Modelling heterogeneous social contacts

Defining a value for \(R_0\)

A number of statistical methods can be used to estimate the \(R_0\) of an epidemic in its early stages from available data. These are not discussed here, but some examples are given in the episoap package.

Instead, this example considers a infection with an \(R_0\) of 1.5, similar to that which could potentially be observed for pandemic influenza.

# define r0 as 1.5
r0 <- 1.5

Getting and preparing contact and demography data

This example uses social contact data from the POLYMOD project (Mossong et al. 2008) to estimate the final size of an epidemic in the U.K. These data are provided with the socialmixr package.

The contact data are divided into five age groups: 0 – 4, 5 – 17, 18 – 39, 40 – 64, and 65 and over, specified using the age.limits argument in socialmixr::contact_matrix(). The symmetric = TRUE argument to socialmixr::contact_matrix() returns a symmetric contact matrix, so that the contacts reported by group \(\{i\}\) of individuals from group \(\{j\}\) are the same as those reported by group \(\{j\}\) of group \(\{i\}\).

The demographic data — the number of individuals in each age group — is also available through socialmixr::contact_matrix().

# get UK polymod data
polymod <- socialmixr::polymod
contact_data <- socialmixr::contact_matrix(
  polymod,
  countries = "United Kingdom",
  age.limits = c(0, 5, 18, 40, 65),
  symmetric = TRUE
)

# view the elements of the contact data list
# the contact matrix
contact_data$matrix
#>       contact.age.group
#>            [0,5)   [5,18)  [18,40)  [40,65)       65+
#>   [1,] 1.9157895 1.467205 3.278803 1.559611 0.3658614
#>   [2,] 0.5191021 8.858736 3.421566 2.616146 0.6260247
#>   [3,] 0.6252718 1.844236 5.440972 3.492124 0.8092367
#>   [4,] 0.2792593 1.324010 3.278895 4.178218 1.2380907
#>   [5,] 0.1306272 0.631752 1.515092 2.468756 1.7142857

# the demography data
contact_data$demography
#>    age.group population proportion  year
#>       <char>      <num>      <num> <int>
#> 1:     [0,5)    3453670 0.05728738  2005
#> 2:    [5,18)    9761554 0.16191873  2005
#> 3:   [18,40)   18110368 0.30040378  2005
#> 4:   [40,65)   19288101 0.31993930  2005
#> 5:       65+    9673058 0.16045081  2005

# get the contact matrix and demography data
contact_matrix <- t(contact_data$matrix)
demography_vector <- contact_data$demography$population
demography_data <- contact_data$demography

# scale the contact matrix so the largest eigenvalue is 1.0
# this is to ensure that the overall epidemic dynamics correctly reflect
# the assumed value of R0
contact_matrix <- contact_matrix / max(Re(eigen(contact_matrix)$values))

# divide each row of the contact matrix by the corresponding demography
# this reflects the assumption that each individual in group {j} make contacts
# at random with individuals in group {i}
contact_matrix <- contact_matrix / demography_vector

n_demo_grps <- length(demography_vector)

Population susceptibility

As a starting scenario, consider a novel pathogen where all age groups have a similar, high susceptibility to infection. This means it is assumed that all individuals fall into a single category: fully susceptible.

Full uniform susceptibility can be modelled as a matrix with values of 1.0, with as many rows as there are demographic groups. The matrix has a single column, representing the single susceptibility group to which all individuals belong.

# all individuals are equally and highly susceptible
n_susc_groups <- 1L
susc_guess <- 1.0

susc_uniform <- matrix(
  data = susc_guess,
  nrow = n_demo_grps,
  ncol = n_susc_groups
)

Final size calculations also need to know the proportion of each demographic group \(\{i\}\) that falls into the susceptibility group \(\{j\}\). This distribution of age groups into susceptibility groups can be represented by the demography-susceptibility distribution matrix.

