knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width=7, fig.height=5, fig.path="figs-overview/" )
epitrix implements small helper functions usefull in infectious disease modelling and epidemics analysis. This vignette provides a quick overview of the package's features.
The main features of the package include:
gamma_shapescale2mucv
: convert shape and scale of a Gamma distribution
to mean and CV
gamma_mucv2shapescale
: convert mean and CV of a Gamma distribution to
shape and scale
gamma_log_likelihood
: Gamma log-likelihood using mean and CV
r2R0
: convert growth rate into a reproduction number
lm2R0_sample
: generates a distribution of R0 from a log-incidence linear
model
fit_disc_gamma
: fits a discretised Gamma distribution to data (typically
useful for describing delays)
hash_names
: generate unique, anonymised, reproducible labels from
various data fields (e.g. First name, Last name, Date of birth).
In this example, we simulate data which replicate the serial interval (SI), i.e. the delays between primary and secondary symptom onsets, in Ebola Virus Disease (EVD). We start by converting previously estimates of the mean and standard deviation of the SI (WHO Ebola Response Team (2014) NEJM 371:1481–1495) to the parameters of a Gamma distribution:
library(epitrix) mu <- 15.3 # mean in days days sigma <- 9.3 # standard deviation in days cv <- mu/sigma # coefficient of variation cv param <- gamma_mucv2shapescale(mu, cv) # convertion to Gamma parameters param
The shape and scale are parameters of a Gamma distribution we can use to
generate delays. However, delays are typically reported per days, which implies
a discretisation (from continuous time to discrete numbers). We use the package
distcrete to achieve this
discretisation. It generates a list of functions, including one to simulate data
($r
), which we use to simulate 500 delays:
si <- distcrete::distcrete("gamma", interval = 1, shape = param$shape, scale = param$scale, w = 0) si set.seed(1) x <- si$r(500) head(x, 10) hist(x, col = "grey", border = "white", xlab = "Days between primary and secondary onset", main = "Simulated serial intervals")
x
contains simulated data, for illustrative purpose. In practice, one would
use real data from an ongoing outbreaks. Now we use fit_disc_gamma
to estimate
the parameters of a dicretised Gamma distribution from the data:
si_fit <- fit_disc_gamma(x) si_fit
The package incidence can fit a
log-linear model to incidence curves (function fit
), which produces a growth
rate (r). This growth rate can in turn be translated into a basic reproduction
number (R0) using r2R0
. We illustrate this using simulated Ebola data from the
outbreaks package, and using the
serial interval from the previous example:
library(outbreaks) library(incidence) i <- incidence(ebola_sim$linelist$date_of_onset) i f <- fit(i[1:150]) # fit on first 150 days plot(i[1:200], fit = f, color = "#9fc2fc") r2R0(f$info$r, si$d(1:100)) r2R0(f$info$r.conf, si$d(1:100))
In addition, we can also use the function lm2R0_sample
to generate samples of
R0 values compatible with a model fit:
R0_val <- lm2R0_sample(f$model, si$d(1:100), n = 100) head(R0_val) hist(R0_val, col = "grey", border = "white")
If you want to use labels that will work across different computers, independent
of local encoding and operating systems, clean_labels
will make your life
easier. The function transforms character strings by replacing diacritic symbols
with their closest alphanumeric matches, setting all characters to lower case,
and replacing various separators with a single, consistent one.
For instance:
x <- " Thîs- is A wêïrD LäBeL .." x clean_labels(x) variables <- c("Date.of.ONSET ", "/ date of hôspitalisation /", "-DäTÈ--OF___DîSCHARGE-", "GEndèr/", " Location. ") variables clean_labels(variables)
hash_names
can be used to generate hashed labels from linelist data. Based on
pre-defined fields, it will generate anonymous labels. This system has the
following desirable features:
given the same input, the output will always be the same, so this encoding system generates labels which can be used by different people and organisations
given different inputs, the output will always be different; even minor differences in input will result in entirely different outputs
given an output, it is very hard to infer the input (it requires hacking skills); if security is challenged, the hashing algorithm can be 'salted' to strengthen security
first_name <- c("Jane", "Joe", "Raoul", "Raoul") last_name <- c("Doe", "Smith", "Dupont", "Dupond") age <- c(25, 69, 36, 36) ## detailed output by default hash_names(first_name, last_name, age) ## short labels for practical use hash_names(first_name, last_name, age, size = 8, full = FALSE)
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