Simulated dataset of dengue transmission with basic reproductive number of 1

Description

Dataset simulated using an agent based model with a spatially heterogeneous population structure. Infectious agents were introduced resulting in agent to agent transmission. The distance between successive cases in a transmission chain were randomly drawn from a uniform distribution U(0,100). Each infectious agent resulted in a single transmission to another agent after a delay of 15 days, reflecting the generation time of dengue. There are 11 transmission chains, each with a different genotype. The genotypes are subdivided into four serotypes.

Usage

DengueSimR01

Format

Matrix with five columns representing the X and Y coordinates of infected individuals, the time of infection, the genotype of the infecting pathogen and the serotype of the infecting pathogen.

Author(s)

Justin Lessler and Henrik Salje

Simulated dataset of dengue cases with basic reproductive number of 2

Description

Dataset simulated using an agent based model with a spatially heterogeneous population structure. Infectious agents were introduced resulting in agent to agent transmission. The distance between successive cases in a transmission chain were randomly drawn from a uniform distribution U(0,100). Each infectious agent resulted in transmissions to two other agents after a delay of 15 days, reflecting the generation time of dengue. There are 11 transmission chains, each with a different genotype. The genotypes are subdivided into four serotypes.

Usage

DengueSimR02

Format

Matrix with five columns representing the X and Y coordinates of infected individuals, the time of infection, the genotype of the infecting pathogen and the serotype of the infecting pathogen.

Author(s)

Justin Lessler and Henrik Salje

Simulated dataset of dengue cases with representative underlying population

Description

Dataset simulated using an agent based model with a spatially heterogeneous population structure. Infectious agents were introduced resulting in agent to agent transmission. The distance between successive cases in a transmission chain were randomly drawn from a uniform distribution U(0,100). Each infectious agent resulted in transmissions to two other agents after a delay of 15 days, reflecting the generation time of dengue. There are 11 transmission chains, each with a different genotype. The genotypes are subdivided into four serotypes. 500 randomly selected individuals from the underlying population also included.

Usage

DengueSimRepresentative

Format

Matrix with five columns representing the X and Y coordinates of infected individuals, the time of infection, the genotype of the infecting pathogen and the serotype of the infecting pathogen. Individuals representative from the underlying population have '-999'for time, genotype and serotype.

Author(s)

Justin Lessler and Henrik Salje

Estimate transmission distance

Description

this function estimates the mean transmission distance of an epidemic when given the locations and times of symptomatic cases and the mean and standard deviation of the generation time of the infecting pathogen

Usage

est.transdist(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 100,
  theta.weights = NULL
)

Arguments

Value

a list containing the estimated mean distance of the transmission kernel (mu.est) and its standard deviation (sigma.est). Bounded estimates (bound.mu.est and bound.sigma.est) are also given for when the assumption of equal mean and standard deviation is violated.

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other est.wt: est.wt.matrix(), est.wt.matrix.weights()

Other transdist: est.transdist.bootstrap.ci(), est.transdist.temporal(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights(), get.transdist.theta()

Examples

set.seed(123)

# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=1.5,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  min.cases=50,
                  tot.generations=12,
                  trans.kern.func=dist.func)

# Estimate mean and standara deviation of transmission kernel
b <- est.transdist(epi.data=a,
                   gen.t.mean=7,
                   gen.t.sd=2,
                   t1=0,
                   max.sep=1e10,
                   max.dist=1e10,
                   n.transtree.reps=10)
b

Bootstrap mean transmission distance values

Description

Runs est.trandsdist on multiple bootstraps of the data and calculates confidence intervals for the mean transmission distance.

Usage

est.transdist.bootstrap.ci(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 100,
  mean.equals.sd = FALSE,
  theta.weights = NULL,
  boot.iter,
  ci.low = 0.025,
  ci.high = 0.975,
  parallel = FALSE,
  n.cores = NULL
)

Arguments

Value

a list object containing the point estimate for mean transmission distance and low and high bootstrapped confidence intervals

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist(), est.transdist.temporal(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights(), get.transdist.theta()

Examples

set.seed(1)

# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=2.5,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  min.cases=20,
                  tot.generations=5,
                  trans.kern.func=dist.func)

# Estimate mean transmission kernel and its bootstrapped confidence intervals
b <- est.transdist.bootstrap.ci(epi.data=a,
                                gen.t.mean=7,
                                gen.t.sd=2,
                                t1=0,
                                max.sep=1e10,
                                max.dist=1e10,
                                n.transtree.reps=10,
                                mean.equals.sd=TRUE,
                                boot.iter=10,
                                ci.low=0.025,
                                ci.high=0.975,
                                n.cores=2)
b

Change in mean transmission distance over time

Description

Estimates the change in mean transmission distance over the duration of the epidemic by running est.trandsdist on all cases occuring up to each time point.

