Introduction

epicontacts aims to facilitate manipulation, visualisation and analysis of epidemiological contact data. Such datasets inherently have network components, in which nodes are typically cases and reported contacts or exposures are (directed or undirected) edges. This package provides a convenient data structure as well as functionality specific to handle these data.

library(outbreaks)
library(epicontacts)

Loading Data

epicontacts provides a convenient structure to store heterogeneous epidemiological contact network data (i.e. nodes and edges) in a single object. The epicontacts class must contain two components: a line list and a contact dataset.

Each row of the line list should represent unique observations of cases, and each row of the contact list should represent unique pairs of contacts. Each can include arbitrary features, but both datasets should share an identification scheme.

Example Dataset

The example that follows will use the mers_korea_2015, which is a dataset (in list format) distributed in the outbreaks package.

str(mers_korea_2015)
## List of 2
##  $ linelist:'data.frame':    162 obs. of  15 variables:
##   ..$ id            : chr [1:162] "SK_1" "SK_2" "SK_3" "SK_4" ...
##   ..$ age           : int [1:162] 68 63 76 46 50 71 28 46 56 44 ...
##   ..$ age_class     : chr [1:162] "60-69" "60-69" "70-79" "40-49" ...
##   ..$ sex           : Factor w/ 2 levels "F","M": 2 1 2 1 2 2 1 1 2 2 ...
##   ..$ place_infect  : Factor w/ 2 levels "Middle East",..: 1 2 2 2 2 2 2 2 2 2 ...
##   ..$ reporting_ctry: Factor w/ 2 levels "China","South Korea": 2 2 2 2 2 2 2 2 2 1 ...
##   ..$ loc_hosp      : Factor w/ 13 levels "365 Yeollin Clinic, Seoul",..: 10 10 10 10 1 10 10 13 10 10 ...
##   ..$ dt_onset      : Date[1:162], format: "2015-05-11" ...
##   ..$ dt_report     : Date[1:162], format: "2015-05-19" ...
##   ..$ week_report   : Factor w/ 5 levels "2015_21","2015_22",..: 1 1 1 2 2 2 2 2 2 2 ...
##   ..$ dt_start_exp  : Date[1:162], format: "2015-04-18" ...
##   ..$ dt_end_exp    : Date[1:162], format: "2015-05-04" ...
##   ..$ dt_diag       : Date[1:162], format: "2015-05-20" ...
##   ..$ outcome       : Factor w/ 2 levels "Alive","Dead": 1 1 2 1 1 2 1 1 1 1 ...
##   ..$ dt_death      : Date[1:162], format: NA ...
##  $ contacts:'data.frame':    98 obs. of  4 variables:
##   ..$ from         : chr [1:98] "SK_14" "SK_14" "SK_14" "SK_14" ...
##   ..$ to           : chr [1:98] "SK_113" "SK_116" "SK_41" "SK_112" ...
##   ..$ exposure     : Factor w/ 5 levels "Contact with HCW",..: 2 2 2 2 2 2 2 2 2 2 ...
##   ..$ diff_dt_onset: int [1:98] 10 13 14 14 15 15 15 16 16 16 ...

What features are in the line list?

colnames((mers_korea_2015$linelist))
##  [1] "id"             "age"            "age_class"      "sex"           
##  [5] "place_infect"   "reporting_ctry" "loc_hosp"       "dt_onset"      
##  [9] "dt_report"      "week_report"    "dt_start_exp"   "dt_end_exp"    
## [13] "dt_diag"        "outcome"        "dt_death"

What about the contact dataset?

colnames((mers_korea_2015$contacts))
## [1] "from"          "to"            "exposure"      "diff_dt_onset"

Creating epicontacts Object

In order to create the epicontacts object, both the line list and contact data frames must be passed to make_epicontacts(). This function accommodates instances when the respective identifiers are not the first columns of these data frames (see the “id”, “from” and “to” arguments). make_epicontacts() can also account for contact networks that have a direction (see “directed” argument).

