Type:	Package
Title:	A Forward Agent-Based Transmission Chain Simulator
Version:	1.1.2
Description:	The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
URL:	https://github.com/slequime/nosoi, https://slequime.github.io/nosoi/
BugReports:	https://github.com/slequime/nosoi/issues
Language:	en-US
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.2.3
Depends:	data.table (≥ 1.12.0), R (≥ 3.5.0)
Imports:	stats (≥ 3.5.2), methods (≥ 3.5.2), raster (≥ 2.8-19)
Suggests:	testthat (≥ 2.1.0), knitr, rmarkdown, igraph, ggplot2, ggnetwork, intergraph, viridis, gifski, png, gganimate, ape (≥ 5.3), tidytree (≥ 0.3.3), treeio (≥ 1.14.0), ggtree (≥ 2.4.0), magrittr (≥ 1.5), dplyr (≥ 0.8.0), covr
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-02-09 12:29:40 UTC; slequime
Author:	Sebastian Lequime [aut, cre], Paul Bastide [aut], Simon Dellicour [aut], Philippe Lemey [aut], Guy Baele [aut], Thijs Janzen [ctb]
Maintainer:	Sebastian Lequime <sebastian.lequime@gmail.com>
Repository:	CRAN
Date/Publication:	2024-02-09 12:50:02 UTC

Checks if the simulator can start

Description

Checks if the simulator can start: did the user provide a length, a maximum number of infected individuals and a number of "seeding" individuals?

Usage

CoreSanityChecks(length.sim, max.infected, init.individuals)

Arguments

Checks if a function is properly formatted

Description

Checks if the function is properly formatted given the user's input and nosoi's requirements.

Usage

FunctionSanityChecks(
  pFunc,
  name,
  param.pFunc,
  timeDep,
  diff,
  hostCount,
  continuous,
  stateNames
)

Arguments

Checks if the matrix is properly formatted

Description

Checks if the transition matrix is properly formatted given the user's input and nosoi's requirements.

Usage

MatrixSanityChecks(structure.matrix, init.structure, none.at.start = NULL)

Arguments

Checks if the raster is properly formatted

Description

Checks if the environmental raster is properly formatted given the user's input and nosoi's requirements.

Usage

RasterSanityChecks(structure.raster, init.structure, none.at.start = NULL)

Arguments

Add one tip

Description

Add a tip to the transmission tree corresponding to the sampled individual.

Usage

add_node_tip(tree, host, time, label, state)

Arguments

Value

modified tree, with on extra tip of length zero at the right place of the tree

Apply a function to table.host

Description

Return a vector of the results of the function

Usage

applyFunctionToHosts(res, pres.time, pasedFunction, active.hosts)

Arguments

Value

result vector

Cumulative number of infected hosts at time t

Description

For a given time t, this function returns the cumulative number of infected units since the start of the simulation.

Usage

cumulativeInfected(table.nosoi, t)

Arguments

Value

Number of infected units at time t

See Also

nosoiSim

Draw newly infected

Description

For each encounter, simulate whether a new individual is infected.

Usage

drawBernouilli(p)

Arguments

Value

Boolean vector giving the newly infected individuals.

Dual-host pathogen in structured (continuous) hosts populations

Description

This function runs a dual-host transmission chain simulation, with structured hosts populations (such as spatial features) in a shared continuous space. The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is passed. The movement of hosts on the continuous space map is a random walk (Brownian motion) that can be modified towards a biased random walk where hosts tend to be attracted to higher values of the environmental variable defined by the raster.

Usage

dualContinuous(
  length.sim,
  max.infected.A,
  max.infected.B,
  init.individuals.A,
  init.individuals.B,
  init.structure.A,
  init.structure.B,
  structure.raster.A,
  structure.raster.B,
  pExit.A,
  param.pExit.A,
  timeDep.pExit.A = FALSE,
  diff.pExit.A = FALSE,
  hostCount.pExit.A = FALSE,
  pMove.A,
  param.pMove.A,
  timeDep.pMove.A = FALSE,
  diff.pMove.A = FALSE,
  hostCount.pMove.A = FALSE,
  sdMove.A,
  param.sdMove.A,
  diff.sdMove.A = FALSE,
  timeDep.sdMove.A = FALSE,
  hostCount.sdMove.A = FALSE,
  attracted.by.raster.A = FALSE,
  nContact.A,
  param.nContact.A,
  timeDep.nContact.A = FALSE,
  diff.nContact.A = FALSE,
  hostCount.nContact.A = FALSE,
  pTrans.A,
  param.pTrans.A,
  timeDep.pTrans.A = FALSE,
  diff.pTrans.A = FALSE,
  hostCount.pTrans.A = FALSE,
  prefix.host.A = "H",
  pExit.B,
  param.pExit.B,
  timeDep.pExit.B = FALSE,
  diff.pExit.B = FALSE,
  hostCount.pExit.B = FALSE,
  pMove.B,
  param.pMove.B,
  timeDep.pMove.B = FALSE,
  diff.pMove.B = FALSE,
  hostCount.pMove.B = FALSE,
  sdMove.B,
  param.sdMove.B,
  diff.sdMove.B = FALSE,
  timeDep.sdMove.B = FALSE,
  hostCount.sdMove.B = FALSE,
  attracted.by.raster.B = FALSE,
  nContact.B,
  param.nContact.B,
  timeDep.nContact.B = FALSE,
  diff.nContact.B = FALSE,
  hostCount.nContact.B = FALSE,
  pTrans.B,
  param.pTrans.B,
  timeDep.pTrans.B = FALSE,
  diff.pTrans.B = FALSE,
  hostCount.pTrans.B = FALSE,
  prefix.host.B = "V",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Raster

The structure raster(s) provided provided should of class raster. High values of the environmental variable can attract hosts if attracted.by.raster is TRUE. Raster have to share the same space (i.e. also the same cell size and ID).

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), current.env.value (optional, only if diff is TRUE), host.count.A or host.count.B (optional, only if hostCount is TRUE) and parameters specified in the list.

Structure Parameters

The pMove function should return a single probability (a number between 0 and 1), and sdMove a real number (keep in mind this number is related to your coordinate space).

The use of diff (switch to TRUE) makes the corresponding function use the argument current.env.value (for "current environmental value").