Usage

est.transdist.temporal(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 10,
  mean.equals.sd = FALSE,
  theta.weights = NULL,
  parallel = FALSE,
  n.cores = NULL
)

Arguments

Value

a numeric matrix containing the point estimate for mean transmission distance for each unique time step of the epidemic and the sample size $n$ used to make the estimate NAs are returned for time steps which contain fewer than three cases

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist(), est.transdist.bootstrap.ci(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights(), get.transdist.theta()

Examples

     
set.seed(123)

# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=2,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  tot.generations=7,
                  min.cases=30,
                  trans.kern.func=dist.func)

a <- a[sample(1:nrow(a), 50),] # subsample a to 50 observations

# Estimate mean transmission kernel over time
b <- est.transdist.temporal(epi.data=a,
                            gen.t.mean=7,
                            gen.t.sd=2,
                            t1=0,
                            max.sep=1e10,
                            max.dist=1e10,
                            n.transtree.reps=5,
                            mean.equals.sd=TRUE,
                            n.cores=2)
b

plot(b[,2], pch=19, col='grey', ylim=c(min(b[,2], na.rm=TRUE), max(b[,2], na.rm=TRUE)), 
     xlab='Time step', ylab='Estimated mean of transmission kernel')
abline(h=100, col='red', lty=2)
axis(3, b[,2])

low <- loess(b[,2] ~ as.vector(1:length(b[,2])))
low <- predict(low, newdata=data.frame(as.vector(1:length(b[,2]))))
lines(low, lwd=3, col='blue')

Bootstrapped confidence intervals for the change in mean transmission distance over time

Description

Estimates bootstrapped confidence intervals for the mean transmission distance over the duration of the epidemic by running est.trandsdist on all cases occuring up to each time point.

Usage

est.transdist.temporal.bootstrap.ci(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 100,
  mean.equals.sd = FALSE,
  theta.weights = NULL,
  boot.iter,
  ci.low = 0.025,
  ci.high = 0.975,
  parallel = FALSE,
  n.cores = NULL
)

Arguments

Value

a four-column numeric matrix containing the point estimate for mean transmission distance, low and high bootstrapped confidence intervals, and the sample size up to each time step

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist(), est.transdist.bootstrap.ci(), est.transdist.temporal(), est.transdist.theta.weights(), get.transdist.theta()

Examples

set.seed(123)

# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=2,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  tot.generations=8,
                  min.cases=30,
                  trans.kern.func=dist.func)

a <- a[sample(1:nrow(a), 70),] # subsample a to 70 observations

# Estimate change in mean transmission kernel over time with confidence intervals
b <- est.transdist.temporal.bootstrap.ci(epi.data=a,
                                         gen.t.mean=7,
                                         gen.t.sd=2,
                                         t1=0,
                                         max.sep=1e10,
                                         max.dist=1e10,
                                         n.transtree.reps=10,
                                         mean.equals.sd=TRUE,
                                         boot.iter=10,
                                         ci.low=0.025,
                                         ci.high=0.975,
                                         n.cores=2)

plot(b[,2], pch=19, col='grey', ylim=c(min(b[,1:3], na.rm=TRUE), max(b[,2:4], na.rm=TRUE)), 
     xlab='Time step', ylab='Estimated mean of transmission kernel')
abline(h=100, col='red', lty=2)
axis(3, 1:nrow(b), b[,5])

low <- loess(b[,2] ~ as.vector(1:nrow(b)), span=1)
low <- predict(low, newdata=data.frame(as.vector(1:nrow(b))))
lines(low, lwd=3, col='blue')

for(i in 3:4) {
     low <- loess(b[,i] ~ as.vector(1:nrow(b)), span=1)
     low <- predict(low, newdata=data.frame(as.vector(1:nrow(b))))
     lines(low, lty=2, lwd=3, col='blue')
}

Estimate transmission distance theta values by replication

Description

This function estimates the weight of each theta value by performing a user defined number of replications with the get.transdist.theta function. The weights of each theta are calculated as the number of simulations in which a case at time t1 and t2 are separated by theta transmission events.

Usage

est.transdist.theta.weights(case.times, gen.t.mean, t.density, t1, n.rep = 100)

Arguments

Value

a three-dimensional array containing the mean normalized theta weights estimated across all replications

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist(), est.transdist.bootstrap.ci(), est.transdist.temporal(), est.transdist.temporal.bootstrap.ci(), get.transdist.theta()

Examples

set.seed(1)

gen.t.mean <- 7
gen.t.sd <- 2
t1 <- 0

# Normally distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=5,
                  gen.t.mean=gen.t.mean,
                  gen.t.sd=gen.t.sd,
                  min.cases=5,
                  tot.generations=3,
                  trans.kern.func=dist.func)

# Get case times
a <- a[order(a[,3]),]
case.times <- round(a[,3])
unique.times <- unique(case.times)
ntimes <- length(unique.times)


# Generation time distribution
max.t <- round(max(unique.times) - t1) - 1
n.step <- round(max.t/gen.t.mean)
gen <- rep(0, max.t*2)
for (i in 1:n.step){gen <- gen + dnorm(1:(max.t*2), gen.t.mean*i, gen.t.sd*i)}
gen[1] <- 0 # No instantaneous infections
t.density <- gen/sum(gen)

# Estimation of theta weights matrix
b <- est.transdist.theta.weights(case.times=case.times,
                                 n.rep=3, 
                                 gen.t.mean=gen.t.mean, 
                                 t1=t1, 
                                 t.density=t.density)

Calculate the Infector-Infectee Wallinga-Teunis matrix

Description

A function which takes the time of each case occurrence, the generation time distribution of the infecting pathogen, and the matrix of basic Wallinga-Teunis weights and estimates the probability that an infectee occurring time step j (columns) was infected by a case occurring at time i (rows).

