The socialmixr package

socialmixr is an R package to derive social mixing matrices from survey data. These are particularly useful for age-structured infectious disease models. For background on age-specific mixing matrices and what data inform them, see, for example, the paper on POLYMOD by [@mossong_social_2008].

The pipeline workflow

Contact matrices are computed through a small pipeline of composable functions:

survey[expr] – filter participants or contacts.
assign_age_groups() – impute missing ages and assign age groups.
weigh() – optionally apply participant weights.
compute_matrix() – compute the mean contact matrix.
symmetrise(), split_matrix(), per_capita() – optional post-processing.

Each step is a separate function.

The POLYMOD data are included with the package and can be loaded using

data(polymod)

An example use would be

polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 1, 5, 15)) |>
  compute_matrix()
#> 
#> ── Contact matrix (4 age groups) ──
#> 
#> Ages: "[0,1)", "[1,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> 
#>           contact.age.group
#> age.group       [0,1)     [1,5)   [5,15) [15,Inf)
#>   [0,1)    0.40000000 0.8000000 1.266667 5.933333
#>   [1,5)    0.11250000 1.9375000 1.462500 5.450000
#>   [5,15)   0.02450980 0.5049020 7.946078 6.215686
#>   [15,Inf) 0.03230337 0.3581461 1.290730 9.594101

This generates a contact matrix from the UK part of the POLYMOD study, with age groups 0-1, 1-5, 5-15 and 15+ years. It contains the mean number of contacts that each member of an age group (row) has reported with members of the same or another age group (column).

Assigning age groups

assign_age_groups() prepares the survey for matrix computation. It imputes participant and contact ages from any available ranges, drops or keeps rows with missing ages (configurable via missing_participant_age and missing_contact_age), and adds age.group and contact.age.group columns using the age_limits you supply:

uk_grouped <- polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 1, 5, 15))

head(uk_grouped$participants[, c("part_id", "part_age", "age.group")])
#>    part_id part_age age.group
#>      <int>    <int>    <fctr>
#> 1:    4536        0     [0,1)
#> 2:    4538        0     [0,1)
#> 3:    4540        0     [0,1)
#> 4:    4541        0     [0,1)
#> 5:    4542        0     [0,1)
#> 6:    4546        0     [0,1)
head(uk_grouped$contacts[, c("part_id", "cnt_age", "contact.age.group")])
#>    part_id cnt_age contact.age.group
#>      <int>   <int>            <fctr>
#> 1:    4517       4             [1,5)
#> 2:    4517      40          [15,Inf)
#> 3:    4517      31          [15,Inf)
#> 4:    4517      52          [15,Inf)
#> 5:    4517      29          [15,Inf)
#> 6:    4517      59          [15,Inf)

The resulting survey object can be inspected, subset, or passed through any number of weigh() calls before compute_matrix().

Surveys

The key argument to the pipeline functions is the survey they operate on. The socialmixr package includes the POLYMOD survey. It also provides access to all surveys in the Social contact data community on Zenodo. The available surveys can be listed (if an internet connection is available) with

list_surveys()

A survey can be downloaded using the get_survey() command. This will get the relevant data of a survey given its Zenodo DOI (as returned by list_surveys()). If a survey is to be used repeatedly it is worth downloading it and storing it locally to avoid the need for a network connection and speed up processing.

peru_survey <- get_survey("https://doi.org/10.5281/zenodo.1095664")
saveRDS(peru_survey, "peru.rds")

This way, the peru data set can be loaded in the future without the need for an internet connection using

peru_survey <- readRDS("peru.rds")

Some surveys may contain data from multiple countries. To check this, use the survey_countries function

survey_countries(polymod)
#> Warning: `survey_countries()` was deprecated in socialmixr 0.5.0.
#> ℹ Please use `contactsurveys::download_survey()` instead.
#> ℹ We recommend using contactsurveys::download_survey() to download your
#>   surveys, and then you can load them with socialmixr::load_survey() and
#>   explore which countries are in the data.
#> [1] "Italy"          "Germany"        "Luxembourg"     "Netherlands"   
#> [5] "Poland"         "United Kingdom" "Finland"        "Belgium"

Use the subset method [ to restrict to one or more countries:

polymod[country %in% c("United Kingdom", "Germany")]

If this is not done, the different sub-surveys contained in a dataset are combined as a single sample (without country-specific weighting).

