contact-fitter-location: Fitting Contact Data onto Serological Data With Different...

In TeaKov/serostat: Modeling Infectious Disease Parameters Based on Serological and Social Contact Data R Package

Description

As a first refinement to the social contact hypothesis addresses the potential of different types of social contacts to contribute to transmission, possibly at their own level. Consider location as the stratification variable with levels “home”, “school”, “work”, and “other”.

Usage

contact.fitter.location(a, y, rij, rij1, rij2, rij3, rij4, rij5, rij6, muy,
  N, D, Lmax, A, plots, startpar)

Arguments

a	Numeric vector of age categories.
y	The response variable (binary, 1=past infection, 0=otherwise).
rij	The smoothed contact matrix.
rij1	Different stratification levels of social contact matrices.
rij2	Different stratification levels of social contact matrices.
rij3	Different stratification levels of social contact matrices.
rij4	Different stratification levels of social contact matrices.
rij5	Different stratification levels of social contact matrices.
rij6	Different stratification levels of social contact matrices.
muy	Mortality function.
N	Population size.
D	Mean infectious period.
Lmax	Maximum age.
A	Age of loss of maternal immunity (0<A<1).
plots	Generate plots during fitting process.
startpar	Starting values for the “nlm” method used in this function.

See Also

pwcrate, waifw.6parms

Examples

ND <- c(489,47,29,21,12,12,16,15,15,6,6,14,17,19,17,23,34,33,62,71,68,68,78,71,71,96,86,83,79,
  80,83,93,126,120,121,132,135,176,161,193,196,218,257,277,331,376,356,435,460,453,535,545,
  576,668,692,759,722,819,939,1015,1051,973,1113,996,940,1074,1252,1367,1468,1541,1661,1838,
  2012,2236,2517,2793,2938,2994,3311,3516,3727,3857,4088,4161,4261,4274,4061,2509,2049,2159,
  2205,2550,2330,1992,1569,1242,1000,726,533,996)
PS <- c(118366,117271,114562,113894,116275,118030,116761,117742,119583,119887,118963,119958,
  124637,129143,131030,129724,127187,126433,124377,124883,122201,124482,126459,130129,133897,
  135009,134516,133495,132705,132040,130602,135638,140537,146151,150467,152113,151656,151412,
  153371,158268,162456,167652,164871,161671,162060,159735,160672,157030,153820,151114,148978,
  145929,142374,141215,135525,135968,134692,135991,134291,134131,113024,112198,105880,92772,
  84462,93787,100820,101866,97208,94145,92451,93027,91640,93593,91933,89900,81718,77891,73104,
  70082,67057,62178,57642,51786,47466,42065,28004,17186,14492,13838,13957,13358,10442,8063,5604,
  4289,2843,2068,1368,2146)
AGE<-c(0:(length(ND)-1))

estimL <- estimateLifeExpectancy(ND, PS, AGE)

# Using Belgian B19 data
data("VZV_B19_BE_0103")
VZV_B19_BE_0103 <- VZV_B19_BE_0103[!is.na(VZV_B19_BE_0103$parvores)&!is.na(VZV_B19_BE_0103$age)&
  VZV_B19_BE_0103$age<70&VZV_B19_BE_0103$age>=1,]
VZV_B19_BE_0103 <- VZV_B19_BE_0103[order(VZV_B19_BE_0103$age),]

y <- VZV_B19_BE_0103$parvores
a <- VZV_B19_BE_0103$age

# Mean duration of infectiousness
D <- 6/365

# Maximum life (if type mortality this is the life expectancy)
Lmax<-100

# Age of loss of maternal immunity (0<A<1)
A <- 0.5

# Mortality function
My <- estimL$My[1:Lmax]
muy <- estimL$muy[1:Lmax]

# Population size
N <- sum(PS)

data("loc_home")
data("loc_school")
data("loc_work")
data("loc_leisure")
data("loc_transport")
data("loc_otherplace")

data("scd_close_p4h")

contact.result<-contact.fitter.location(a=a,y=y,rij=scd_close_p4h,loc_home,
  loc_school,loc_work,0*loc_leisure,0*loc_transport,loc_otherplace,muy=muy,
  N=N,D=D,Lmax=85,A=A,plots="TRUE",
  startpar=c(0.4,0.001,0.001,0.001,0.001,0.001))
c(contact.result$q,contact.result$R0,contact.result$aic)

contact.result<-contact.fitter.location(a=a,y=y,rij=scd_close_p4h, loc_home,
  loc_school,loc_work,0*loc_leisure,0*loc_transport,loc_otherplace,muy=muy,
  N=N,D=D,Lmax=85,A=A,plots="TRUE",
  startpar=c(0.01,0.41,0.001,0.001,0.001,0.001))
c(contact.result$q,contact.result$R0,contact.result$aic)

TeaKov/serostat documentation built on May 21, 2019, 1:21 p.m.