merskor15 <- make_epicontacts(linelist = mers_korea_2015$linelist,
                              contacts = mers_korea_2015$contacts, 
                              directed = FALSE)
class(merskor15)
## [1] "epicontacts"
summary(merskor15)
## 
## /// Overview //
##   // number of unique IDs in linelist: 162
##   // number of unique IDs in contacts: 97
##   // number of unique IDs in both: 97
##   // number of contacts: 98
##   // contacts with both cases in linelist: 100 %
## 
## /// Degrees of the network //
##   // in-degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   1.000   1.000   2.021   1.000  39.000 
## 
##   // out-degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   1.000   1.000   2.021   1.000  39.000 
## 
##   // in and out degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   1.000   1.000   2.021   1.000  39.000 
## 
## /// Attributes //
##   // attributes in linelist:
##  age age_class sex place_infect reporting_ctry loc_hosp dt_onset dt_report week_report dt_start_exp dt_end_exp dt_diag outcome dt_death
## 
##   // attributes in contacts:
##  exposure diff_dt_onset

Data Manipulation

Access Unique Identifiers

The summary() method above provided counts for the number unique cases in both the contact and line list. The get_id() function retrieves similar information but as vectors of identifiers. This can be parameterized as follows:

  • linelist: IDs in the line list data
  • contacts: IDs in the contact dataset (“from” and “to” combined)
  • from: IDs in the “from” column of contact datset
  • to IDs in the “to” column of contact dataset
  • all: IDs that appear anywhere in either dataset
  • common: IDs that appear in both contacts dataset and line list

What are the first ten IDs in the contacts dataset?

contacts_ids <- get_id(merskor15, "contacts")
head(contacts_ids, n = 10)
##  [1] "SK_14"  "SK_16"  "SK_6"   "SK_87"  "SK_1"   "SK_15"  "SK_76" 
##  [8] "SK_11"  "SK_12"  "SK_118"

How many IDs are common to both?

length(get_id(merskor15, "common"))
## [1] 97

Subsetting

The subset() method for epicontacts objects allows for, among other things, pruning of networks based on values of node and edge attributes. These values must be passed as named lists to the respective argument.

subset(merskor15, node_attribute = list("outcome" = "Dead", "sex" = "M"), 
       edge_attribute = list("exposure" = "Emergency room"))
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 14 cases in linelist; 33 contacts;  non directed 
## 
##   // linelist
## 
## # A tibble: 14 × 15
##        id   age age_class    sex        place_infect reporting_ctry
##     <chr> <int>     <chr> <fctr>              <fctr>         <fctr>
## 1    SK_3    76     70-79      M Outside Middle East    South Korea
## 2    SK_6    71     70-79      M Outside Middle East    South Korea
## 3   SK_23    73     70-79      M Outside Middle East    South Korea
## 4   SK_24    78     70-79      M Outside Middle East    South Korea
## 5   SK_28    58     50-59      M Outside Middle East    South Korea
## 6   SK_36    82     80-89      M Outside Middle East    South Korea
## 7   SK_38    49     40-49      M Outside Middle East    South Korea
## 8   SK_64    75     70-79      M Outside Middle East    South Korea
## 9   SK_81    62     60-69      M Outside Middle East    South Korea
## 10  SK_83    65     60-69      M Outside Middle East    South Korea
## 11  SK_84    80     80-89      M Outside Middle East    South Korea
## 12  SK_90    62     60-69      M Outside Middle East    South Korea
## 13  SK_98    58     50-59      M Outside Middle East    South Korea
## 14 SK_123    65     60-69      M Outside Middle East    South Korea
## # ... with 9 more variables: loc_hosp <fctr>, dt_onset <date>,
## #   dt_report <date>, week_report <fctr>, dt_start_exp <date>,
## #   dt_end_exp <date>, dt_diag <date>, outcome <fctr>, dt_death <date>
## 
##   // contacts
## 
## # A tibble: 33 × 4
##     from     to       exposure diff_dt_onset
##    <chr>  <chr>         <fctr>         <int>
## 1  SK_14 SK_113 Emergency room            10
## 2  SK_14 SK_116 Emergency room            13
## 3  SK_14  SK_41 Emergency room            14
## 4  SK_14 SK_112 Emergency room            14
## 5  SK_14 SK_100 Emergency room            15
## 6  SK_14 SK_114 Emergency room            15
## 7  SK_14 SK_136 Emergency room            15
## 8  SK_14  SK_47 Emergency room            16
## 9  SK_14 SK_110 Emergency room            16
## 10 SK_14 SK_122 Emergency room            16
## # ... with 23 more rows

