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).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 11.00 14.00 14.47 18.00 27.00hist(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.
## [1] "M - M" "M - F" "M - F" "M - M" "M - F" "M - M" "M - M" "M - F" "M - F"
## [10] "M - F"## values.to
## values.from F M
## F 2 4
## M 38 54##
## 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
## 0.712926Identify 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 x 11
## id generation date_of_infection date_of_onset date_of_hospitalisation
## <chr> <int> <date> <date> <date>
## 1 d1fafd 0 NA 2014-04-07 2014-04-17
## 2 53371b 1 2014-04-09 2014-04-15 2014-04-20
## 3 f5c3d8 1 2014-04-18 2014-04-21 2014-04-25
## 4 6c286a 2 NA 2014-04-27 2014-04-27
## 5 0f58c4 2 2014-04-22 2014-04-26 2014-04-29
## 6 49731d 0 2014-03-19 2014-04-25 2014-05-02
## 7 f9149b 3 NA 2014-05-03 2014-05-04
## 8 881bd4 3 2014-04-26 2014-05-01 2014-05-05
## 9 e66fa4 2 NA 2014-04-21 2014-05-06
## 10 20b688 3 NA 2014-05-05 2014-05-06
## # i 5,878 more rows
## # i 6 more variables: date_of_outcome <date>, outcome <fct>, gender <fct>,
## # hospital <fct>, lon <dbl>, lat <dbl>
##
## // contacts
##
## # A tibble: 3,800 x 3
## from to source
## <chr> <chr> <fct>
## 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
## # i 3,790 more rows## [1] "data.frame"## [1] 7047 3##
## 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 42barplot(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:
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.
Which individuals have the ten most contacts?
## 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 2What is the mean number of contacts?
## [1] 1.209877