The use of hostCount (switch to TRUE) makes the corresponding function use the argument host.count.

Suffixes

The suffix .A or .B specifies if the considered function or parameter concerns host type A or B.

See Also

For simulations with a discrete structure, see dualDiscrete. For simulations without any structures, see dualNone.

Examples

library(raster)

#Generating a raster for the movement
set.seed(860)

test.raster <- raster(nrows=100, ncols=100, xmn=-50, xmx=50, ymn=-50,ymx=50)
test.raster[] <- runif(10000, -80, 180)
test.raster <- focal(focal(test.raster, w=matrix(1, 5, 5), mean), w=matrix(1, 5, 5), mean)

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Move_fct  <- function(t){return(0.1)}

sdMove_fct = function(t,current.env.value){return(100/(current.env.value+1))}

p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

start.pos <- c(0,0)

set.seed(805)
test.nosoi <- nosoiSim(type="dual", popStructure="continuous",
                      length.sim=200,
                      max.infected.A=500,
                      max.infected.B=500,
                      init.individuals.A=1,
                      init.individuals.B=0,
                      init.structure.A=start.pos,
                      init.structure.B=NA,
                      structure.raster.A=test.raster,
                      structure.raster.B=test.raster,
                      pExit.A=p_Exit_fct,
                      param.pExit.A=NA,
                      timeDep.pExit.A=FALSE,
                      diff.pExit.A=FALSE,
                      pMove.A=p_Move_fct,
                      param.pMove.A=NA,
                      timeDep.pMove.A=FALSE,
                      diff.pMove.A=FALSE,
                      diff.sdMove.A=TRUE,
                      sdMove.A=sdMove_fct,
                      param.sdMove.A=NA,
                      attracted.by.raster.A=TRUE,
                      nContact.A=time_contact,
                      param.nContact.A=NA,
                      timeDep.nContact.A=FALSE,
                      diff.nContact.A=FALSE,
                      pTrans.A=proba,
                      param.pTrans.A=list(p_max=p_max_fct,
                                          t_incub=t_incub_fct),
                      timeDep.pTrans.A=FALSE,
                      diff.pTrans.A=FALSE,
                      prefix.host.A="H",
                      pExit.B=p_Exit_fct,
                      param.pExit.B=NA,
                      timeDep.pExit.B=FALSE,
                      diff.pExit.B=FALSE,
                      pMove.B=p_Move_fct,
                      param.pMove.B=NA,
                      timeDep.pMove.B=FALSE,
                      diff.pMove.B=FALSE,
                      diff.sdMove.B=TRUE,
                      sdMove.B=sdMove_fct,
                      param.sdMove.B=NA,
                      attracted.by.raster.B=TRUE,
                      nContact.B=time_contact,
                      param.nContact.B=NA,
                      timeDep.nContact.B=FALSE,
                      diff.nContact.B=FALSE,
                      pTrans.B=proba,
                      param.pTrans.B=list(p_max=p_max_fct,
                                          t_incub=t_incub_fct),
                      timeDep.pTrans.B=FALSE,
                      diff.pTrans.B=FALSE,
                      prefix.host.B="V")
test.nosoi

Dual-host pathogen in structured (discrete) hosts populations

Description

This function, that can be wrapped within nosoiSim, runs a dual-host transmission chain simulation, with discrete hosts populations structures (e.g. spatial, socio-economic, etc.). The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is crossed.

Usage

dualDiscrete(
  length.sim,
  max.infected.A,
  max.infected.B,
  init.individuals.A,
  init.individuals.B,
  init.structure.A,
  init.structure.B,
  structure.matrix.A,
  structure.matrix.B,
  pExit.A,
  param.pExit.A,
  timeDep.pExit.A = FALSE,
  diff.pExit.A = FALSE,
  hostCount.pExit.A = FALSE,
  pMove.A,
  param.pMove.A,
  timeDep.pMove.A = FALSE,
  diff.pMove.A = FALSE,
  hostCount.pMove.A = FALSE,
  nContact.A,
  param.nContact.A,
  timeDep.nContact.A = FALSE,
  diff.nContact.A = FALSE,
  hostCount.nContact.A = FALSE,
  pTrans.A,
  param.pTrans.A,
  timeDep.pTrans.A = FALSE,
  diff.pTrans.A = FALSE,
  hostCount.pTrans.A = FALSE,
  prefix.host.A = "H",
  pExit.B,
  param.pExit.B,
  timeDep.pExit.B = FALSE,
  diff.pExit.B = FALSE,
  hostCount.pExit.B = FALSE,
  pMove.B,
  param.pMove.B,
  timeDep.pMove.B = FALSE,
  diff.pMove.B = FALSE,
  hostCount.pMove.B = FALSE,
  nContact.B,
  param.nContact.B,
  timeDep.nContact.B = FALSE,
  diff.nContact.B = FALSE,
  hostCount.nContact.B = FALSE,
  pTrans.B,
  param.pTrans.B,
  timeDep.pTrans.B = FALSE,
  diff.pTrans.B = FALSE,
  hostCount.pTrans.B = FALSE,
  prefix.host.B = "V",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Structure Matrix

The structure/transition matrix provided provided should of class matrix, with the same number of rows and columns, rows representing departure state and column the arrival state. All rows should add to 1. Probability values can be different for hosts A and B (so two different matrices), but the name of the column and the rows should be shared.

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), current.in (optional, only if diff is TRUE), host.count.A or host.count.B (optional, only if hostCount is TRUE) and parameters specified in the list.

Structure Parameters

The pMove function should return a single probability (a number between 0 and 1).

The use of diff (switch to TRUE) makes the corresponding function use the argument current.in (for "currently in"). Your function should in that case give a result for every possible discrete state.

The use of hostCount (switch to TRUE) makes the corresponding function use the argument host.count.

Suffixes

The suffix .A or .B specifies if the considered function or parameter concerns host type A or B.

See Also

For simulations with a structure in continuous space, see dualContinuous. For simulations without any structures, see dualNone.

