contactmatrix: POLYMOD Contact Matrices for Germany

Description

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 (5-year 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.

Usage

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

Value

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.

Format

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)).

Arguments

which: character string indicating which contact matrix to return. "mossong" uses the average numbers of reported contacts as published in Table S5 of Mossong et al. (2008), available as contactmatrix_mossong or contactmatrix_mossong_physical. "corrected" (from contactmatrix_POLYMOD or contactmatrix_POLYMOD_physical) fixes an error in these numbers related to the age group 70+ (see the Examples) and is the default. If which="reciprocal" (corresponding to contactmatrix_wallinga or contactmatrix_wallinga_physical as used by Meyer and Held, 2017), the returned social contact matrix fulfils reciprocity of contacts with respect to the age distribution of Berlin, pop2011, via the method of Wallinga et al. (2006).
type: a character string to select the type of contacts to use: either "all" contacts, i.e., count both physical and pure conversational contacts, or only "physical" contacts.
grouping: specification of how to aggregate groups (a named list or an integer vector of group sizes) using aggregateC with the "agedistri" attribute of the contact matrix as weights. If NULL, the original 5-year intervals are returned. The default setting produces the six age groups of Meyer and Held (2017).
normalize: a logical indicating whether to normalize the matrix such that each row sums to 1.

Author

Sebastian Meyer

References

Meyer S and Held L (2017): Incorporating social contact data in spatio-temporal models for infectious disease spread. Biostatistics, 18 (2), 338-351. tools:::Rd_expr_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. tools:::Rd_expr_doi("http://dx.doi.org/10.1371/journal.pmed.0050074")

Wallinga J, Teunis P and Kretzschmar M (2006): Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. American Journal of Epidemiology, 164 (10), 936-944. tools:::Rd_expr_doi("10.1093/aje/kwj317")

Examples

Run this code

## 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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