Since all individuals in each age group have the same susceptibility, there is no variation within age groups. Consequently, all individuals in each age group are assumed to be fully susceptible. This can be represented as a single-column matrix, with as many rows as age groups, and as many columns as susceptibility groups.

In this example, the matrix p_susc_uniform has 5 rows, one for each age group, and only one column, for the single high susceptibility group that holds all individuals.

p_susc_uniform <- matrix(
  data = 1.0,
  nrow = n_demo_grps,
  ncol = n_susc_groups
)

Running final_size

The final size of the epidemic in the population can then be calculated using the only function in the package, final_size(). This example allows the function to fall back on the default options for the arguments solver ("iterative") and control (an empty list).

# calculate final size
final_size_data <- final_size(
  r0 = r0,
  contact_matrix = contact_matrix,
  demography_vector = demography_vector,
  susceptibility = susc_uniform,
  p_susceptibility = p_susc_uniform
)

# view the output data frame
final_size_data
#>   demo_grp   susc_grp susceptibility p_infected
#> 1    [0,5) susc_grp_1              1  0.4160682
#> 2   [5,18) susc_grp_1              1  0.6888703
#> 3  [18,40) susc_grp_1              1  0.5379381
#> 4  [40,65) susc_grp_1              1  0.4639496
#> 5      65+ susc_grp_1              1  0.3042623

Visualise final sizes

Code

# order demographic groups as factors
final_size_data$demo_grp <- factor(
  final_size_data$demo_grp,
  levels = demography_data$age.group
)

Code

# plot data
ggplot(final_size_data) +
  geom_col(
    aes(
      demo_grp, p_infected
    ),
    colour = "black", fill = "grey"
  ) +
  scale_y_continuous(
    labels = scales::percent,
    limits = c(0, 1)
  ) +
  expand_limits(
    x = c(0.5, nrow(final_size_data) + 0.5)
  ) +
  theme_classic() +
  coord_cartesian(
    expand = FALSE
  ) +
  labs(
    x = "Age group",
    y = "% Infected"
  )

Figure 1: Final size of an SIR epidemic in each age group. The final size is the cumulative number of infections in each age group over the course of the epidemic, expressed as a proportion of the respective age group.

Final size proportions to counts

finalsize returns the proportion of each age (and susceptibility) group infected in an epidemic outbreak. The final counts of individuals infected can be visualised as well, by multiplying the final proportion of each age group infected with the total number of individuals in that group.

The example below show how this can be done.

Code

# prepare demography data
demography_data <- contact_data$demography

# merge final size counts with demography vector
final_size_data <- merge(
  final_size_data,
  demography_data,
  by.x = "demo_grp",
  by.y = "age.group"
)

# reset age group order
final_size_data$demo_grp <- factor(
  final_size_data$demo_grp,
  levels = contact_data$demography$age.group
)

# multiply counts with proportion infected
final_size_data$n_infected <- final_size_data$p_infected *
  final_size_data$population

Code

ggplot(final_size_data) +
  geom_col(
    aes(
      x = demo_grp, y = n_infected
    ),
    fill = "grey", col = "black"
  ) +
  expand_limits(
    x = c(0.5, nrow(final_size_data) + 0.5)
  ) +
  scale_y_continuous(
    labels = scales::comma_format(
      scale = 1e-6, suffix = "M"
    ),
    limits = c(0, 15e6)
  ) +
  theme_classic() +
  coord_cartesian(
    expand = FALSE
  ) +
  labs(
    x = "Age group",
    y = "Number infected (millions)"
  )

Figure 2: Final size of an epidemic outbreak in a population, for different values of infection \(R_0\). Converting the final size proportions in each age group to counts shows that individuals aged 18 – 64 make up the bulk of cases in this scenario. This may be attributed to this being both the largest age range in the analysis (more years in this range than any other), and because more people fall into this wide range than others. Contrast this figure with the one above, in which similar proportions of each age group are infected.