.
Authors:	Fernando S. Marques [aut, cre], Jose H. H. Grisi-Filho [aut], Marcos Amaku [aut]
Maintainer:	Fernando S. Marques <[email protected]>
License:	GPL (>= 2)
Version:	0.3.7
Built:	2025-02-16 04:01:35 UTC
Source:	https://github.com/fernandosm/hybridmodels

It builds an object of a pre-specified class.

Description

buildModelClass is generic function that calls a method to create a object base on model's name.

Usage

buildModelClass(
  x,
  var.names,
  init.cond,
  model.parms,
  probWeights,
  emigrRule,
  prop.func = NULL,
  state.var = NULL,
  infl.var = NULL,
  state.change.matrix = NULL
)
buildModelClass(
  x,
  var.names,
  init.cond,
  model.parms,
  probWeights,
  emigrRule,
  prop.func = NULL,
  state.var = NULL,
  infl.var = NULL,
  state.change.matrix = NULL
)

Arguments

`x`	is an empty object of a class requested.
`var.names`	a `list` with variable names of the network: the donor node, the receiver node, the time when each connection between donor to the receiver happened and the weight of these connection. The variables names must be "from", "to", "Time" and "arc", respectively.
`init.cond`	a named `vector` with initial conditions.
`model.parms`	a named `vector` with model's parameters.
`probWeights`	a named `vector` (optional and for migration type only) mapping state variables to migration probability weights based on state variables. These argument can be used to give weights for sampling individuals from node. They need not sum to one, they should be non-negative and not zero. For more information on the sampling method sample.
`emigrRule`	a string (optional and for migration type only) stating how many individual emigrate based on state variables. It requires that the network have weights instead of number of individuals that migrate.
`prop.func`	a character `vector` with propensity functions of a generic node. See references for more details
`state.var`	a character `vector` with the state variables of the propensity functions.
`infl.var`	a named `vector` mapping state variables to influence variables.
`state.change.matrix`	is a state-change `matrix`. See references for more details

Value

An object of the class requested.

References

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
)
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).

Details

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

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.

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

# 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)
# 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)

Hybrid model simulation.

Description

hybridModel function runs hybrid models simulations.

Usage

hybridModel(
  network = stop("undefined 'network'"),
  var.names = NULL,
  link.type = "migration",
  model = "custom",
  probWeights = NULL,
  emigrRule = NULL,
  init.cond = stop("undefined 'initial conditions'"),
  fill.time = F,
  model.parms = stop("undefined 'model parmeters'"),
  prop.func = NULL,
  state.var = NULL,
  infl.var = NULL,
  state.change.matrix = NULL,
  ssa.method = NULL,
  nodesCensus = NULL,
  sim.number = 1,
  pop.correc = TRUE,
  num.cores = "max"
)
hybridModel(
  network = stop("undefined 'network'"),
  var.names = NULL,
  link.type = "migration",
  model = "custom",
  probWeights = NULL,
  emigrRule = NULL,
  init.cond = stop("undefined 'initial conditions'"),
  fill.time = F,
  model.parms = stop("undefined 'model parmeters'"),
  prop.func = NULL,
  state.var = NULL,
  infl.var = NULL,
  state.change.matrix = NULL,
  ssa.method = NULL,
  nodesCensus = NULL,
  sim.number = 1,
  pop.correc = TRUE,
  num.cores = "max"
)

Arguments

`network`	a `data.frame` with variables that describe the donor node, the receiver node, the time when each connection between donor to the receiver happened and the number of individual or weight of these connection.
`var.names`	a `list` with variable names of the network: the donor node, the receiver node, the time when each connection between donor to the receiver happened and the weight of these connection. The variables names must be "from", "to", "Time" and "arc", respectively.
`link.type`	a `character` describing the link type between nodes. There are two types: 'migration' and 'influence'. In the migration link type there are actual migration between nodes. In the influence link type individuals does not migrate, just influences another node.
`model`	a `character` describing model's name.
`probWeights`	a named `vector` (optional and for migration type only) mapping state variables to migration probability weights based on state variables. These argument can be used to give weights for sampling individuals from node. They need not sum to one, they should be non-negative and not zero. For more information on the sampling method sample.
`emigrRule`	a string (optional and for migration type only) stating how many individual emigrate based on state variables. It requires that the network have weights instead of number of individuals that migrate.
`init.cond`	a named `vector` with initial conditions.
`fill.time`	It indicates whether to return all dates or just the dates when nodes get connected.
`model.parms`	a named `vector` with model's parameters.
`prop.func`	a character `vector` with propensity functions of a generic node. See references for more details
`state.var`	a character `vector` with the state variables of the propensity functions.
`infl.var`	a named `vector` mapping state variables to influence variables.
`state.change.matrix`	is a state-change `matrix`. See references for more details
`ssa.method`	a `list` with SSA parameters. The default method is the direct method. See references for more details
`nodesCensus`	a `data.frame` with the first column describing nodes' ID, the second column with the number of individuals and the third describing the day of the census.
`sim.number`	Number of repetitions.The default value is 1
`pop.correc`	Whether `hybridModel` function tries to balance the number of individuals or not. The default value is TRUE.
`num.cores`	number of threads/cores that the simulation will use. the default value is num.cores = 'max', the Algorithm will use all threads/cores available.