Examples

#Host A
t_infectA_fct <- function(x){rnorm(x,mean = 12,sd=3)}
pTrans_hostA <- function(t,t_infectA){
 if(t/t_infectA <= 1){p=sin(pi*t/t_infectA)}
 if(t/t_infectA > 1){p=0}
 return(p)
}

p_Move_fctA  <- function(t){return(0.1)}

p_Exit_fctA  <- function(t,t_infectA){
 if(t/t_infectA <= 1){p=0}
 if(t/t_infectA > 1){p=1}
 return(p)
}

time_contact_A = function(t){sample(c(0,1,2),1,prob=c(0.2,0.4,0.4))}

t_incub_fct_B <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct_B <- function(x){rbeta(x,shape1 = 5,shape2=2)}

p_Exit_fct_B  <- function(t,current.in){
 if(current.in=="A"){return(0.1)}
 if(current.in=="B"){return(0.2)}
 if(current.in=="C"){return(1)}}

pTrans_hostB <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact_B = function(t){round(rnorm(1, 3, 1), 0)}

transition.matrix = matrix(c(0,0.2,0.4,0.5,0,0.6,0.5,0.8,0),
                          nrow = 3, ncol = 3,
                          dimnames=list(c("A","B","C"),c("A","B","C")))

set.seed(6262)
test.nosoi <- nosoiSim(type="dual", popStructure="discrete",
                      length.sim=40,
                      max.infected.A=100,
                      max.infected.B=200,
                      init.individuals.A=1,
                      init.individuals.B=0,
                      init.structure.A="A",
                      init.structure.B=NA,
                      structure.matrix.A=transition.matrix,
                      structure.matrix.B=transition.matrix,
                      pExit.A = p_Exit_fctA,
                      param.pExit.A = list(t_infectA = t_infectA_fct),
                      pMove.A=p_Move_fctA,
                      param.pMove.A=NA,
                      timeDep.pMove.A=FALSE,
                      diff.pMove.A=FALSE,
                      timeDep.pExit.A=FALSE,
                      nContact.A = time_contact_A,
                      param.nContact.A = NA,
                      timeDep.nContact.A=FALSE,
                      pTrans.A = pTrans_hostA,
                      param.pTrans.A = list(t_infectA=t_infectA_fct),
                      timeDep.pTrans.A=FALSE,
                      prefix.host.A="H",
                      pExit.B = p_Exit_fct_B,
                      param.pExit.B = NA,
                      timeDep.pExit.B=FALSE,
                      diff.pExit.B=TRUE,
                      pMove.B=NA,
                      param.pMove.B=NA,
                      timeDep.pMove.B=FALSE,
                      diff.pMove.B=FALSE,
                      nContact.B = time_contact_B,
                      param.nContact.B = NA,
                      timeDep.nContact.B=FALSE,
                      pTrans.B = pTrans_hostB,
                      param.pTrans.B = list(p_max=p_max_fct_B,
                                            t_incub=t_incub_fct_B),
                      timeDep.pTrans.B=FALSE,
                      prefix.host.B="V")

test.nosoi

Dual-host pathogen in homogeneous hosts populations

Description

This function, that can be wrapped within nosoiSim, runs a dual-host transmission chain simulation, without any structure features in both hosts populations. The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is crossed.

Usage

dualNone(
  length.sim,
  max.infected.A,
  max.infected.B,
  init.individuals.A,
  init.individuals.B,
  pExit.A,
  param.pExit.A,
  timeDep.pExit.A = FALSE,
  nContact.A,
  param.nContact.A,
  timeDep.nContact.A = FALSE,
  pTrans.A,
  param.pTrans.A,
  timeDep.pTrans.A = FALSE,
  prefix.host.A = "H",
  pExit.B,
  param.pExit.B,
  timeDep.pExit.B = FALSE,
  nContact.B,
  param.nContact.B,
  timeDep.nContact.B = FALSE,
  pTrans.B,
  param.pTrans.B,
  timeDep.pTrans.B = FALSE,
  prefix.host.B = "V",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Suffixes

The suffix .A or .B specifies if the considered function or parameter concerns host type A or B.

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), parameters specified in the list.

See Also

For simulations with a discrete structured host population, see dualDiscrete. For simulations with a structured population in continuous space, dualContinuous

Examples

 #Host A
t_infectA_fct <- function(x){rnorm(x,mean = 12,sd=3)}
pTrans_hostA <- function(t,t_infectA){
  if(t/t_infectA <= 1){p=sin(pi*t/t_infectA)}
  if(t/t_infectA > 1){p=0}
  return(p)
}

p_Exit_fctA  <- function(t,t_infectA){
  if(t/t_infectA <= 1){p=0}
  if(t/t_infectA > 1){p=1}
  return(p)
}

time_contact_A = function(t){sample(c(0,1,2),1,prob=c(0.2,0.4,0.4))}

#Host B
t_incub_fct_B <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct_B <- function(x){rbeta(x,shape1 = 5,shape2=2)}

p_Exit_fct_B  <- function(t,prestime){(sin(prestime/12)+1)/5}

pTrans_hostB <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact_B = function(t){round(rnorm(1, 3, 1), 0)}

set.seed(90)
test.nosoi <- nosoiSim(type="dual", popStructure="none",
                       length.sim=40,
                       max.infected.A=100,
                       max.infected.B=200,
                       init.individuals.A=1,
                       init.individuals.B=0,
                       pExit.A = p_Exit_fctA,
                       param.pExit.A = list(t_infectA = t_infectA_fct),
                       timeDep.pExit.A=FALSE,
                       nContact.A = time_contact_A,
                       param.nContact.A = NA,
                       timeDep.nContact.A=FALSE,
                       pTrans.A = pTrans_hostA,
                       param.pTrans.A = list(t_infectA=t_infectA_fct),
                                             timeDep.pTrans.A=FALSE,
                       prefix.host.A="H",
                       pExit.B = p_Exit_fct_B,
                       param.pExit.B = NA,
                       timeDep.pExit.B=TRUE,
                       nContact.B = time_contact_B,
                       param.nContact.B = NA,
                       timeDep.nContact.B=FALSE,
                       pTrans.B = pTrans_hostB,
                       param.pTrans.B = list(p_max=p_max_fct_B,
                                            t_incub=t_incub_fct_B),
                       timeDep.pTrans.B=FALSE,
                       prefix.host.B="V")

test.nosoi

End message

Description

Message that ends the simulation

Usage

endMessage(Host.count.A, Host.count.B = NULL, pres.time, type = "single")

Arguments

Gets the cumulative number of infected hosts for the full length of the simulation

Description

This function computes from the output of a nosoiSim simulation the cumulative count of infected hosts at each time step of the simulation. The output is a data.table.