Usage

est.wt.matrix(case.times, gen.t.dist, basic.wt.weights = NULL)

Arguments

Value

a numerical matrix with the number of columns and rows equal to the number of cases in the epidemic

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other est.wt: est.transdist(), est.wt.matrix.weights()

Examples

case.times <- c(1,2,2,3,3)
gen <- c(0, 2/3, 1/3, 0, 0)
t.density <- gen/sum(gen)

a <- est.wt.matrix(case.times=case.times, gen.t.dist=t.density)

Estimate matrix of basic Wallinga-Teunis weights

Description

A function called by est.wt.matrix, which calculates the basic weights in the Wallinga-Teunis matrix given the time of each case occurrence and the generation time distribution of the pathogen. Code adapted from the R0 package.

Usage

est.wt.matrix.weights(case.times, gen.t.dist)

Arguments

Value

a numerical matrix with the number of columns and rows equal to the number of time steps in the epidemic

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Boelle P and Obadia T (2015). R0: Estimation of R0 and Real-Time Reproduction Number from Epidemics. R package version 1.2-6, https://CRAN.R-project.org/package=R0.

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other est.wt: est.transdist(), est.wt.matrix()

Examples

case.times <- c(1,2,2,3,3)
gen <- c(0, 2/3, 1/3, 0, 0)
t.density <- gen/sum(gen)

a <- est.wt.matrix.weights(case.times=case.times, gen.t.dist=t.density)

Cross type K function using homotypic and heterotypic case types

Description

A wrapper function of the Kcross function from the spatstat.explore package (Baddeley et al. 2016) that takes epidemiological data used by IDSpatialStats functions and calculates the cross type K-function based on user defined case type homology

Usage

get.cross.K(epi.data, type, hom, het = NULL, r = NULL, correction = "none")

Arguments

Value

a data frame with a minimum of three columns giving the radius (r), the theoretical value of the K function for a Poisson process (theo), and value of the K function evaluated at radius r. The column name gives the type of edge correction used

Author(s)

John Giles

References

Baddeley A, Rubak E, and Turner R. (2016). "Spatial Point Patterns: Methodology and Applications with R". CRC Press.

Examples

data(DengueSimR01)

k <- get.cross.K(epi.data=DengueSimR01, type=5, hom=2, het=NULL, r=NULL, correction='border')

plot(k[,2], type='l', col='red', lty=2, xlab='r', ylab='cross K function')
lines(k$border)

Cross type Pair Correlation Function using homotypic and heterotypic case types

Description

A wrapper function of the pcf function from the spatstat.explore package (Baddeley et al. 2016) that takes epidemiological data used by IDSpatialStats functions and calculates the cross type Pair Correlation Function based on user defined case type homology

Usage

get.cross.PCF(epi.data, type, hom, het = NULL, r = NULL, correction = "none")

Arguments

Value

a data frame with two columns giving the radius r, the theoretical value of the Pair Correlation Function for a Poisson process (theo), and value of the Pair Correlation Function pcf

Author(s)

John Giles

References

Baddeley A, Rubak E, and Turner R. (2016). "Spatial Point Patterns: Methodology and Applications with R". CRC Press.

Examples

data(DengueSimR01)

g <- get.cross.PCF(epi.data=DengueSimR01, type=5, hom=2, het=NULL, r=NULL, correction='none')

plot(g$pcf, type='l', xlab='r', ylab='cross PCF')
abline(h=1, col='red', lty=2)

Generalized version of get.pi

Description

Generalized version of the get.pi function that takes in an arbitrary function and returns the probability that a point within a particular range of a point of interest shares the relationship specified by the passed in function with that point.

Usage

get.pi(posmat, fun, r = 1, r.low = rep(0, length(r)), data.frame = TRUE)

Arguments

Value

pi value for each distance range that we look at. Where:

\pi(d_1, d_2) = \frac{\sum \boldsymbol{1} (d_{ij} \in [d_1,d_2)) \boldsymbol{1} (f(i,j)=1) }{\sum \sum \boldsymbol{1} [d_{ij} \in (d_1,d_2)) \boldsymbol{1} (f(i,j) \in \{1,2\}) }

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.pi: get.pi.bootstrap(), get.pi.ci(), get.pi.permute(), get.pi.typed(), get.pi.typed.bootstrap(), get.pi.typed.permute()

Other spatialtau: get.tau(), get.theta()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

sero.type.func<-function(a,b,tlimit=20){
  if(a[5]==b[5]&(abs(a[3]-b[3])<=tlimit)){rc=1}
  else{rc=2}
  return(rc)
}

sero.pi<-get.pi(DengueSimR02,sero.type.func,r=r.max,r.low=r.min)

Bootstrap get.pi values.