Value

Object containing a data.frame (results) with the number of individuals through time per node and per state.

References

[1] Pineda-krch, M. (2008). GillespieSSA : Implementing the Stochastic Simulation Algorithm in R. Journal of Statistical Software, Volume 25 Issue 12 <doi:10.1146/annurev.physchem.58.032806.104637>.

[2] 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>.

Examples

# Migration model
# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check the number of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
plot(sim.results, plot.type = 'subpop.mean')

# changing plot layout with ggplot2 (example)
# uncomment the lines below to test new layout exemple
#library(ggplot2)
#plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') + 
#  theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))

# Influence model
# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * (I + i) / (S + I + s + i)', 'gamma * I')
state.var <- c('S', 'I')
infl.var <- c(S = "s", I = "i") # mapping influence
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
# Uncomment to run
# sim.results <- hybridModel(network = networkSample, var.names = var.names,
#                            model.parms = model.parms, state.var = state.var,
#                            infl.var = infl.var, prop.func = prop.func,
#                            init.cond = init.cond,
#                            state.change.matrix = state.change.matrix,
#                            sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
# plot(sim.results, plot.type = 'subpop.mean')

# Migration model
# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check the number of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
plot(sim.results, plot.type = 'subpop.mean')

# changing plot layout with ggplot2 (example)
# uncomment the lines below to test new layout exemple
#library(ggplot2)
#plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') + 
#  theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))

# Influence model
# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * (I + i) / (S + I + s + i)', 'gamma * I')
state.var <- c('S', 'I')
infl.var <- c(S = "s", I = "i") # mapping influence
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
# Uncomment to run
# sim.results <- hybridModel(network = networkSample, var.names = var.names,
#                            model.parms = model.parms, state.var = state.var,
#                            infl.var = infl.var, prop.func = prop.func,
#                            init.cond = init.cond,
#                            state.change.matrix = state.change.matrix,
#                            sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
# plot(sim.results, plot.type = 'subpop.mean')

hybridModels: an R package for stochastic simulation of disease spreading in dynamic networks.

Description

The hybridModels package provides functions to simulate stochastic models in dynamics networks, using two processes in different scales: 1 Global scale to simulate the transmission from one node to another 2 Local scale to simulate the transmission inside the node

Modeling transmission of diseases

'Framework to run n simulations in dynamic network and plot results'

Daily record of animal's movement (from 2012 to 2013).

Description

One dataset containing the number of animals that were moved from one node to another.

Usage

networkSample
networkSample

Format

A data frame with 78 rows and 4 variables:

Day: The day when the movement occurs
originID: The ID of the origin premises
destinationID: The ID of the destination premises
num.animals: The number of animals traded

Source

ADAGRO

Information about animal premises (from 2012 to 2013).

Description

A dataset containing animal premises' identification and census.

Usage

nodesCensus
nodesCensus

Format

A data frame with 507 rows and 2 variables:

nodes.ID: The ID of the premises
pop: premises's population size

Summary plots for hybrid Models

Description

plot.HM is a method to plot hybrid models from this package

Usage

## S3 method for class 'HM'
plot(x, sim = 1, plot.type = "subpop", facet.scales = "free_y", ...)
## S3 method for class 'HM'
plot(x, sim = 1, plot.type = "subpop", facet.scales = "free_y", ...)

Arguments

`x`	`HM` object
`sim`	indicates which simulation to plot.
`plot.type`	plots the mean number of each state variable for the whole population ('pop.mean'), or the subpopulations of a particular simulation ('subpop', default value), or the mean of each subpopulation ('subpop.mean').
`facet.scales`	should scales be fixed ("free_y", the default), free ("free"), or free in one dimension ("free_x", "free_y"). See ggplot2 package for more details.
`...`	arguments to be passed to methods.

References

[1] 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>.

