Handling, Visualisation and Analysis of Epidemiological Contacts
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R
docs
figs
inst
logo
man
tests
vignettes
.Rbuildignore
.gitignore
.travis.yml
DESCRIPTION
NAMESPACE
NEWS.md
README.Rmd
README.md
appveyor.yml
cran-comments.md
epicontacts.Rproj

README.md

Welcome to the epicontacts package!


Travis-CI Build Status Build status Coverage Status CRAN_Status_Badge CRAN Downloads Downloads from Rstudio mirror


Installing the package

To install the current stable, CRAN version of the package, type:

install.packages("epicontacts")

To benefit from the latest features and bug fixes, install the development, github version of the package using:

devtools::install_github("reconhub/epicontacts")

Note that this requires the package devtools installed.

What does it do?

The main features of the package include:

  • epicontacts: a new S3 class for storing linelists and contacts data

  • make_epicontacts: a constructor for the new epicontacts class

  • get_id: access unique IDs in an epicontacts with various options

  • get_pairwise: extract attributes of record(s) in contacts database using information provided in the linelist data of an epicontacts object.

  • get_degree: access degree of cases in epicontacts with various options

  • x[i,j,contacts]: subset an epicontacts object by retaining specified cases

  • thin: retains matching cases in linelist / contacts

  • summary: summary for epicontacts objects

  • plot: plot for epicontacts objects; various types of plot are available; default to vis_epicontacts

  • vis_epicontacts: plot an epicontacts object using visNetwork

  • as.igraph.epicontacts: create an igraph object from an epicontacts object

  • get_clusters: assign clusters and corresponding cluster sizes to linelist of an epicontacts object (clusters being groups of connected individuals/nodes).

  • subset_clusters_by_id: subset an epicontacts object based on a IDs of cases of interest.

  • subset_clusters_by_size: subset an epicontacts object based on size(s) of clusters (clusters being groups of connected individuals/nodes).

  • graph3D: 3D graph from an epicontacts object.

Resources

Vignettes

An overview of epicontacts is provided below in the worked example below. More detailed tutorials are distributed as vignettes with the package:

vignette("overview", package="epicontacts")
vignette("customize_plot", package="epicontacts")
vignette("epicontacts_class", package="epicontacts")

Websites

The following websites are available:

Getting help online

Bug reports and feature requests should be posted on github using the issue system. All other questions should be posted on the RECON forum:
http://www.repidemicsconsortium.org/forum/

A quick overview

The following worked example provides a brief overview of the package's functionalities. See the vignettes section for more detailed tutorials.

Obtaining an epicontacts object

epicontacts need two types of data:

  • a linelist, i.e. a spreadsheet documenting cases where columns are variables and rows correspond to unique cases

  • a list of edges, defining connections between cases, identified by their unique identifier

There does not need to be an one-to-one correspondance between cases documented in the linelist and those appearing in the contacts. However, links will be made between the two sources of data whenever unique identifiers match. This will be especially handy when subsetting data, or when characterising contacts in terms of 'node' (case) properties.

We illustrate the construction of an epicontacts using contact data from a Middle East Respiratory Syndrom coronavirus (MERS CoV) from South Korea in 2015, available as the dataset mers_korea_2015 in the package outbreaks.

library(outbreaks)
library(epicontacts)

names(mers_korea_2015)
#> [1] "linelist" "contacts"
dim(mers_korea_2015$linelist) 
#> [1] 162  15
dim(mers_korea_2015$contacts) 
#> [1] 98  4

x <- make_epicontacts(linelist = mers_korea_2015$linelist,
                      contacts = mers_korea_2015$contacts, 
                      directed = TRUE)
x
#> 
#> /// Epidemiological Contacts //
#> 
#>   // class: epicontacts
#>   // 162 cases in linelist; 98 contacts;  directed 
#> 
#>   // linelist
#> 
#> # A tibble: 162 x 15
#>    id      age age_class sex   place_infect  reporting_ctry loc_hosp      
#>  * <chr> <int> <chr>     <fct> <fct>         <fct>          <fct>         
#>  1 SK_1     68 60-69     M     Middle East   South Korea    Pyeongtaek St…
#>  2 SK_2     63 60-69     F     Outside Midd… South Korea    Pyeongtaek St…
#>  3 SK_3     76 70-79     M     Outside Midd… South Korea    Pyeongtaek St…
#>  4 SK_4     46 40-49     F     Outside Midd… South Korea    Pyeongtaek St…
#>  5 SK_5     50 50-59     M     Outside Midd… South Korea    365 Yeollin C…
#>  6 SK_6     71 70-79     M     Outside Midd… South Korea    Pyeongtaek St…
#>  7 SK_7     28 20-29     F     Outside Midd… South Korea    Pyeongtaek St…
#>  8 SK_8     46 40-49     F     Outside Midd… South Korea    Seoul Clinic,…
#>  9 SK_9     56 50-59     M     Outside Midd… South Korea    Pyeongtaek St…
#> 10 SK_10    44 40-49     M     Outside Midd… China          Pyeongtaek St…
#> # ... with 152 more rows, and 8 more variables: dt_onset <date>,
#> #   dt_report <date>, week_report <fct>, dt_start_exp <date>,
#> #   dt_end_exp <date>, dt_diag <date>, outcome <fct>, dt_death <date>
#> 
#>   // contacts
#> 
#> # A tibble: 98 x 4
#>    from  to     exposure       diff_dt_onset
#>    <chr> <chr>  <fct>                  <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
class(x)
#> [1] "epicontacts"
summary(x)
#> 
#> /// 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. 
#>    0.00    1.00    1.00    1.01    1.00    3.00 
#> 
#>   // out-degree summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    0.00    0.00    1.01    0.00   38.00 
#> 
#>   // 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