Description

Runs get.pi on multiple bootstraps of the data. Is formulated such that the relationships between points and themselves will not be calculated.

Usage

get.pi.bootstrap(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 500,
  data.frame = TRUE
)

Arguments

Value

pi values for all the distances we looked at

Note

In each bootstrap iteration N observations are drawn from the existing data with replacement. To avoid errors in inference resulting from the same observatin being compared with itself in the bootstrapped data set, original indices are perserved, and pairs of points in the bootstrapped dataset with the same original index are ignored.

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.pi: get.pi(), get.pi.ci(), get.pi.permute(), get.pi.typed(), get.pi.typed.bootstrap(), get.pi.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2


pi<-get.pi(x,fun,r=r.max,r.low=r.min)
pi.boot<-get.pi.bootstrap(x,fun,r=r.max,r.low=r.min,boot.iter=100)

pi.ci<-apply(pi.boot[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, pi$pi , type="l")
lines(r.mid, pi.ci[1,] , lty=2)
lines(r.mid, pi.ci[2,] , lty=2)

Calculate bootstrapped confidence intervals for get.pi values.

Description

Wrapper to get.pi.bootstrap that takes care of calculating the confidence intervals based on the bootstrapped values..

Usage

get.pi.ci(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 1000,
  ci.low = 0.025,
  ci.high = 0.975,
  data.frame = TRUE
)

Arguments

Value

a matrix with a row for the high and low values and a column per distance

Author(s)

Justin Lessler

See Also

Other get.pi: get.pi(), get.pi.bootstrap(), get.pi.permute(), get.pi.typed(), get.pi.typed.bootstrap(), get.pi.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2


pi<-get.pi(x,fun,r=r.max,r.low=r.min)
pi.ci<-get.pi.ci(x,fun,r=r.max,r.low=r.min,boot.iter=100)

plot(r.mid, pi$pi, type="l")
lines(r.mid, pi.ci[,2] , lty=2)
lines(r.mid, pi.ci[,3] , lty=2)

get the null distribution of the get.pi function

Description

Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

Usage

get.pi.permute(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  data.frame = TRUE
)

Arguments

Value

pi values for all the distances we looked at

See Also

Other get.pi: get.pi(), get.pi.bootstrap(), get.pi.ci(), get.pi.typed(), get.pi.typed.bootstrap(), get.pi.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

pi<-get.pi(x,fun,r=r.max,r.low=r.min)
pi.null<-get.pi.permute(x,fun,r=r.max,r.low=r.min,permutations=100)

null.ci<-apply(pi.null[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, pi$pi, type="l")
lines(r.mid, null.ci[1,] , lty=2)
lines(r.mid, null.ci[2,] , lty=2)

Optimized version of get.pi for typed data.

Description

Version of the get.pi function that is optimized for statically typed data. That is data where we are interested in the probability of points within some distance of points of typeA are of typeB.

Usage

get.pi.typed(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  data.frame = TRUE
)

Arguments

Value

pi values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.pi: get.pi(), get.pi.bootstrap(), get.pi.ci(), get.pi.permute(), get.pi.typed.bootstrap(), get.pi.typed.permute()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<120)
tmp<-cbind(DengueSimR02,type=type)

typed.pi<-get.pi.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min)

runs bootstrapping on get.pi.typed

Description

Bootstraps typed pi values. Makes sure distances between a sample and another draw of itself are left out

Usage

get.pi.typed.bootstrap(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter,
  data.frame = TRUE
)

Arguments

Value

pi values for all the distances we looked at

See Also

Other get.pi: get.pi(), get.pi.bootstrap(), get.pi.ci(), get.pi.permute(), get.pi.typed(), get.pi.typed.permute()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<120)
tmp<-cbind(DengueSimR02,type=type)

typed.pi.bs<-get.pi.typed.bootstrap(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min,boot.iter=100)

get the null distribution of the get.pi.typed function

Description

Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

Usage

get.pi.typed.permute(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  data.frame = TRUE
)

Arguments

Value

pi values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.pi: get.pi(), get.pi.bootstrap(), get.pi.ci(), get.pi.permute(), get.pi.typed(), get.pi.typed.bootstrap()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<75)
tmp<-cbind(DengueSimR02,type=type)

typed.pi<-get.pi.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min)
typed.pi.type.null<-get.pi.typed.permute(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min,permutations=100)

generalized version of get.tau

Description

returns the relative probability (or odds) that points at some distance from an index point share some relationship with that point versus the probability (or odds) any point shares that relationship with that point.

