docs/articles/demo.md

In reconhub/vimes: VIsualisation and Monitoring of EpidemicS

title: "Detecting disease outbreaks using vimes" author: "Thibaut Jombart" date: "2017-02-28" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{vimes: a quick demo.} \usepackage[utf8]{inputenc}

vimes: VIsualisation and Monitoring of EpidemicS

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vimes provides tools for integrating various types of surveillance data for detecting disease outbreaks. This document provides an overview of the package's content.

Installing vimes

To install the development version from github:

library(devtools)
install_github("reconhub/vimes")

The stable version can be installed from CRAN using:

install.packages("vimes")

Then, to load the package, use:

library("vimes")

A short demo

Here is a short demonstration of the package using a dummy dataset.

We first simulate the data using 3 mixtures of 3 normal distributions, and compute Euclidean distances between the observations for each mixture. In practice, each mixture would be a different data type (e.g. location, time of onset of symptoms, genetic sequences of the pathogen):

set.seed(2)
dat1 <- rnorm(30, c(0,1,6))
dat2 <- rnorm(30, c(0,0,1))
dat3 <- rnorm(30, c(8,1,2))
x <- lapply(list(dat1, dat2, dat3), dist)

The function vimes_data processes the data and ensures matching of the individuals across different data sources:

x <- vimes_data(x)
plot(x)

We can now run vimes on the data:

res <- vimes(x, cutoff = c(2,4,2))
names(res)

## [1] "graph"           "clusters"        "cutoff"          "separate_graphs"

res$graph

## IGRAPH UN-- 30 104 -- 
## + attr: layout_1 (g/n), layout_2 (g/n), layout_3 (g/n), layout
## | (g/n), color_1 (v/c), color_2 (v/c), color_3 (v/c), size_1
## | (v/n), size_2 (v/n), size_3 (v/n), label.family_1 (v/c),
## | label.family_2 (v/c), label.family_3 (v/c), label.color_1 (v/c),
## | label.color_2 (v/c), label.color_3 (v/c), name (v/c), color
## | (v/c), size (v/n), label.family (v/c), label.color (v/c),
## | weight_1 (e/n), weight_2 (e/n), weight_3 (e/n), label.color_1
## | (e/c), label.color_2 (e/c), label.color_3 (e/c), label.color
## | (e/c)
## + edges (vertex names):

res$clusters

## $membership
##  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 
##  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1 
## 26 27 28 29 30 
##  2  3  1  2  3 
## 
## $size
## [1] 10 10 10
## 
## $K
## [1] 3
## 
## $color
##         1         2         3 
## "#ccddff" "#79d2a6" "#ffb3b3"

The main graph is:

plot(res$graph, main="Main graph")

for(i in 1:3) {
plot(res$separate_graphs[[i]]$graph, main = paste("Graph from data", i))
}

reconhub/vimes documentation built on May 27, 2019, 4:03 a.m.