Usage

getCumulative(nosoi.output)

Arguments

Value

The output is a data.table with the following structure:

t

Time-step (integer).

Count

Cumulative number of infected hosts at given time-step.

type

Host-type, identified by its user-defined prefix.

See Also

summary.nosoiSim

Gets the current number of infected hosts for the full length of the simulation

Description

This function computes from the output of a nosoiSim simulation the dynamic count of infected hosts at each time step (and each state if discrete structure) of the simulation. The output is a data.table.

Usage

getDynamic(nosoi.output)

Arguments

Value

The output is a data.table with the following structure:

state

(only when discrete structure) Given state

Count

Current number of infected hosts at given time-step.

type

Host-type, identified by its user-defined prefix.

t

Time-step (integer).

See Also

summary.nosoiSim

Get Exiting or Moving individuals

Description

Return a vector of exiting individuals.

Usage

getExitingMoving(res, pres.time, pasedFunction)

Arguments

Value

Boolean vector

Extracts specific data from a nosoiSim object

Description

This function extracts data user-defined data (i.e. table.hosts, N.infected, table.state or popStructure) from a nosoiSim object.

Usage

getHostData(
  nosoi.output,
  what = c("table.hosts", "N.infected", "table.state", "popStructure"),
  pop = "A"
)

Arguments

Value

Returns a data.table with the requested data.

See Also

To directly extract table.hosts or table.state, you can also use getTableHosts and getTableState respectively.

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact <- function(t){round(rnorm(1, 3, 1), 0)}

test.nosoi <- nosoiSim(type="single", popStructure="none",
                       length=40,
                       max.infected=100,
                       init.individuals=1,
                       nContact=time_contact,
                       param.nContact=NA,
                       pTrans = proba,
                       param.pTrans = list(p_max=p_max_fct,
                                           t_incub=t_incub_fct),
                       pExit=p_Exit_fct,
                       param.pExit=NA)


data.extracted <- getHostData(test.nosoi, "table.hosts", "A")

get Position Infected

Description

Return the relevant position of the infected individual.

Usage

getPositionInfected(nosoiSim, df.meetTransmit, i)

Arguments

Value

A vector of positions

Gets R0 from a nosoi simulation

Description

Gets an estimate of secondary cases (what R0 usually tries to estimate) and its distribution from the output of a nosoiSim simulation. The actual calculation is based on inactive hosts at the end of the simulation to avoid bias introduced by hosts that have not finished their transmission potential.

Usage

getR0(nosoi.output)

Arguments

Details

Current getR0 (after and including version 1.1.0) is a corrected version. In previous versions (prior to 1.1.0), the output included in its computation hosts that should not have been counted (still active).

Value

A list with the following items:

N.inactive

Number of inactive hosts at the end of the simulation.

R0.mean

Mean R0 based on the distribution (see below).

R0.dist

Distribution for each host of the secondary cases it generated (in case of dual-hosts, then the secondary cases of the same host-type).

See Also

summary.nosoiSim

Extracts table.hosts from a nosoiSim object

Description

This function extracts the table.hosts for the request host-type from a nosoiSim object.

Usage

getTableHosts(nosoi.output, pop = "A")

Arguments

Value

Returns a data.table with the requested data. The table.hosts (class data.table) contains informations about each host that has been simulated (one row is one host). The structure of the table is the following:

hosts.ID

Unique identifier for the host, based on user-defined prefix and an integer.

inf.by

Unique identifier for the host that infected the current one.

inf.in

(only if structure is present) State or coordinates (in that case inf.in.x and inf.in.y) in which the host was infected.

current.in

(only if structure is present) State or coordinates (in that case current.in.x and current.in.y) in which the host is at the end of the simulation.

current.env.value

(only if continuous structure is present) Environmental value (raster cell value) in which the host is at the end of the simulation.

current.cell.raster

(only if continuous structure is present) Raster cell numeric ID in which the host is at the end of the simulation.

host.count

(only if structure is present) Host count in the current state or raster cell (beware, updated only if used).

inf.time

When did the host enter the simulation (infection time).

out.time

When did the host exit the simulation (NA if still active).

active

Is the host still active at the end of the simulation (TRUE for YES, FALSE for NO)?

parameters

The remaining columns are the sampled values for the individual-based parameters (if any) specified by the user.

Extracts table.state from a nosoiSim object

Description

This function extracts the table.state for the request host-type from a nosoiSim object. table.state is present only if there is any structure (discrete or continuous) used.

Usage

getTableState(nosoi.output, pop = "A")

Arguments

Value

Returns a data.table with the requested data. The table.state (class data.table) contains informations the location of each host during time (one row is one host at one location). The structure of the table is the following:

hosts.ID

Unique identifier for the host, based on user-defined prefix and an integer.

state

State or coordinates (in that case state.x and state.y) in which the host is during that time interval.

current.env.value

(only if continuous structure is present) Environmental value (raster cell value) in which the host is at the end of the simulation.

current.cell.raster

(only if continuous structure is present) Raster cell numeric ID in which the host is at the end of the simulation.

time.from

Time-step at which the host moved to the location.

time.to

Time-step at which the host exited the location (either by exiting the simulation or moving somewhere else).

Gets the full transmission tree (phylogenetic tree-like) from a nosoi simulation

Description

From a nosoi simulated epidemics, this function extracts the full transmission tree in a form mimicking a phylogenetic tree.

Usage

getTransmissionTree(nosoiInf)

Arguments

Details

This function uses packages tidytree and treeio, that rely on ape.

Value

A tree of class treedata, containing a phylogenetic tree based on the transmission chain and the mapped data at all the nodes.

See Also

For exporting the annotated tree to other software packages, see functions in treeio (e.g. write.beast).