Usage

get.tau(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

The tau value for each distance we look at. If comparison.type is "representative", this is:

tau = get.pi(posmat, fun, r, r.low)/get.pi(posmat,fun,infinity,0)

If comparison.type is "independent", this is:

tau = get.theta(posmat, fun, r, r.low)/get.theta(posmat,fun,infinity,0)

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau.bootstrap(), get.tau.ci(), get.tau.permute(), get.tau.typed(), get.tau.typed.bootstrap(), get.tau.typed.permute()

Other spatialtau: get.pi(), get.theta()

Examples

data(DengueSimR01)
data(DengueSimR02)
data(DengueSimRepresentative)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
r.mid<-(r.max+r.min)/2

sero.type.func<-function(a,b,tlimit=20){
     if(a[5]==b[5]&(abs(a[3]-b[3])<=tlimit)){rc=1}
     else{rc=2}
     return(rc)
}

geno.type.func<-function(a,b,tlimit=20){
     if(a[4]==b[4]&(abs(a[3]-b[3])<=tlimit)){rc=1}
     else{rc=2}
     return(rc)
}

sero.type.rep.func<-function(a,b,tlimit=20){
     if(a[5]==1&b[5]==1&(abs(a[3]-b[3])<=tlimit)){rc=1}
     else{if(a[5]==1&b[5]==-999){rc=2}else{rc=3}}
     return(rc)
}

sero.tau.R01 <- get.tau(DengueSimR01, sero.type.func, r=r.max, r.low=r.min, 
                      comparison.type="independent")
geno.tau.R01 <- get.tau(DengueSimR01, geno.type.func, r=r.max, r.low=r.min, 
                      comparison.type="independent")

sero.tau.R02 <- get.tau(DengueSimR02, sero.type.func, r=r.max, r.low=r.min, 
                      comparison.type="independent")
geno.tau.R02 <- get.tau(DengueSimR02, geno.type.func, r=r.max, r.low=r.min, 
                      comparison.type="independent")

sero.tau.representative <- get.tau(DengueSimRepresentative, sero.type.rep.func, 
                                   r=r.max, r.low=r.min, comparison.type="representative")

## R0 of 1
plot(r.mid,sero.tau.R01$tau,ylim=c(0.3,max(geno.tau.R01$tau)),log="y",
     cex.axis=1.25,col=rgb(t(col2rgb("blue")/255),alpha=0.6),
     xlab="Distance (m)",ylab="Tau",cex.main=0.9,lwd=2,type="l",las=1,cex.axis=0.75)
abline(h=1,lty=2)

abline(v=100,lty=1,lwd=2)
lines(r.mid,geno.tau.R01$tau,pch=20,col=rgb(t(col2rgb("dark green")/255),alpha=0.6),lwd=1)
lines(r.mid,sero.tau.representative$tau,pch=20,col=rgb(t(col2rgb("dark blue")/255),alpha=0.6),lty=2)
legend("topright",
       legend=c("Genotype",
                "Serotype",
                "Serotype (representative population)",
                "Maximum transmission distance"),
       lwd=1,col=c("dark green","blue","blue","black"),
       lty=c(1,1,2,1),bty="n")

## R0 of 2
plot(r.mid,sero.tau.R02$tau,ylim=c(0.3,max(geno.tau.R02)),log="y",
     cex.axis=1.25,col=rgb(t(col2rgb("blue")/255),alpha=0.6),
     xlab="Distance (m)",ylab="Tau",cex.main=0.9,lwd=2,type="l",las=1,cex.axis=0.75)
abline(h=1,lty=2)
abline(v=100,lty=1,lwd=2)
lines(r.mid,geno.tau.R02$tau,pch=20,col=rgb(t(col2rgb("dark green")/255),alpha=0.6),lwd=1)
legend("topright",
       legend=c("Genotype",
                "Serotype",
                "Maximum transmission distance"),
       lwd=1,col=c("dark green","blue","black"),lty=1,bty="n")

Bootstrap get.tau values.

Description

Runs get.tau on multiple bootstraps of the data. Is formulated such that the relationship between points and themselves will not be calculated

Usage

get.tau.bootstrap(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter,
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

a matrix containing all bootstrapped values of tau for each distance interval

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.ci(), get.tau.permute(), get.tau.typed(), get.tau.typed.bootstrap(), get.tau.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

tau<-get.tau(x,fun,r=r.max,r.low=r.min)
tau.boot<-get.tau.bootstrap(x,fun,r=r.max,r.low=r.min,boot.iter=50)

tau.ci<-apply(tau.boot[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, tau$tau ,ylim=c(min(tau.ci),max(tau.ci)), type="l", log="y")
lines(c(0,100),c(1,1), lty=3, col="grey")
lines(r.mid, tau.ci[1,] , lty=2)
lines(r.mid, tau.ci[2,] , lty=2)

Bootstrap confidence interval for the get.tau values

Description

Wrapper to get.tau.bootstrap that takes care of calulating the confidence intervals based on the bootstrapped values

Usage

get.tau.ci(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 1000,
  comparison.type = "representative",
  ci.low = 0.025,
  ci.high = 0.975,
  data.frame = TRUE
)

Arguments

Value

a data frame with the point estimate of tau and its low and high confidence interval at each distance