A reference for any given survey can be obtained using get_citation(), e.g.

get_citation(polymod)
#> Warning: `get_citation()` was deprecated in socialmixr 0.5.0.
#> ℹ Please use `contactsurveys::get_citation()` instead.
#> Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari
#> M, Salmaso S, Tomba GS, Wallinga J, Heijne J, Sadkowska-Todys M,
#> Rosinska M, Edmunds WJ (2017). "POLYMOD social contact data."
#> doi:10.5281/zenodo.1157934 <https://doi.org/10.5281/zenodo.1157934>.
#> Version 1.1.

Bootstrapping

To get an idea of the uncertainty in the contact matrices, participants can be resampled with replacement. A short helper replicates participant (and matching contact) rows for each occurrence of a resampled ID, so that duplicates are preserved:

bootstrap <- function(survey) {
  sampled_ids <- sample(
    unique(survey$participants$part_id),
    replace = TRUE
  )
  survey$participants <- survey$participants[
    list(sampled_ids), on = "part_id"
  ]
  survey$contacts <- survey$contacts[
    list(sampled_ids),
    on = "part_id",
    nomatch = NULL,
    allow.cartesian = TRUE
  ]
  survey
}

uk <- polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 1, 5, 15))

m <- suppressWarnings(
  replicate(n = 5, uk |> bootstrap() |> compute_matrix())
)
mr <- Reduce("+", lapply(m["matrix", ], function(x) x / ncol(m)))
mr
#>           contact.age.group
#> age.group       [0,1)     [1,5)    [5,15)  [15,Inf)
#>   [0,1)    0.51235178 1.2759881  3.186423  9.584447
#>   [1,5)    0.25827301 3.4266294  2.731001 10.673686
#>   [5,15)   0.03522628 0.9413949 15.206880 11.795335
#>   [15,Inf) 0.05385497 0.7839227  2.619152 19.693526

Demography

Obtaining symmetric contact matrices, splitting out their components (see below) and population-based participant weights require information about the underlying demographic composition of the survey population. This is represented as a data.frame with columns lower.age.limit (the lower end of each age group) and population (the number of people in that age group).

Using custom population data

You can construct any comparable data frame and pass it to the relevant post-processing step:

custom_pop <- data.frame(
  lower.age.limit = c(0, 18, 60),
  population = c(12000000, 35000000, 20000000)
)
polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 18, 60)) |>
  compute_matrix() |>
  symmetrise(survey_pop = custom_pop)
#>           contact.age.group
#> age.group    [0,18)  [18,60) [60,Inf)
#>   [0,18)   7.813187 5.899817 1.166637
#>   [18,60)  2.022794 7.931429 2.084481
#>   [60,Inf) 0.699982 3.647842 1.918033

For recent UN World Population Prospects data, the wpp2024 package is available from GitHub (remotes::install_github("PPgp/wpp2024")):

data("popAge1dt", package = "wpp2024")
uk_pop <- popAge1dt[name == "United Kingdom" & year == 2020,
  .(lower.age.limit = age, population = pop * 1000)
]
polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 18, 60)) |>
  compute_matrix() |>
  symmetrise(survey_pop = uk_pop)
#>           contact.age.group
#> age.group     [0,18)  [18,60)  [60,Inf)
#>   [0,18)   7.8131868 5.490727 0.8787841
#>   [18,60)  2.1528265 7.931429 1.7516352
#>   [60,Inf) 0.7665599 3.896982 1.9180328

survey_country_population() looks up country-specific population data from the wpp2017 package, which is an optional (Suggests) dependency of socialmixr; install it separately to use this function. Passing population data directly (as above) is preferred where possible:

uk_pop_default <- survey_country_population(
  polymod, countries = "United Kingdom"
)
head(uk_pop_default)
#>    lower.age.limit population
#>              <int>      <num>
#> 1:               0    3453670
#> 2:               5    3558887
#> 3:              10    3826567
#> 4:              15    3960166
#> 5:              20    3906577
#> 6:              25    3755132

Symmetric contact matrices

Conceivably, contact matrices should be symmetric: the total number of contacts made by members of one age group with those of another should be the same as vice versa. Mathematically, if $m_{ij}$ is the mean number of contacts made by members of age group $i$ with members of age group $j$ , and the total number of people in age group $i$ is $N_i$ , then