In addition to subsetting by node and edge attributes, networks can be pruned to only include components that are connected to certain nodes. The “id” argument takes a vector of nodes and returns the line list of individuals that “touch” those IDs.

nodes <- c("SK_14","SK_145")                  
subset(merskor15, cluster_id = nodes)
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 97 cases in linelist; 98 contacts;  non directed 
## 
##   // linelist
## 
## # A tibble: 97 × 15
##       id   age age_class    sex        place_infect reporting_ctry
##    <chr> <int>     <chr> <fctr>              <fctr>         <fctr>
## 1   SK_1    68     60-69      M         Middle East    South Korea
## 2   SK_2    63     60-69      F Outside Middle East    South Korea
## 3   SK_3    76     70-79      M Outside Middle East    South Korea
## 4   SK_4    46     40-49      F Outside Middle East    South Korea
## 5   SK_5    50     50-59      M Outside Middle East    South Korea
## 6   SK_6    71     70-79      M Outside Middle East    South Korea
## 7   SK_7    28     20-29      F Outside Middle East    South Korea
## 8   SK_8    46     40-49      F Outside Middle East    South Korea
## 9  SK_10    44     40-49      M Outside Middle East          China
## 10 SK_11    79     70-79      F Outside Middle East    South Korea
## # ... with 87 more rows, and 9 more variables: loc_hosp <fctr>,
## #   dt_onset <date>, dt_report <date>, week_report <fctr>,
## #   dt_start_exp <date>, dt_end_exp <date>, dt_diag <date>,
## #   outcome <fctr>, dt_death <date>
## 
##   // contacts
## 
## # A tibble: 98 × 4
##     from     to       exposure diff_dt_onset
##    <chr>  <chr>         <fctr>         <int>
## 1  SK_14 SK_113 Emergency room            10
## 2  SK_14 SK_116 Emergency room            13
## 3  SK_14  SK_41 Emergency room            14
## 4  SK_14 SK_112 Emergency room            14
## 5  SK_14 SK_100 Emergency room            15
## 6  SK_14 SK_114 Emergency room            15
## 7  SK_14 SK_136 Emergency room            15
## 8  SK_14  SK_47 Emergency room            16
## 9  SK_14 SK_110 Emergency room            16
## 10 SK_14 SK_122 Emergency room            16
## # ... with 88 more rows

The subset() method for epicontacts objects also accepts cluster size parameters (see “cs”, “cs_min” and “cs_max” arguments).