To sub-sample this tree, see functions sampleTransmissionTree and sampleTransmissionTreeFromExiting

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}
p_Move_fct  <- function(t){return(0.1)}

proba <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

transition.matrix = matrix(c(0, 0.2, 0.4, 0.5, 0, 0.6, 0.5, 0.8, 0),
                           nrow = 3, ncol = 3,
                           dimnames = list(c("A", "B", "C"), c("A", "B", "C")))

set.seed(805)
test.nosoi <- nosoiSim(type="single", popStructure="discrete",
                        length=20,
                        max.infected=100,
                        init.individuals=1,
                        init.structure="A",
                        structure.matrix=transition.matrix,
                        pMove=p_Move_fct,
                        param.pMove=NA,
                        nContact=time_contact,
                        param.nContact=NA,
                        pTrans = proba,
                        param.pTrans = list(p_max=p_max_fct,
                                            t_incub=t_incub_fct),
                        pExit=p_Exit_fct,
                        param.pExit=NA
)

## Make sure all needed packages are here
if (requireNamespace("ape", quietly = TRUE) &&
    requireNamespace("tidytree", quietly = TRUE) &&
    requireNamespace("treeio", quietly = TRUE)) {
  library(ape)
  library(tidytree)
  library(treeio)

  #' ## Full transmission tree
  ttreedata <- getTransmissionTree(test.nosoi)
  plot(ttreedata@phylo)

  ## Sampling "non dead" individuals
  hID <- c("H-1", "H-7", "H-15", "H-100")
  samples <- data.table(hosts = hID,
                        times = c(5.2, 9.3, 10.2, 16),
                        labels = paste0(hID, "-s"))

  sampledTree <- sampleTransmissionTree(test.nosoi, ttreedata, samples)
  plot(sampledTree@phylo)

  ## Sampling "dead" individuals
  sampledDeadTree <- sampleTransmissionTreeFromExiting(ttreedata, hID)
  plot(sampledDeadTree@phylo)
  }

Get Node

Description

Find the node descending from the branch where the host lies at a given time on the transmission tree.

Usage

get_node(tdata, host, time)

Arguments

Value

number of the node in the transmission tree descending from the host at given time.

See Also

get_position

Get host count from table

Description

Returns the host count for a table and a state

Usage

get_other_count(table_hosts, name_cur, active_hosts, name_count, cur)

Arguments

Value

data.table containing state.ID (col1) and count (col2)

Get Position on branch

Description

Find the position on the branch above the node of the times sample. Warning: The node needs to be extracted with get_node. Result of this function is to be used in bind.tree

Usage

get_position(tdata, node, time)

Arguments

Value

time between the node and the sampling event.

See Also

get_node

Get State at sampling time

Description

Find the state of the host at the sampling time

Usage

get_state(table.state, host, time, total.time)

Arguments

Value

state of the host

Generates initial table to start the simulation (internal function)

Description

This function creates the initial table for the host.

Usage

iniTable(
  init.individuals,
  init.structure,
  prefix.host,
  ParamHost,
  current.environmental.value = NULL,
  current.cell.number.raster = NULL,
  current.count.A = integer(0),
  current.count.B = integer(0)
)

Arguments

Generates initial movement table to start the simulation (internal function)

Description

This function creates the initial table for the host, with 5+number of parameters of the transmission probability function parameters, and init.individuals row(s).

Usage

iniTableState(
  init.individuals,
  init.structure,
  prefix.host,
  current.environmental.value = NULL,
  current.cell.number.raster = NA
)

Arguments

Keep tips

Description

Keep the tips in the list. See keep.tip

Usage

keep.tip.treedata(tree, tip)

Should we build the table.host table

Description

Should we build the table.host table

Usage

keepState(nosoiSim)

Arguments

Value

boolean

Make Move function

Description

Makes the move.

Usage

makeMoves(
  res,
  pres.time,
  moving.full,
  structure.matrix = NA,
  sdMoveParsed = NA,
  structure.raster = NA,
  attracted.by.raster = NA,
  max.raster = NA
)

Arguments

Value

The modified object res

Meet Transmit function

Description

Perform the tasks related to meeting and transmission events.

Usage

meetTransmit(res, pres.time, positions, nContactParsed, pTransParsed)

Arguments

Value

A table with active transmission events

Merge Population Data

Description

Utility function to get merged hosts data if dual.

Usage

merge_host_tables(nosoiInf)

merge_state_tables(nosoiInf)

Arguments

Value

the merged 'table.hosts' or 'table.state' of the two populations.

Make Continuous Move function

Description

Makes the continuous move.

Usage

moveFunction.continuous(
  res,
  pres.time,
  moving.full,
  sdMoveParsed,
  structure.raster,
  attracted.by.raster,
  max.raster,
  ...
)

Arguments

Value

The modified object res

Make Discrete Move function

Description

Makes the discrete move.

Usage

moveFunction.discrete(res, pres.time, moving.full, structure.matrix, ...)

Arguments

Value

The modified object res

Rotates movement in 2D space

Description

Rotates movement in 2D space

Usage

moveRotateContinuous(pt1, dX, dY, angle)

Arguments

Creates a new line to be added to the table when new host is infected (internal function)

Description

This function creates a new line for the table. The lines are to be bounded with rbindlist.

Usage

newLine(
  hosts.ID,
  infected.by,
  infected.in,
  time.is,
  ParamHost,
  current.environmental.value = NULL,
  current.cell.number.raster = NULL,
  current.count.A = integer(0),
  current.count.B = integer(0)
)

Arguments

Value

a list with the new line to add.

Creates a new line to be added to the movement table when hosts moves (internal function)

Description

This function creates a new line for the table, The lines are to be bounded with rbindlist.

Usage

newLineState(
  hosts.ID,
  state.pres,
  time.is,
  current.environmental.value = NA,
  current.cell.number.raster = NA
)

Arguments

Value

a list with the new line to add.

Top-level function to use nosoi.

Description

This function determines which general settings the user wants to use for his simulation. All other arguments are passed down to the chosen simulator itself, such as singleNone, singleDiscrete, singleContinuous, dualNone, dualDiscrete or dualContinuous.

Usage

nosoiSim(type = "single", popStructure = "none", ...)

Arguments

Value

An object of class nosoiSim, containing all results of the simulation. Class nosoiSim object have the following slots:

total.time

Number of time steps the simulation ran (integer).

type

String giving the simulation type ("single" or "dual" host).

host.info.A: object of class nosoiSimOne

N.infected

Number of infected hosts (integer).

table.hosts

Table containing the results of the simulation (see getTableHosts for more details on the table).

table.state

Table containing the results of the simulation, focusing on the movement history of each host (see getTableState for more details on the table).

prefix.host

String containing the prefix used to name hosts (character string).

popStructure

String giving the population structure (one of "none", "discrete" or "continuous").

host.info.B: object of class nosoiSimOne

Same structure as host.info.A, but for host B (if it exists).