$m_{ij} N_i = m_{ji}N_j$

Because of variation in the sample from which the contact matrix is obtained, this relationship is usually not fulfilled exactly. In order to obtain a symmetric contact matrix that fulfills it, one can use

$m'_{ij} = \frac{1}{2N_i} (m_{ij} N_i + m_{ji} N_j)$

To get this version of the contact matrix, pipe the matrix through symmetrise(), passing the population data:

uk_pop <- survey_country_population(polymod, countries = "United Kingdom")

polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 1, 5, 15)) |>
  compute_matrix() |>
  symmetrise(survey_pop = uk_pop)
#> Warning: Not all age groups represented in population data (5-year age band).
#> ℹ Linearly estimating age group sizes from the 5-year bands.
#> Warning in normalise_weighted_matrix(survey_pop = resolved_pop, weighted_matrix = x$matrix, : Large differences in the size of the sub-populations with the current age
#> breaks are likely to result in artefacts after making the matrix symmetric.
#> ! Please reconsider the age breaks to obtain more equally sized
#>   sub-populations.
#> ℹ Normalization factors: [0.3 and 2.9]
#> 
#> ── Contact matrix (4 age groups) ──
#> 
#> Ages: "[0,1)", "[1,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> 
#>           contact.age.group
#> age.group       [0,1)     [1,5)   [5,15) [15,Inf)
#>   [0,1)    0.40000000 0.6250000 0.764365 4.122919
#>   [1,5)    0.15625000 1.9375000 1.406063 5.929829
#>   [5,15)   0.07148821 0.5260153 7.946078 7.428739
#>   [15,Inf) 0.05759306 0.3313352 1.109550 9.594101

Contact rates per capita

The contact matrix per capita $c_{ij}$ contains the social contact rates of one individual of age $i$ with one individual of age $j$ , given the population details. For example, $c_{ij}$ is used in infectious disease modelling to calculate the force of infection, which is based on the likelihood that one susceptible individual of age $i$ will be in contact with one infectious individual of age $j$ . The contact rates per capita are calculated as follows:

$c_{ij} = \tfrac{m_{ij}}{N_{j}}$

Pipe the matrix through per_capita(). If combined with symmetrise(), the contact matrix $m_{ij}$ can show asymmetry if the sub-population sizes are different, but the contact matrix per capita will be fully symmetric:

$c'_{ij} = \frac{m_{ij} N_i + m_{ji} N_j}{2N_iN_j} = c'_{ji}$

de_pop <- survey_country_population(polymod, countries = "Germany")

polymod[country == "Germany"] |>
  assign_age_groups(age_limits = c(0, 60)) |>
  compute_matrix() |>
  symmetrise(survey_pop = de_pop) |>
  per_capita(survey_pop = de_pop)
#>           contact.age.group
#> age.group        [0,60)     [60,Inf)
#>   [0,60)   1.261735e-07 4.418248e-08
#>   [60,Inf) 4.418248e-08 1.047852e-07

Splitting contact matrices

split_matrix() decomposes the contact matrix into a global component as well as three components representing contacts, assortativity and demography. In other words, the elements $m_{ij}$ of the contact matrix are modelled as

$m_{ij} = c q d_i a_{ij} n_j$

where $c$ is the mean number of contacts across the whole population, $c q d_i$ is the number of contacts that a member of group $i$ makes across age groups, $n_j$ is the proportion of the surveyed population in age group $j$ . The constant $q$ is set so that $c q$ is equal to the value of the largest eigenvalue of $m_{ij}$ ; if used in an infectious disease model and assumed that every contact leads to infection, $c q$ can be replaced by the basic reproduction number $R_0$ .

split_matrix() returns the assortativity matrix $a_{ij}$ in $matrix, with additional components $mean.contacts ( $c$ ), $normalisation ( $q$ ) and $contacts ( $d_i$ ).

polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 1, 5, 15)) |>
  compute_matrix() |>
  split_matrix(survey_pop = uk_pop)
#> Warning: Not all age groups represented in population data (5-year age band).
#> ℹ Linearly estimating age group sizes from the 5-year bands.
#> 
#> ── Contact matrix (4 age groups) ──
#> 
#> Ages: "[0,1)", "[1,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> Mean contacts: 11.55
#> 
#>           contact.age.group
#> age.group      [0,1)     [1,5)   [5,15)  [15,Inf)
#>   [0,1)    4.1561551 2.0780776 1.230914 0.8611839
#>   [1,5)    1.0955555 4.7169752 1.332022 0.7413849
#>   [5,15)   0.1456110 0.7498969 4.415104 0.5158328
#>   [15,Inf) 0.2500527 0.6930808 0.934443 1.0374170

Filtering

The [ method can be used to select particular participants or contacts. For example, in the polymod dataset, the indicators cnt_home, cnt_work, cnt_school, cnt_transport, cnt_leisure and cnt_otherplace take value 0 or 1 depending on where a contact occurred. The filter is evaluated against whichever table contains the referenced columns (participants, contacts, or both). Multiple filters can be chained:

# contact matrix for school-related contacts
polymod[cnt_school == 1] |>
  assign_age_groups(age_limits = c(0, 20, 60)) |>
  compute_matrix()
#>           contact.age.group
#> age.group      [0,20)    [20,60)   [60,Inf)
#>   [0,20)   5.15826279 1.09311741 0.03570114
#>   [20,60)  0.45610034 0.47434436 0.01453820
#>   [60,Inf) 0.08917836 0.07314629 0.03507014

# contact matrix for work-related contacts involving physical contact
polymod[cnt_work == 1][phys_contact == 1] |>
  assign_age_groups(age_limits = c(0, 20, 60)) |>
  compute_matrix()
#>           contact.age.group
#> age.group      [0,20)    [20,60)    [60,Inf)
#>   [0,20)   0.04266274 0.06325855 0.009194557
#>   [20,60)  0.16020525 1.26966933 0.145952109
#>   [60,Inf) 0.04212638 0.29287864 0.062186560

# contact matrix for daily contacts at home with males
polymod[cnt_home == 1][cnt_gender == "M"][duration_multi == 5] |>
  assign_age_groups(age_limits = c(0, 20, 60)) |>
  compute_matrix()
#>           contact.age.group
#> age.group      [0,20)   [20,60)   [60,Inf)
#>   [0,20)   0.39242369 0.5855094 0.03089371
#>   [20,60)  0.25919589 0.3940690 0.04875962
#>   [60,Inf) 0.05717151 0.1153460 0.23871615

Participant weights

Temporal aspects and demography

Participant weights are commonly used to align sample and population characteristics in terms of temporal aspects and the age distribution. For example, the day of the week has been reported as a driving factor for social contact behaviour, hence to obtain a weekly average, the survey data should represent the weekly 2/5 distribution of weekend/week days. To align the survey data to this distribution, one can obtain participant weights in the form of: $w_{\textrm{day.of.week}} = \tfrac{5/7}{N_{\textrm{weekday}}/N} \text{ OR } \tfrac{2/7}{N_{\textrm{weekend}}/N}$ with sample size $N$ , and $N_{weekday}$ and $N_{weekend}$ the number of participants that were surveyed during weekdays and weekend days, respectively.

Another driver of social contact patterns is age. To improve the representativeness of survey data, age-specific weights can be calculated as: $w_{age} = \tfrac{P_{a}\ /\ P}{N_{a}\ /\ N}$ with $P$ the population size, $P_a$ the population fraction of age $a$ , $N$ the survey sample size and $N_a$ the survey fraction of age $a$ . The combination of age-specific and temporal weights for participant $i$ of age $a$ can be constructed as: $w_{i} = w_{\textrm{age}} * w_{\textrm{day.of.week}}$

If the social contact analysis is based on stratification by splitting the population into non-overlapping groups, it requires the weights to be standardised so that the weighted totals within mutually exclusive cells equal the known population totals [@kolenikov_post-stratification_2016]. compute_matrix() applies this post-stratification normalisation within age groups.

weigh() is composable: each call multiplies new weights into the participants’ weight column. In POLYMOD, the dayofweek column uses 0 for Sunday and 6 for Saturday, so weekdays are 1-5 and weekend days are 0 and 6:

polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 18, 60)) |>
  weigh("dayofweek", target = c(5, 2), groups = list(1:5, c(0, 6))) |>
  weigh("part_age", target = uk_pop) |>
  compute_matrix()
#>           contact.age.group
#> age.group    [0,18)  [18,60)  [60,Inf)
#>   [0,18)   7.637824 5.372202 0.4878625
#>   [18,60)  2.279212 7.924999 1.0941688
#>   [60,Inf) 1.187088 5.303965 2.2302108

The first weigh() call assigns weekday participants a total weight of 5 and weekend participants a total weight of 2 (the weekly 5/2 split). The second call post-stratifies against the population structure in uk_pop.