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.bootstrap(), get.tau.permute(), get.tau.typed(), get.tau.typed.bootstrap(), get.tau.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
     if(a[1]!=2) return(3)
     if (b[1]==2) return(1)
     return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

tau <- get.tau.ci(x,fun,r=r.max,r.low=r.min,boot.iter=50)

plot(r.mid, tau$pt.est, ylim=c(1/max(tau[,3:5]), max(tau[,3:5])), type="l", log="y",
     xlab="Distance", ylab="Tau")
lines(r.mid, tau$ci.low , lty=2)
lines(r.mid, tau$ci.high, lty=2)
lines(c(0,100),c(1,1), lty=3, col="grey")

get the null distribution of the get.tau function

Description

Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

Usage

get.tau.permute(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

tau values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.bootstrap(), get.tau.ci(), get.tau.typed(), get.tau.typed.bootstrap(), get.tau.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

tau<-get.tau(x,fun,r=r.max,r.low=r.min,comparison.type = "independent")
tau.null<-get.tau.permute(x,fun,r=r.max,r.low=r.min,permutations=50,comparison.type = "independent")

null.ci<-apply(tau.null[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, tau$tau, ylim=c(1/max(tau$tau),max(tau$tau)), type="l", log="y")
lines(c(0,100),c(1,1), lty=3, col="grey")
lines(r.mid, null.ci[1,] , lty=2)
lines(r.mid, null.ci[2,] , lty=2)

Optimized version of get.tau for typed data

Description

Version of th e get.tau function that is optimized for statically typed data. That is data where we want the relationship between points of type A and points of type B

Usage

get.tau.typed(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

data frame of tau values for all the distances

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.bootstrap(), get.tau.ci(), get.tau.permute(), get.tau.typed.bootstrap(), get.tau.typed.permute()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
r.mid<-(r.max+r.min)/2

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<120)
tmp<-cbind(DengueSimR02,type=type)

typed.tau<-get.tau.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min,comparison.type = "independent")

plot(r.mid,typed.tau$tau,log="y",cex.axis=1.25,
     xlab="Distance (m)",ylab="Tau",cex.main=0.9,lwd=2,type="l")
abline(h=1,lty=2)

runs bootstrapping for get.tau.typed

Description

runs bootstrapping for get.tau.typed

Usage

get.tau.typed.bootstrap(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter,
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

tau values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.bootstrap(), get.tau.ci(), get.tau.permute(), get.tau.typed(), get.tau.typed.permute()

Examples

data(DengueSimulationR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
r.mid<-(r.max+r.min)/2

# Lets see if there's a difference in spatial dependence between those that occurred 
# late versus early in the outbreak

type <- 2 - (DengueSimR02[,"time"] < 120)
tmp <- cbind(DengueSimR02, type=type)

typed.tau <- get.tau.typed(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                           comparison.type = "independent")

typed.tau.type.bs <- get.tau.typed.bootstrap(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                                             boot.iter=100, comparison.type = "independent")

ci <- apply(typed.tau.type.bs[,-(1:2)], 1, quantile, probs=c(0.025,0.975))

plot(r.mid, typed.tau$tau, log="y",
     ylim=c(0.1,4), cex.axis=1.25,
     xlab="Distance (m)", ylab="Tau",
     cex.main=0.9, lwd=2, type="n")
abline(h=1,lty=1)
lines(r.mid,typed.tau$tau,pch=20,col=1,lwd=3)
lines(r.mid, ci[1,] , lty=2)
lines(r.mid, ci[2,] , lty=2)

get the null distribution for the get.tau.typed function

Description

get the null distribution for the get.tau.typed function

Usage

get.tau.typed.permute(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  comparison.type = "representative",
  data.frame = TRUE
)

Arguments

Value

a matrix with permutation tau values for each distance specified

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.tau: get.tau(), get.tau.bootstrap(), get.tau.ci(), get.tau.permute(), get.tau.typed(), get.tau.typed.bootstrap()

Examples

data(DengueSimulationR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)
r.mid<-(r.max+r.min)/2

#Lets see if there's a difference in spatial dependence by time case occurs
type <- 2 - (DengueSimR02[,"time"] < 120)
tmp <- cbind(DengueSimR02, type=type)

typed.tau <- get.tau.typed(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                           comparison.type = "independent")

typed.tau.type.null<-get.tau.typed.permute(tmp, typeA=1, typeB=2, r=r.max, r.low=r.min, 
                                           permutations=100, comparison.type = "independent")

null.ci <- apply(typed.tau.type.null[,-(1:2)], 1, quantile, probs=c(0.025,0.975))

plot(r.mid, typed.tau$tau, ylim=c(0.3,4), log="y", cex.axis=1.25, 
     xlab="Distance (m)", ylab="Tau", cex.main=0.9, lwd=2, type="n")
abline(h=1,lty=1)
lines(r.mid,typed.tau$tau,pch=20,col=1,lwd=3)
lines(r.mid, null.ci[1,] , lty=2)
lines(r.mid, null.ci[2,] , lty=2)

Generalized version of get.theta

Description

Generalized version of the get.theta function that takes in an arbitrary function and returns the odds that a point within a particular range of a point of interest shares the relationship specified by the passed in function with that point.