See Also

Individual simulation functions:

singleNone, singleDiscrete, singleContinuous, dualNone, dualDiscrete and dualContinuous.

Functions to extract the results:

getTableHosts, getTableState

Summary statistics functions:

nosoiSummary, getCumulative, getDynamic, getR0

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

test.nosoi <- nosoiSim(type="single", popStructure="none",
                      length=40,
                      max.infected=100,
                      init.individuals=1,
                      nContact=time_contact,
                      param.nContact=NA,
                      pTrans = proba,
                      param.pTrans = list(p_max=p_max_fct,
                                          t_incub=t_incub_fct),
                      pExit=p_Exit_fct,
                      param.pExit=NA)
test.nosoi

nosoiSim Constructor

Description

Creates a nosoiSim object.

Usage

nosoiSimConstructor(
  total.time,
  type = c("single", "dual"),
  pop.A,
  pop.B = NULL
)

Arguments

Value

An object of class nosoiSim

nosoiSimOne Constructor

Description

Creates a nosoiSimOne object.

Usage

nosoiSimOneConstructor(
  N.infected,
  table.hosts,
  table.state,
  prefix.host,
  popStructure = c("none", "discrete", "continuous")
)

Arguments

Value

An object of class nosoiSimOne

Summarizes the epidemiological features of a nosoi simulation

Description

This function provides summary information about the simulation (number of infected hosts, R0, etc.) as a list.

Usage

nosoiSummary(object)

## S3 method for class 'nosoiSim'
summary(object, ...)

Arguments

Value

All computed data is provided in a list:

R0

Provides a sublist with number of inactive hosts at the end of the simulation N.inactive, mean R0 R0.mean, and R0 distribution R0.dist. For more details, see getR0.

dynamics

data.table with the count of currently infected (i.e. active) hosts at each time step of the simulation (by state if the simulation was in a discrete structured host population). For more details, see getDynamic.

cumulative

data.table with the cumulative count of infected hosts at each time step of the simulation. For more details, see getCumulative.

See Also

You can directly compute each elements of the list without using the summarise function. See getR0, getDynamic and getCumulative.

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact <- function(t){round(rnorm(1, 3, 1), 0)}

test.nosoi <- nosoiSim(type="single", popStructure="none",
                      length=40,
                      max.infected=100,
                      init.individuals=1,
                      nContact=time_contact,
                      param.nContact=NA,
                      pTrans = proba,
                      param.pTrans = list(p_max=p_max_fct,
                                          t_incub=t_incub_fct),
                      pExit=p_Exit_fct,
                      param.pExit=NA)


nosoiSummary(test.nosoi)

Number of active infected hosts at time t

Description

For a given time t, this function returns the number of infected active units.

Usage

numberInfected(table.hosts, t)

Arguments

Value

Number of infected units at time t

See Also

nosoiSim

Number of infected hosts at time t (BGW)

Description

For a given time t, this function returns the number of infected active units. The difference with numberInfected is that it counts an "out" individual as still there, but with no children. This is for comparison with the BGW process, should be internal only.

Usage

numberInfectedBGW(table.nosoi, t)

Arguments

Value

Number of infected units at time t

See Also

nosoiSim

Number of active infected hosts at time t

Description

For a given time t, this function returns the number of infected active units.

Usage

numberInfectedStates(table.states, t)

Arguments

Value

Number of infected units at time t

See Also

nosoiSim

Param concatenator

Description

Creates a ParamHost object.

Usage

paramConstructor(
  param.pExit,
  param.pMove,
  param.nContact,
  param.pTrans,
  param.sdMove
)

Arguments

Value

An object of class ParamHost

Parse function for later use

Description

Parse a user-provided function to get the vectorized version.

Usage

parseFunction(
  pFunc,
  param.pFunc,
  name,
  diff = FALSE,
  timeDep = FALSE,
  hostCount = FALSE,
  continuous = FALSE,
  stateNames = NA
)

Arguments

Value

list of parsed quantities:

• "vect" Vectorized version of the function.

• "vectArgs" Vector of arguments for the vectorized function.

Progress bar

Description

Echos the state of the simulation at any given time step provided by the user.

Usage

progressMessage(
  Host.count.A,
  Host.count.B = NULL,
  pres.time,
  print.step,
  length.sim,
  max.infected.A,
  max.infected.B = NULL,
  type = "single"
)

Arguments

Sample the transmission tree (phylogenetic tree-like)

Description

Sample a full transmission tree. This function allows for sampling multiple times on the same lineage. When this happens, the sampled ancestor is a tip with length zero.

Usage

sampleTransmissionTree(nosoiInf, tree, samples)

Arguments

Details

The tree needs to be produced by function getTransmissionTree applied on the same nosoiSim object.

Value

A tree of class treedata, containing a phylogenetic tree based on the transmission chain and the mapped data at all the nodes.

See Also

For exporting the annotated tree to other software packages, see functions in treeio (e.g. write.beast).

To get the full transmission matrix, see getTransmissionTree.

For sampling only dead individuals, see sampleTransmissionTreeFromExiting.