subset(merskor15, cs = 3)
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 3 cases in linelist; 2 contacts;  non directed 
## 
##   // linelist
## 
## # A tibble: 3 × 15
##       id   age age_class    sex        place_infect reporting_ctry
##    <chr> <int>     <chr> <fctr>              <fctr>         <fctr>
## 1  SK_76    75     70-79      F Outside Middle East    South Korea
## 2 SK_145    37     30-39      M Outside Middle East    South Korea
## 3 SK_150    44     40-49      M Outside Middle East    South Korea
## # ... with 9 more variables: loc_hosp <fctr>, dt_onset <date>,
## #   dt_report <date>, week_report <fctr>, dt_start_exp <date>,
## #   dt_end_exp <date>, dt_diag <date>, outcome <fctr>, dt_death <date>
## 
##   // contacts
## 
## # A tibble: 2 × 4
##    from     to         exposure diff_dt_onset
##   <chr>  <chr>           <fctr>         <int>
## 1 SK_76 SK_145 Contact with HCW             5
## 2 SK_76 SK_150    Hospital room             6
subset(merskor15, cs_min = 10, cs_max = 100)
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 94 cases in linelist; 96 contacts;  non directed 
## 
##   // linelist
## 
## # A tibble: 94 × 15
##       id   age age_class    sex        place_infect reporting_ctry
##    <chr> <int>     <chr> <fctr>              <fctr>         <fctr>
## 1   SK_1    68     60-69      M         Middle East    South Korea
## 2   SK_2    63     60-69      F Outside Middle East    South Korea
## 3   SK_3    76     70-79      M Outside Middle East    South Korea
## 4   SK_4    46     40-49      F Outside Middle East    South Korea
## 5   SK_5    50     50-59      M Outside Middle East    South Korea
## 6   SK_6    71     70-79      M Outside Middle East    South Korea
## 7   SK_7    28     20-29      F Outside Middle East    South Korea
## 8   SK_8    46     40-49      F Outside Middle East    South Korea
## 9  SK_10    44     40-49      M Outside Middle East          China
## 10 SK_11    79     70-79      F Outside Middle East    South Korea
## # ... with 84 more rows, and 9 more variables: loc_hosp <fctr>,
## #   dt_onset <date>, dt_report <date>, week_report <fctr>,
## #   dt_start_exp <date>, dt_end_exp <date>, dt_diag <date>,
## #   outcome <fctr>, dt_death <date>
## 
##   // contacts
## 
## # A tibble: 96 × 4
##     from     to       exposure diff_dt_onset
##    <chr>  <chr>         <fctr>         <int>
## 1  SK_14 SK_113 Emergency room            10
## 2  SK_14 SK_116 Emergency room            13
## 3  SK_14  SK_41 Emergency room            14
## 4  SK_14 SK_112 Emergency room            14
## 5  SK_14 SK_100 Emergency room            15
## 6  SK_14 SK_114 Emergency room            15
## 7  SK_14 SK_136 Emergency room            15
## 8  SK_14  SK_47 Emergency room            16
## 9  SK_14 SK_110 Emergency room            16
## 10 SK_14 SK_122 Emergency room            16
## # ... with 86 more rows

Visualisation

Default Plotting Method

One of the main features of epicontacts is its visualisation capabilities. As a default, the package uses interactive plotting based on the visNetwork package1. This interactivity is particularly useful for visualising large datasets.

plot(merskor15) 

The above is a generic method based on the vis_epicontacts() and accepts a number of arguments to customize the plot appearance and functionality. For a full list of options use ?vis_epicontacts(). Alternatively, the method used for plotting can be graph3D, in which case a 3-dimensional graph will be used (see below).

3D plots

epicontacts loads the threejs package to enable 3D visualisation tools with the graph3D() function2.

graph3D(merskor15, group = "sex", g_title = "MERS Korea 2014")

To interact with the plot:

  • zoom: scrollwheel
  • rotate: left-mouse button + move
  • pan: right-mouse button + move
  • identify node by label: mouse over

Analysis

Extract Characteristics of Pairwise Nodes

The get_pairwise() function allows processing of variable(s) in the line list according to each pair in the contact dataset. For the following example, date of onset of disease is extracted from the line list in order to compute the difference between disease date of onset for each pair. The value that is produced from this comparison represents the serial interval (si).

si <- get_pairwise(merskor15, "dt_onset")   
summary(si)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.00   11.00   14.00   14.47   18.00   27.00
hist(si, col="grey", border="white", xlab="Days after symptoms",
     main="MERS Korea 2014\nSerial Interval")

The get_pairwise() will interpret the class of the column being used for comparison, and will adjust its method of comparing the values accordingly. For numbers and dates (like the si example above), the function will subtract the values. When applied to columns that are characters or categorical, get_pairwise() will paste values together. Because the function also allows for arbitrary processing (see “f” argument), these discrete combinations can be easily tabulated and analyzed.