In practice, the linelist and contacts will usually be imported from a text file (e.g. .txt, .csv) or a spreadsheet (e.g. .ods, .xls) using usual import functions (e.g. read.table, read.csv, gdata::read.xls).

Simple analyses

First, we plot the epicontacts object:

plot(x, selector = FALSE)

This is a screenshot of the actual plot. For interactive graphs, see the introductory vignette.

We can look for patterns of contacts between genders:

plot(x, "sex", col_pal = spectral)

There is no obvious signs of non-random mixing patterns, but this is worth testing. The function get_pairwise is particularly useful for this. In its basic form, it reports nodes of attributes for all contacts. For instance:

get_pairwise(x, attribute = "sex")
#>  [1] "M -> M" "M -> F" "M -> F" "M -> M" "M -> F" "M -> M" "M -> M"
#>  [8] "M -> F" "M -> F" "M -> F" "M -> M" "M -> M" "M -> F" "M -> F"
#> [15] "M -> F" "M -> M" "M -> F" "M -> M" "M -> F" "M -> M" "M -> M"
#> [22] "M -> M" "M -> M" "M -> M" "M -> M" "M -> M" "M -> M" "M -> M"
#> [29] "M -> F" "M -> M" "M -> F" "M -> M" "F -> F" "M -> F" "M -> F"
#> [36] "M -> F" "M -> M" "M -> F" "M -> M" "M -> M" "M -> F" "F -> M"
#> [43] "M -> M" "M -> M" "M -> M" "M -> F" "M -> F" "M -> F" "M -> M"
#> [50] "M -> F" "M -> M" "F -> M" "M -> M" "M -> F" "M -> M" "M -> F"
#> [57] "M -> F" "M -> M" "M -> M" "M -> M" "M -> M" "M -> M" "M -> M"
#> [64] "M -> F" "M -> F" "F -> M" "M -> M" "M -> M" "M -> M" "M -> M"
#> [71] "M -> M" "M -> M" "M -> F" "M -> F" "M -> M" "M -> M" "M -> F"
#> [78] "M -> F" "M -> M" "M -> F" "M -> M" "M -> F" "F -> M" "M -> F"
#> [85] "F -> F" "M -> F" "M -> M" "M -> M" "M -> M" "M -> M" "M -> M"
#> [92] "M -> F" "M -> M" "M -> M" "M -> F" "M -> F" "M -> M" "M -> M"

However, one can specify the function to be used to compare the attributes of connected nodes; for instance:

sex_tab <- get_pairwise(x, attribute = "sex", f = table)
sex_tab
#>            values.to
#> values.from  F  M
#>           F  2  4
#>           M 38 54
fisher.test(sex_tab)
#> 
#> 	Fisher's Exact Test for Count Data
#> 
#> data:  sex_tab
#> 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

Indeed, there are no patterns of association between genders. We can also use this function to compute delays between dates. For instance, to get the distribution of the serial interval (delay between primary and secondary onset):

si <- get_pairwise(x, attribute = "dt_onset")
si
#>  [1] 10 13 14 14 15 15 15 16 16 16 17 18 18 18 18 19 19 19 20 20 20 21 21
#> [24] 22 22 22 24 24 24 25 17 15  2  7  9 10 14 15 14 22 15  5  9 27 10 12
#> [47] 14 17  9 14 14  6  6  9  9  9 10 12 13 21  5 14 16 20 21 13  3 11 11
#> [70] 12 12 12 12 13 13 14 15 15 16 18 21 22 12 11  9  3 10 10 10 10 11 12
#> [93] 12 13 14 16 17 18
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", nclass = 30, 
     xlab = "Time to secondary onset (days)", 
     main = "Distribution of the serial interval")

plot of chunk si

Contributors (by alphabetic order):

See details of contributions on:
https://github.com/reconhub/epicontacts/graphs/contributors

Contributions are welcome via pull requests.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Maintainer: VP Nagraj (vpnagraj@virginia.edu)