User-defined participant weights

weigh() with no target multiplies an existing participant column directly into the weight. For instance, to give more importance to participants from large households:

polymod |>
  assign_age_groups(age_limits = c(0, 18, 60)) |>
  weigh("hh_size") |>
  compute_matrix()
#>           contact.age.group
#> age.group     [0,18)   [18,60)  [60,Inf)
#>   [0,18)   8.9599558  5.907367 0.7338418
#>   [18,60)  2.4650353 10.960550 1.2399199
#>   [60,Inf) 0.9909593  5.659468 2.7081868

Weight threshold

If the survey population differs extensively from the demography, some participants can end up with relatively high weights and as such, an excessive contribution to the population average. This warrants the limitation of single participant influences by a truncation of the weights. compute_matrix() accepts a numeric weight_threshold which caps the standardised weights and re-normalises so that the weight sum equals the group size. Weights close to the threshold may slightly exceed it after re-normalisation.

polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 18, 60)) |>
  weigh("dayofweek", target = c(5, 2), groups = list(1:5, c(0, 6))) |>
  weigh("part_age", target = uk_pop) |>
  compute_matrix(weight_threshold = 3)
#>           contact.age.group
#> age.group    [0,18)  [18,60)  [60,Inf)
#>   [0,18)   7.637824 5.372202 0.4878625
#>   [18,60)  2.282014 7.932570 1.0774717
#>   [60,Inf) 1.110740 5.275613 2.2700262

Numerical example

With these numeric examples, we show the importance of post-stratification weights in contrast to using the crude weights directly within age-groups. We will apply the weights by age and day of week separately in these examples, though the combination is straightforward via multiplication.

Get survey data

We start from a survey including 6 participants of 1, 2 and 3 years of age. The ages are not equally represented in the sample, though we assume they are equally present in the reference population. We will calculate the weighted average number of contacts by age and by age group, using {1,2} and {3} years of age. The following table shows the reported number of contacts per participant $i$ , represented by $m_i$ :

age	day.of.week	age.group	m_i
1	weekend	A	3
1	weekend	A	2
2	weekend	A	9
2	week	A	10
2	week	A	8
3	week	B	15

The summary statistics for the sample (N) and reference population (P) are as follows

N <- 6
N_age <- c(2, 3, 1)
N_age.group <- c(5, 1)
N_day.of.week <- c(3, 3)

P <- 3000
P_age <- c(1000, 1000, 1000)
P_age.group <- c(2000, 1000)

P_day.of.week <- c(5 / 7, 2 / 7) * 3000

This survey data results in an unweighted average number of contacts:

#> unweighted average number of contacts: 7.83

and age-specific unweighted averages on the number of contacts:

age	age.group	m_i
1	A	2.5
2	A	9.0
3	B	15.0

Weight by day of week

The following table contains the participants weights based on the survey day with and without the population and sample size constants ( $w$ and $w'$ , respectively). Note that the standardised weights $\tilde{w}$ and $\tilde{w'}$ are the same:

age	day.of.week	age.group	m_i	w	w_tilde	w_dot	w_dot_tilde
1	weekend	A	3	0.57	0.57	285.71	0.57
1	weekend	A	2	0.57	0.57	285.71	0.57
2	weekend	A	9	0.57	0.57	285.71	0.57
2	week	A	10	1.43	1.43	714.29	1.43
2	week	A	8	1.43	1.43	714.29	1.43
3	week	B	15	1.43	1.43	714.29	1.43

Note the different scale of $w$ and $w'$ , and the more straightforward interpretation of the numerical value of $w$ in terms of relative differences to apply truncation. Using the standardised weights, we are able to calculate the weighted number of contacts:

age	day.of.week	age.group	m_i	w	w_tilde	m_i * w_tilde
1	weekend	A	3	0.57	0.57	1.71
1	weekend	A	2	0.57	0.57	1.14
2	weekend	A	9	0.57	0.57	5.13
2	week	A	10	1.43	1.43	14.30
2	week	A	8	1.43	1.43	11.44
3	week	B	15	1.43	1.43	21.45