Examples

# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
plot(sim.results, plot.type = 'subpop.mean')

# changing plot layout with ggplot2 (example)
# uncomment the lines below to test new layout exemple
#library(ggplot2)
#plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') + 
#  theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))

# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 2, num.cores = 2)

# default plot layout (plot.types: 'pop.mean', 'subpop', or 'subpop.mean')
plot(sim.results, plot.type = 'subpop.mean')

# changing plot layout with ggplot2 (example)
# uncomment the lines below to test new layout exemple
#library(ggplot2)
#plot(sim.results, plot.type = 'subpop') + ggtitle('New Layout') + 
#  theme_bw() + theme(axis.title = element_text(size = 14, face = "italic"))

It runs the chosen hybrid model.

Description

simHM is generic function that calls a method to run the simulation base on object's class

Usage

simHM(x, network, sim.number, num.cores = "max", fill.time)
simHM(x, network, sim.number, num.cores = "max", fill.time)

Arguments

`x`	of a specific class of model.
`network`	a `data.frame` with variables that describe the donor node, the receiver node, the time when each connection between donor to the receiver happened and the number of individual or weight of these connection.
`sim.number`	Number of repetitions.The default value is 1
`num.cores`	number of threads/cores that the simulation will use. the default value is num.cores = 'max', the Algorithm will use all threads/cores available.
`fill.time`	It indicates whether to return all dates or just the dates when nodes get connected.

Value

A data.frame with the number of individuals through time per node, per state and per simulation.

References

summary for hybrid models

Description

summary.HM is a method to print a summary with basic description of nodes' states at a specific time (the time must be present in the network data). The default value is Null, that means it prints nodes' final states.

Usage

## S3 method for class 'HM'
summary(object, at = NULL, stateVars = NULL, nodes = NULL, ...)
## S3 method for class 'HM'
summary(object, at = NULL, stateVars = NULL, nodes = NULL, ...)

Arguments

`object`	`HM` object
`at`	the date (as character) that will be used to print the summary
`stateVars`	`vector` containing the state variable to summarize. The default value is NULL, which will print a summary with all states.
`nodes`	`vector` containing the nodes of interest. The default value is NULL, which will print a summary with all nodes.
`...`	arguments to be passed to methods.

References

Examples

# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 4, num.cores = 2)

summary(sim.results, stateVars = c('S', 'I'), nodes = c(36812, 36813))

# Parameters and initial conditions for an SIS model
# loading the data set 
data(networkSample) # help("networkSample"), for more info
networkSample <- networkSample[which(networkSample$Day < "2012-03-20"),]

var.names <- list(from = 'originID', to = 'destinationID', Time = 'Day',
                  arc = 'num.animals')
                  
prop.func <- c('beta * S * I / (S + I)', 'gamma * I')
state.var <- c('S', 'I')
state.change.matrix <- matrix(c(-1,  1,  # S
                                 1, -1), # I
                              nrow = 2, ncol = 2, byrow = TRUE)
                              
model.parms <- c(beta = 0.1, gamma = 0.01)

init.cond <- rep(100, length(unique(c(networkSample$originID,
                                      networkSample$destinationID))))
names(init.cond) <- paste('S', unique(c(networkSample$originID,
                                        networkSample$destinationID)), sep = '')
init.cond <- c(init.cond, c(I36811 = 10, I36812 = 10)) # adding infection
                  
# running simulations, check num of cores available (num.cores)
sim.results <- hybridModel(network = networkSample, var.names = var.names,
                           model.parms = model.parms, state.var = state.var,
                           prop.func = prop.func, init.cond = init.cond,
                           state.change.matrix = state.change.matrix,
                           sim.number = 4, num.cores = 2)

summary(sim.results, stateVars = c('S', 'I'), nodes = c(36812, 36813))

Package 'hybridModels'

Help Index

It builds an object of a pre-specified class.

Description

Usage

Arguments

Value

References

Finding elements in contact chains of a dynamic network.

Description

Usage

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Details

Value

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Examples

Hybrid model simulation.

Description

Usage

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Value

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See Also

Examples

hybridModels: an R package for stochastic simulation of disease spreading in dynamic networks.

Description

Modeling transmission of diseases

Daily record of animal's movement (from 2012 to 2013).

Description

Usage

Format

Source

Information about animal premises (from 2012 to 2013).

Description

Usage

Format

Summary plots for hybrid Models

Description

Usage

Arguments

References

Examples

It runs the chosen hybrid model.

Description

Usage

Arguments

Value

References

See Also

summary for hybrid models

Description

Usage

Arguments

References

Examples