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}
p_Move_fct  <- function(t){return(0.1)}

proba <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

transition.matrix = matrix(c(0, 0.2, 0.4, 0.5, 0, 0.6, 0.5, 0.8, 0),
                           nrow = 3, ncol = 3,
                           dimnames = list(c("A", "B", "C"), c("A", "B", "C")))

set.seed(805)
test.nosoi <- nosoiSim(type="single", popStructure="discrete",
                        length=20,
                        max.infected=100,
                        init.individuals=1,
                        init.structure="A",
                        structure.matrix=transition.matrix,
                        pMove=p_Move_fct,
                        param.pMove=NA,
                        nContact=time_contact,
                        param.nContact=NA,
                        pTrans = proba,
                        param.pTrans = list(p_max=p_max_fct,
                                            t_incub=t_incub_fct),
                        pExit=p_Exit_fct,
                        param.pExit=NA
)

## Make sure all needed packages are here
if (requireNamespace("ape", quietly = TRUE) &&
    requireNamespace("tidytree", quietly = TRUE) &&
    requireNamespace("treeio", quietly = TRUE)) {
  library(ape)
  library(tidytree)
  library(treeio)

  #' ## Full transmission tree
  ttreedata <- getTransmissionTree(test.nosoi)
  plot(ttreedata@phylo)

  ## Sampling "non dead" individuals
  hID <- c("H-1", "H-7", "H-15", "H-100")
  samples <- data.table(hosts = hID,
                        times = c(5.2, 9.3, 10.2, 16),
                        labels = paste0(hID, "-s"))

  sampledTree <- sampleTransmissionTree(test.nosoi, ttreedata, samples)
  plot(sampledTree@phylo)

  ## Sampling "dead" individuals
  sampledDeadTree <- sampleTransmissionTreeFromExiting(ttreedata, hID)
  plot(sampledDeadTree@phylo)
  }

Sample the transmission tree (phylogenetic tree-like) among the exited hosts

Description

Sample a full transmission tree. This function allows for sampling only exited (i.e. inactive) individuals (e.g. when the sampling procedure is destructive or cuts the hosts from the population). Beware because it does not influence the epidemiological process, it only means that the host has been sampled when exiting the simulation.

Usage

sampleTransmissionTreeFromExiting(tree, hosts)

Arguments

Details

The tree needs to be produced by function getTransmissionTree applied on the same nosoiSim object.

Value

A tree of class treedata, containing a phylogenetic tree based on the transmission chain and the mapped data at all the nodes.

See Also

For exporting the annotated tree to other software packages, see functions in treeio (e.g. write.beast).

To get the full transmission matrix, see getTransmissionTree.

For sampling among non-dead individuals, see sampleTransmissionTree.

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}
p_Move_fct  <- function(t){return(0.1)}

proba <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

transition.matrix = matrix(c(0, 0.2, 0.4, 0.5, 0, 0.6, 0.5, 0.8, 0),
                           nrow = 3, ncol = 3,
                           dimnames = list(c("A", "B", "C"), c("A", "B", "C")))

set.seed(805)
test.nosoi <- nosoiSim(type="single", popStructure="discrete",
                        length=20,
                        max.infected=100,
                        init.individuals=1,
                        init.structure="A",
                        structure.matrix=transition.matrix,
                        pMove=p_Move_fct,
                        param.pMove=NA,
                        nContact=time_contact,
                        param.nContact=NA,
                        pTrans = proba,
                        param.pTrans = list(p_max=p_max_fct,
                                            t_incub=t_incub_fct),
                        pExit=p_Exit_fct,
                        param.pExit=NA
)

## Make sure all needed packages are here
if (requireNamespace("ape", quietly = TRUE) &&
    requireNamespace("tidytree", quietly = TRUE) &&
    requireNamespace("treeio", quietly = TRUE)) {
  library(ape)
  library(tidytree)
  library(treeio)

  #' ## Full transmission tree
  ttreedata <- getTransmissionTree(test.nosoi)
  plot(ttreedata@phylo)

  ## Sampling "non dead" individuals
  hID <- c("H-1", "H-7", "H-15", "H-100")
  samples <- data.table(hosts = hID,
                        times = c(5.2, 9.3, 10.2, 16),
                        labels = paste0(hID, "-s"))

  sampledTree <- sampleTransmissionTree(test.nosoi, ttreedata, samples)
  plot(sampledTree@phylo)

  ## Sampling "dead" individuals
  sampledDeadTree <- sampleTransmissionTreeFromExiting(ttreedata, hID)
  plot(sampledDeadTree@phylo)
  }

Single-host pathogen in a structured (continuous) host population

Description

This function runs a single-host transmission chain simulation, with a structured host population (such as spatial features) in a continuous space. The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is passed. The movement of hosts on the continuous space map is a random walk (Brownian motion) that can be modified towards a biased random walk where hosts tend to be attracted to higher values of the environmental variable defined by the raster.

Usage

singleContinuous(
  length.sim,
  max.infected,
  init.individuals,
  init.structure,
  structure.raster,
  diff.pExit = FALSE,
  timeDep.pExit = FALSE,
  hostCount.pExit = FALSE,
  pExit,
  param.pExit,
  diff.pMove = FALSE,
  timeDep.pMove = FALSE,
  hostCount.pMove = FALSE,
  pMove,
  param.pMove,
  diff.sdMove = FALSE,
  timeDep.sdMove = FALSE,
  hostCount.sdMove = FALSE,
  sdMove,
  param.sdMove,
  attracted.by.raster = FALSE,
  diff.nContact = FALSE,
  timeDep.nContact = FALSE,
  hostCount.nContact = FALSE,
  nContact,
  param.nContact,
  diff.pTrans = FALSE,
  timeDep.pTrans = FALSE,
  hostCount.pTrans = FALSE,
  pTrans,
  param.pTrans,
  prefix.host = "H",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Raster

The structure raster provided provided should of class raster. High values of the environmental variable can attract hosts if attracted.by.raster is TRUE.

Structure Parameters

The pMove function should return a single probability (a number between 0 and 1), and sdMove a real number (keep in mind this number is related to your coordinate space).

The use of diff (switch to TRUE) makes the corresponding function use the argument current.env.value (for "current environmental value").

The use of hostCount (switch to TRUE) makes the corresponding function use the argument host.count.

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), current.env.value (optional, only if diff is TRUE), host.count (optional, only if hostCount is TRUE) and parameters specified in the list.

See Also

For simulations with a discrete structure, see singleDiscrete. For simulations without any structures, see singleNone.

