Description Usage Arguments Format Value Author(s) Source References Examples
The function contactmatrix
retrieves various social contact matrices
for Germany from the POLYMOD survey (Mossong et al., 2008). Such a matrix
contains the average numbers of reported contacts by participant age group.
The original age groups (5year intervals) can be joined together by the
grouping
argument, which first sums over contact groups (columns) and
then averages over the corresponding participant groups (rows) using the
corresponding age distribution as weights.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  contactmatrix(
which = c("corrected", "mossong", "reciprocal"),
type = c("all", "physical"),
grouping = c(1, 2, 2, 4, 4, 2),
normalize = FALSE
)
contactmatrix_mossong
contactmatrix_mossong_physical
contactmatrix_POLYMOD
contactmatrix_POLYMOD_physical
contactmatrix_wallinga
contactmatrix_wallinga_physical

which 
character string indicating which contact matrix to return.

type 
a character string to select the type of contacts to use:
either 
grouping 
specification of how to aggregate groups (a named list or
an integer vector of group sizes) using 
normalize 
a logical indicating whether to normalize the matrix such that each row sums to 1. 
The dataset contactmatrix_POLYMOD
and its variants are all
square numeric matrices with 15 rows (participants) and 15 columns
(contacts), labelled with the corresponding age groups. There is an attribute
"agedistri"
, a named numeric vector of length 15, which for the
“_mossong_” and “_POLYMOD_” variants gives the
age distribution of the German POLYMOD sample, and for the _wallinga_
variants gives the age distribution of Berlin, i.e.,
prop.table(colSums(pop2011))
.
a square numeric matrix containing the average numbers of contact persons recorded per day per survey participant in Germany, potentially averaged over multiple row (participant) age groups and aggregated over the corresponding column (contact) age groups.
Sebastian Meyer
contactmatrix_mossong
and contactmatrix_mossong_physical
are taken from the Supporting Information in Mossong et al. (2008):
the matrices from Table S5 (8.2), and the attached age distribution
from Table S2 (3.2).
The corrected versions contactmatrix_POLYMOD
and
contactmatrix_POLYMOD_physical
were constructed from the raw POLYMOD
data available at
https://www.researchgate.net/publication/232701632_POLYMOD_contact_survey_for_researchers.
The reciprocal contact matrices contactmatrix_wallinga
and
contactmatrix_wallinga_physical
were estimated from these raw data
via the method of Wallinga et al. (2006).
Meyer S and Held L (2017): Incorporating social contact data in spatiotemporal models for infectious disease spread. Biostatistics, 18 (2), 338351. doi: 10.1093/biostatistics/kxw051
Mossong et al. (2008): Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine, 5 (3), e74. doi: 10.1371/journal.pmed.0050074
Wallinga J, Teunis P and Kretzschmar M (2006): Using data on social contacts to estimate agespecific transmission parameters for respiratoryspread infectious agents. American Journal of Epidemiology, 164 (10), 936944. doi: 10.1093/aje/kwj317
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42  ## contact matrix reported in Mossong et al (2008, Table S5)
(C_original < contactmatrix(which = "mossong", grouping = NULL))
## this simply returns the dataset 'contactmatrix_mossong'
stopifnot(identical(C_original, contactmatrix_mossong))
## with corrected numbers for the 70+ age group (the default)
C_corrected < contactmatrix(which = "corrected", grouping = NULL)
## this simply returns the dataset 'contactmatrix_POLYMOD'
stopifnot(identical(C_corrected, contactmatrix_POLYMOD))
## check for differences
C_original == round(C_corrected, 2)
## compare entries of last row and last column
round(rbind(original = C_original[15,], corrected = C_corrected[15,]), 2)
round(cbind(original = C_original[,15], corrected = C_corrected[,15]), 2)
## contact matrix estimated to be reciprocal on the population level
C_reciprocal < contactmatrix(which = "reciprocal", grouping = NULL)
## this simply returns the dataset 'contactmatrix_wallinga'
## (without its "overdisp" attribute)
stopifnot(all.equal(C_reciprocal, contactmatrix_wallinga, check.attributes=FALSE))
## check reciprocity
agedistriBE < attr(C_reciprocal, "agedistri")
stopifnot(identical(agedistriBE, prop.table(colSums(pop2011))))
stopifnot(isSymmetric(C_reciprocal * agedistriBE, check.attributes=FALSE))
## visually compare raw to reciprocal contact matrix
if (require("gridExtra"))
grid.arrange(plotC(C_corrected, main = "raw"),
plotC(C_reciprocal, main = "reciprocal"),
nrow = 1)
## select physical contacts and aggregate into 5 age groups
contactmatrix(type = "physical", grouping = c(1, 2, 7, 3, 2))
## the default 6 age groups, normalized to a transition matrix
contactmatrix(normalize = TRUE)
## reciprocity also holds for this grouping
(C6 < contactmatrix(which = "reciprocal"))
stopifnot(isSymmetric(C6 * attr(C6, "agedistri"), check.attributes=FALSE))

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