head(get_pairwise(merskor15, "sex"), n = 10)
##  [1] "M - M" "M - F" "M - F" "M - M" "M - F" "M - M" "M - M" "M - F"
##  [9] "M - F" "M - F"
get_pairwise(merskor15, "sex", f=table)
##            values.to
## values.from  F  M
##           F  2  4
##           M 38 54
fisher.test(get_pairwise(merskor15, "sex", f=table)) 
## 
##  Fisher's Exact Test for Count Data
## 
## data:  get_pairwise(merskor15, "sex", f = table)
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.06158088 5.26628732
## sample estimates:
## odds ratio 
##   0.712926

Identify Clusters

The get_clusters() function can be used for to identify connected components in an epicontacts object. Here, we illustrate its use to study contact patterns in a simulated Ebola outbreak. First, we use it to retrieve data.frame containing the cluster information:

x <- make_epicontacts(ebola_sim$linelist, ebola_sim$contacts,
                      id = "case_id", to = "case_id", from = "infector",
                      directed = TRUE)
x
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 5,888 cases in linelist; 3,800 contacts;  directed 
## 
##   // linelist
## 
## # A tibble: 5,888 × 9
##        id generation date_of_infection date_of_onset
## *   <chr>      <int>            <date>        <date>
## 1  d1fafd          0              <NA>    2014-04-07
## 2  53371b          1        2014-04-09    2014-04-15
## 3  f5c3d8          1        2014-04-18    2014-04-21
## 4  6c286a          2              <NA>    2014-04-27
## 5  0f58c4          2        2014-04-22    2014-04-26
## 6  49731d          0        2014-03-19    2014-04-25
## 7  f9149b          3              <NA>    2014-05-03
## 8  881bd4          3        2014-04-26    2014-05-01
## 9  e66fa4          2              <NA>    2014-04-21
## 10 20b688          3              <NA>    2014-05-05
## # ... with 5,878 more rows, and 5 more variables:
## #   date_of_hospitalisation <date>, date_of_outcome <date>,
## #   outcome <fctr>, gender <fctr>, hospital <fctr>
## 
##   // contacts
## 
## # A tibble: 3,800 × 3
##      from     to  source
## *   <chr>  <chr>  <fctr>
## 1  d1fafd 53371b   other
## 2  cac51e f5c3d8 funeral
## 3  f5c3d8 0f58c4   other
## 4  0f58c4 881bd4   other
## 5  8508df 40ae5f   other
## 6  127d83 f547d6 funeral
## 7  f5c3d8 d58402   other
## 8  20b688 d8a13d   other
## 9  2ae019 a3c8b8   other
## 10 20b688 974bc1   other
## # ... with 3,790 more rows
clust <- get_clusters(x, output = "data.frame")
class(clust)
## [1] "data.frame"
dim(clust)
## [1] 7047    3
table(clust$cluster_size)
## 
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14 
## 1536 1680 1182  784  545  342  308  208  171  100   99   24   26   42
barplot(table(clust$cluster_size),
        main = "Cluster size distribution",
    xlab = "Cluster size",
    ylab = "Frequency")

Let us look at the largest clusters. For this, we add cluster information to the epicontacts object, and then subset it:

x <- get_clusters(x)
x_14 <- subset(x, cs = 14)
plot(x_14, "cluster_member")

Calculate Degree

The degree of a node corresponds to its number of edges or connections to other nodes. get_degree() provides an easy method for calculating this value for epicontacts networks. A high degree in this context indicates an individual who was in contact with many others.

nb use of “type” argument depends on whether or not the network is directed.

deg_both <- get_degree(merskor15, "both", only_linelist = TRUE)

Which individuals have the ten most contacts?

head(sort(deg_both, decreasing = TRUE), 10)
## SK_14  SK_1 SK_16 SK_15  SK_6 SK_39 SK_11 SK_12 SK_76 SK_87 
##    39    26    22     5     3     3     2     2     2     2

What is the mean number of contacts?

mean(deg_both)
## [1] 1.209877

References