Examples

library(raster)
#Generating a raster for the movement
set.seed(860)

test.raster <- raster(nrows=100, ncols=100, xmn=-50, xmx=50, ymn=-50,ymx=50)
test.raster[] <- runif(10000, -80, 180)
test.raster <- focal(focal(test.raster, w=matrix(1, 5, 5), mean), w=matrix(1, 5, 5), mean)
plot(test.raster)

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Move_fct  <- function(t){return(0.1)}

sdMove_fct = function(t,current.env.value){return(100/(current.env.value+1))}

p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
  if(t <= t_incub){p=0}
  if(t >= t_incub){p=p_max}
  return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

start.pos <- c(0,0)

test.nosoiA <- nosoiSim(type="single", popStructure="continuous",
                   length=200,
                   max.infected=500,
                   init.individuals=1,
                   init.structure=start.pos,
                   structure.raster=test.raster,
                   pMove=p_Move_fct,
                   param.pMove=NA,
                   diff.sdMove=TRUE,
                   sdMove=sdMove_fct,
                   param.sdMove=NA,
                   attracted.by.raster=TRUE,
                   nContact=time_contact,
                   param.nContact=NA,
                   pTrans = proba,
                   param.pTrans = list(p_max=p_max_fct,
                                       t_incub=t_incub_fct),
                   pExit=p_Exit_fct,
                   param.pExit=NA)

Single-host pathogen in a structured (discrete) host population

Description

This function, that can be wrapped within nosoiSim, runs a single-host transmission chain simulation, with a discrete host population structure (e.g. spatial, socio-economic, etc.). The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is crossed.

Usage

singleDiscrete(
  length.sim,
  max.infected,
  init.individuals,
  init.structure,
  structure.matrix,
  diff.pExit = FALSE,
  timeDep.pExit = FALSE,
  hostCount.pExit = FALSE,
  pExit,
  param.pExit,
  diff.pMove = FALSE,
  timeDep.pMove = FALSE,
  hostCount.pMove = FALSE,
  pMove,
  param.pMove,
  diff.nContact = FALSE,
  timeDep.nContact = FALSE,
  hostCount.nContact = FALSE,
  nContact,
  param.nContact,
  diff.pTrans = FALSE,
  timeDep.pTrans = FALSE,
  hostCount.pTrans = FALSE,
  pTrans,
  param.pTrans,
  prefix.host = "H",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Structure Matrix

The structure matrix provided provided should of class matrix, with the same number of rows and columns, rows representing departure state and column the arrival state. All rows should add to 1.

Structure Parameters

The pMove function should return a single probability (a number between 0 and 1).

The use of diff (switch to TRUE) makes the corresponding function use the argument current.in (for "currently in"). Your function should in that case give a result for every possible discrete state.

The use of hostCount (switch to TRUE) makes the corresponding function use the argument host.count.

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), current.in (optional, only if diff is TRUE), host.count (optional, only if hostCount is TRUE) and parameters specified in the list.

See Also

For simulations with a structure in continuous space, see singleContinuous. For simulations without any structures, see singleNone.

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}
p_Move_fct  <- function(t){return(0.1)}

proba <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact = function(t){round(rnorm(1, 3, 1), 0)}

transition.matrix = matrix(c(0,0.2,0.4,0.5,0,0.6,0.5,0.8,0),
                           nrow = 3, ncol = 3,
                           dimnames=list(c("A","B","C"),c("A","B","C")))

set.seed(805)
test.nosoiA <- nosoiSim(type="single", popStructure="discrete",
                       length=20,
                       max.infected=100,
                       init.individuals=1,
                       init.structure="A",
                       structure.matrix=transition.matrix,
                       pMove=p_Move_fct,
                       param.pMove=NA,
                       nContact=time_contact,
                       param.nContact=NA,
                       pTrans = proba,
                       param.pTrans = list(p_max=p_max_fct,
                                           t_incub=t_incub_fct),
                      pExit=p_Exit_fct,
                      param.pExit=NA)

Single-host pathogen in a homogeneous host population

Description

This function, that can be wrapped within nosoiSim, runs a single-host transmission chain simulation, without any structure features in the host population. The simulation stops either at the end of given time (specified by length.sim) or when the number of hosts infected threshold (max.infected) is crossed.

Usage

singleNone(
  length.sim,
  max.infected,
  init.individuals,
  pExit,
  param.pExit,
  timeDep.pExit = FALSE,
  nContact,
  param.nContact,
  timeDep.nContact = FALSE,
  pTrans,
  param.pTrans,
  timeDep.pTrans = FALSE,
  prefix.host = "H",
  print.progress = TRUE,
  print.step = 10
)

Arguments

Details

The pExit and pTrans functions should return a single probability (a number between 0 and 1), and nContact a positive natural number (positive integer) or 0.

The param arguments should be a list of functions or NA. Each item name in the parameter list should have the same name as the argument in the corresponding function.

The use of timeDep (switch to TRUE) makes the corresponding function use the argument prestime (for "present time").

Value

An object of class nosoiSim, containing all results of the simulation.

Order of Arguments

The user specified function's arguments should follow this order: t (mandatory), prestime (optional, only if timeDep is TRUE), parameters specified in the list.

See Also

For simulations with a discrete structured host population, see singleDiscrete. For simulations with a structured population in continuous space, singleContinuous

Examples

t_incub_fct <- function(x){rnorm(x,mean = 5,sd=1)}
p_max_fct <- function(x){rbeta(x,shape1 = 5,shape2=2)}
p_Exit_fct  <- function(t){return(0.08)}

proba <- function(t,p_max,t_incub){
 if(t <= t_incub){p=0}
 if(t >= t_incub){p=p_max}
 return(p)
}

time_contact <- function(t){round(rnorm(1, 3, 1), 0)}

test.nosoi <- nosoiSim(type="single", popStructure="none",
                      length=40,
                      max.infected=100,
                      init.individuals=1,
                      nContact=time_contact,
                      param.nContact=NA,
                      pTrans = proba,
                      param.pTrans = list(p_max=p_max_fct,
                                          t_incub=t_incub_fct),
                      pExit=p_Exit_fct,
                      param.pExit=NA)

test.nosoi

Summarise position of hosts in a discrete or discretized (raster) space

Description

Returns a table of the number of hosts per discrete or discretized (raster cells) states.

Usage

updateHostCount(res, res.B = NULL, type)

Arguments

Value

data.table containing state.ID (col1) and count (col2)

Update table state with exiting individuals

Description

Return a vector of exiting individuals.

Usage

updateTableState(res, exiting, pres.time)

Arguments

Value

nosoiSim object

Write newly infected function

Description

Perform the tasks related to creating new lines of hosts in relevant tables.

Usage

writeInfected(df.meetTransmit, res, pres.time, ParamHost)

Arguments

Value

The modified object res