Usage

get.theta(posmat, fun, r = 1, r.low = rep(0, length(r)), data.frame = TRUE)

Arguments

Value

theta value for each distance range that we look at. Where:

\theta(d_1,d_2) = \frac{\sum \boldsymbol{1} d_{ij} \in [d_1,d_2)) \boldsymbol{1} (f(i,j)=1) }{\sum \sum \boldsymbol{1} d_{ij} \in [d_1,d_2)) \boldsymbol{1} (f(i,j)=2) }

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.theta: get.theta.bootstrap(), get.theta.ci(), get.theta.permute(), get.theta.typed(), get.theta.typed.bootstrap(), get.theta.typed.permute()

Other spatialtau: get.pi(), get.tau()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

sero.type.func<-function(a,b,tlimit=20){
  if(a[5]==b[5]&(abs(a[3]-b[3])<=tlimit)){rc=1}
  else{rc=2}
  return(rc)
}

sero.theta<-get.theta(DengueSimR02,sero.type.func,r=r.max,r.low=r.min)

Bootstrap get.theta values.

Description

Runs get.theta on multiple bootstraps of the data. Is formulated such that the relationships between points and themselves will not be calculated.

Usage

get.theta.bootstrap(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 500,
  data.frame = TRUE
)

Arguments

Value

theta values for all the distances we looked at

Note

In each bootstrap iteration N observations are drawn from the existing data with replacement. To avoid errors in inference resulting from the same observatin being compared with itself in the bootstrapped data set, original indices are perserved, and pairs of points in the bootstrapped dataset with the same original index are ignored.

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.theta: get.theta(), get.theta.ci(), get.theta.permute(), get.theta.typed(), get.theta.typed.bootstrap(), get.theta.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2


theta<-get.theta(x,fun,r=r.max,r.low=r.min)
theta.boot<-get.theta.bootstrap(x,fun,r=r.max,r.low=r.min,boot.iter=100)

theta.ci<-apply(theta.boot[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, theta$theta , type="l")
lines(r.mid, theta.ci[1,] , lty=2)
lines(r.mid, theta.ci[2,] , lty=2)

Calculate bootstrapped confidence intervals for get.theta values.

Description

Wrapper to get.theta.bootstrap that takes care of calculating the confience intervals based on the bootstrapped values.

Usage

get.theta.ci(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter = 1000,
  ci.low = 0.025,
  ci.high = 0.975,
  data.frame = TRUE
)

Arguments

Value

a matrix with a row for the high and low values and a column per distance

Author(s)

Justin Lessler

See Also

Other get.theta: get.theta(), get.theta.bootstrap(), get.theta.permute(), get.theta.typed(), get.theta.typed.bootstrap(), get.theta.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

theta<-get.theta(x,fun,r=r.max,r.low=r.min)
theta.ci<-get.theta.ci(x,fun,r=r.max,r.low=r.min,boot.iter=100)

plot(r.mid, theta$theta, type="l")
lines(r.mid, theta.ci[,2] , lty=2)
lines(r.mid, theta.ci[,3] , lty=2)

get the null distribution of the get.theta function

Description

Does permutations to calculate the null distribution of get theta if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

Usage

get.theta.permute(
  posmat,
  fun,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  data.frame = TRUE
)

Arguments

Value

theta values for all the distances we looked at

See Also

Other get.theta: get.theta(), get.theta.bootstrap(), get.theta.ci(), get.theta.typed(), get.theta.typed.bootstrap(), get.theta.typed.permute()

Examples

#compare normally distributed with uniform points
x<-cbind(1,runif(100,-100,100), runif(100,-100,100))
x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20)))
colnames(x) <- c("type","x","y")

fun<-function(a,b) {
    if(a[1]!=2) return(3)
    if (b[1]==2) return(1)
    return(2)
}

r.max<-seq(10,100,10)
r.min<-seq(0,90,10)
r.mid <- (r.max+r.min)/2

theta<-get.theta(x,fun,r=r.max,r.low=r.min)
theta.null<-get.theta.permute(x,fun,r=r.max,r.low=r.min,permutations=100)

null.ci<-apply(theta.null[,-(1:2)],1,quantile,probs=c(0.25,0.75))

plot(r.mid, theta$theta , type="l")
lines(r.mid, null.ci[1,] , lty=2)
lines(r.mid, null.ci[2,] , lty=2)

Optimized version of get.theta for typed data.

Description

Version of the get.theta function that is optimized for statically typed data. That is data where we are interested in the odds that points within some distance of points of typeA are of typeB.