#> weighted average number of contacts: 9.2

If the population-based weights are directly used in age-specific groups, the contact behaviour of the 3 year-old participant, which participated during week day, is inflated due to the under-representation of week days in the survey sample. In addition, the number of contacts for 1 year-old participants is decreased because of the over-representation of weekend days in the survey. Using the population-weights within the two aggregated age groups, we obtain a more intuitive weighting for age group A, but it is still skewed for individuals in age group B. As such, this weighted average for age group B has no meaning in terms of social contact behaviour:

age	m_i * w_tilde
1	1.425
2	10.290
3	21.450

age.group	m_i * w_tilde
A	6.744
B	21.450

If we subdivide the population, we need to use post-stratification weights (“w_PS”) in which the weighted totals within mutually exclusive cells equal the sample size. For the age groups, this goes as follows:

age	day.of.week	age.group	m_i	w	w_tilde	w_PS
1	weekend	A	3	0.57	0.57	0.62
1	weekend	A	2	0.57	0.57	0.62
2	weekend	A	9	0.57	0.57	0.62
2	week	A	10	1.43	1.43	1.56
2	week	A	8	1.43	1.43	1.56
3	week	B	15	1.43	1.43	1.00

The weighted means equal:

age.group	m_i * w_PS
A	7.352
B	15.000

Weight by age

We repeated the example by calculating age-specific participant weights on the population and age-group level:

age	day.of.week	age.group	m_i	w	w_tilde	w_PS
1	weekend	A	3	1.00	1.00	1.25
1	weekend	A	2	1.00	1.00	1.25
2	weekend	A	9	0.67	0.67	0.83
2	week	A	10	0.67	0.67	0.83
2	week	A	8	0.67	0.67	0.83
3	week	B	15	2.00	2.00	1.00

#> weighted average number of contacts: 8.85

If the age-specific weights are directly used within the age groups, the contact behaviour for age group B is inflated to unrealistic levels and the number of contacts for age group A is artificially low:

age	m_i * w_tilde
1	2.50
2	6.03
3	30.00

age.group	m_i * w_tilde
A	4.618
B	30.000

Using the post-stratification weights, we end up with:

age.group	m_i * w_PS
A	5.732
B	15.000

Apply threshold

We start with survey data of 14 participants of 1, 2 and 3 years of age, sampled from a population in which all ages are equally present. Given the high representation of participants aged 1 year, the age-specific proportions are skewed in comparison with the reference population. If we calculate the age-specific weights and (un)weighted average number of contacts, we end up with:

age	day.of.week	age.group	m_i	w	w_tilde
1	weekend	A	3	0.47	0.47
1	weekend	A	2	0.47	0.47
1	weekend	A	3	0.47	0.47
1	weekend	A	2	0.47	0.47
1	weekend	A	3	0.47	0.47
1	weekend	A	2	0.47	0.47
1	weekend	A	3	0.47	0.47
1	weekend	A	2	0.47	0.47
1	weekend	A	3	0.47	0.47
1	weekend	A	2	0.47	0.47
2	weekend	A	9	1.56	1.56
2	week	A	10	1.56	1.56
2	week	A	8	1.56	1.56
3	week	B	30	4.67	4.67

#> unweighted average number of contacts: 5.86
#> weighted average number of contacts: 13.86

The single participant of 3 years of age has a very large influence on the weighted population average. As such, we propose to truncate the relative age-specific weights $w$ at 3. As such, the weighted population average equals:

#> weighted average number of contacts after truncation: 10.28

Plotting

Using ggplot2

The contact matrices can be plotted by using the geom_tile() function of the ggplot2 package.

df <- reshape2::melt(
  mr,
  varnames = c("age.group", "age.group.contact"),
  value.name = "contacts"
)
ggplot(df, aes(x = age.group, y = age.group.contact, fill = contacts)) +
  theme(legend.position = "bottom") +
  geom_tile()

Using R base

The contact matrices can also be plotted with the matrix_plot() function as a grid of coloured rectangles with the numeric values in the cells. Heat colours are used by default, though this can be changed.

matrix_plot(mr)

matrix_plot(mr, color.palette = gray.colors)