Usage

get.theta.typed(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  data.frame = TRUE
)

Arguments

Value

theta values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.theta: get.theta(), get.theta.bootstrap(), get.theta.ci(), get.theta.permute(), get.theta.typed.bootstrap(), get.theta.typed.permute()

Examples

data(DengueSimR02)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR02[,"time"]<120)
tmp<-cbind(DengueSimR02,type=type)

typed.theta.R01<-get.theta.typed(tmp,typeA=2,typeB=2,r=r.max,r.low=r.min)

runs bootstrapping on get.theta.typed

Description

Bootstraps typed pi values. Makes sure distances between a sample and another draw of itself are left out

Usage

get.theta.typed.bootstrap(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  boot.iter,
  data.frame = TRUE
)

Arguments

Value

theta values for all the distances we looked at

See Also

Other get.theta: get.theta(), get.theta.bootstrap(), get.theta.ci(), get.theta.permute(), get.theta.typed(), get.theta.typed.permute()

Examples

data(DengueSimR01)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR01[,"time"]<75)
tmp<-cbind(DengueSimR01,type=type)

typed.theta.bs<-get.theta.typed.bootstrap(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min,boot.iter=100)

get the null distribution of the get.theta.typed function

Description

Does permutations to calculate the null distribution of get theta if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

Usage

get.theta.typed.permute(
  posmat,
  typeA = -1,
  typeB = -1,
  r = 1,
  r.low = rep(0, length(r)),
  permutations,
  data.frame = TRUE
)

Arguments

Value

theta values for all the distances we looked at

Author(s)

Justin Lessler and Henrik Salje

See Also

Other get.theta: get.theta(), get.theta.bootstrap(), get.theta.ci(), get.theta.permute(), get.theta.typed(), get.theta.typed.bootstrap()

Examples

data(DengueSimR01)

r.max<-seq(20,1000,20)
r.min<-seq(0,980,20)

#Lets see if there's a difference in spatial dependence by time case occurs
type<-2-(DengueSimR01[,"time"]<75)
tmp<-cbind(DengueSimR01,type=type)

typed.theta.R01<-get.theta.typed(tmp,typeA=1,typeB=2,r=r.max,r.low=r.min)
typed.theta.type.null<-get.theta.typed.permute(tmp, typeA=1, typeB=2, 
                                               r=r.max, r.low=r.min, permutations=100)

Get weights of transmission distance theta

Description

This function estimates the weights of each theta (number of transmission events separating cases at two time points). A randomized transmission tree is drawn and the number of transmission events separating cases at two time points is calculated based on probabilies found in the Wallinga-Teunis matrix.

Usage

get.transdist.theta(
  wal.teun.mat,
  cases,
  gen.t.mean,
  max.sep,
  ret.theta.mat = FALSE
)

Arguments

Value

a three-dimensional array containing normalized theta weights. Columns and rows represent unique case times. The third dimension is the number of transmission events between two cases.

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist(), est.transdist.bootstrap.ci(), est.transdist.temporal(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights()

Examples

case.times <- c(1,2,2,3,3)
gen <- c(0, 2/3, 1/3, 0, 0)
t.density <- gen/sum(gen)

gen.time <- 2 # mean generation time

wt <- est.wt.matrix(case.times=case.times, gen.t.dist=t.density)

ngen <- round((max(case.times) - min(case.times)) / gen.time) + 1 # Number of generations

a <- get.transdist.theta(wal.teun.mat=wt,
                         cases=case.times,
                         gen.t.mean=gen.time,
                         max.sep=ngen*2)

Simulation of an epidemic in space and time

Description

A function which simulates the spatial spread of infections through time given the reproductive number (R), a function describing the spatial transmission kernel (trans.kern.func), and the mean and standard deviation of the generation time distribution (gen.t.mean and gen.t.sd) for the infecting pathogen. The function returns the location (x, y) and time (t) for each case of infection in the simulation.

Usage

sim.epidemic(
  R,
  gen.t.mean,
  gen.t.sd,
  trans.kern.func,
  tot.generations = 10,
  min.cases = 0,
  max.try = 1000
)

Arguments

Value

a numerical matrix with three columns giving the coordinates x and y, and time t of simulated cases

Author(s)

John Giles, Justin Lessler, and Henrik Salje

Examples

     
set.seed(1)

dist.func <- alist(n=1, a=1/100, rexp(n, a)) # Exponential transmission kernel with mean = sd = 100

# Simulate epidemic with constant R value
a <- sim.epidemic(R=1.5,
             gen.t.mean=7,
             gen.t.sd=2,
             tot.generations=15,
             min.cases=100,
             trans.kern.func=dist.func)

sim.plot(a)

# Simulate an epidemic with variable R value
r1 <- 2
r2 <- 0.25
tg <- 25
R <- seq(r1, r2, (r2 -r1)/(tg - 1))

b <- sim.epidemic(R=R,
             gen.t.mean=7,
             gen.t.sd=2,
             tot.generations=tg,
             min.cases=100,
             trans.kern.func=dist.func)

sim.plot(b)

Plot output of simulated epidemic

Description

A simple visualization function which plots the location of the index case and the spatial distribution of subsequent cases, and the epidemic curve showing the case count over time.

Usage

sim.plot(sim)

Arguments

Value

A two-panel plotted object

Author(s)

John Giles, Justin Lessler, and Henrik Salje