Fitting epicurves

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  fig.width = 7,
  fig.height = 5
)

Example

To illustrate the trend fitting functionality of i2extras we will use the simulated Ebola Virus Disease (EVD) outbreak data from the outbreaks package.

Loading relevant packages and data

library(outbreaks)
library(incidence2)
library(i2extras)

raw_dat <- ebola_sim_clean$linelist

For this example we will restrict ourselves to the first 20 weeks of data:

dat <- incidence(
    raw_dat, 
    date_index = date_of_onset,
    interval = "week"
)[1:20, ]
dat
plot(dat)

Modeling incidence

We can use fit_curve() to fit the data with either a poisson or negative binomial regression model. The output from this will be a nested object with class incidence2_fit which has methods available for both automatic plotting and the calculation of growth (decay) rates and doubling (halving) times.

out <- fit_curve(dat, model = "poisson", alpha = 0.05)
out
plot(out)
growth_rate(out)

To unnest the data we can use unnest() (a function that has been reexported from the tidyr package.

unnest(out, estimates)

fit_curve() also works nicely with grouped incidence2 objects. In this situation, we return a nested tibble with some additional columns that indicate whether there has been a warning or error during the fitting or prediction stages.

grouped_dat <- incidence(
    raw_dat, 
    date_index = date_of_onset,
    interval = "week",
    groups = hospital
)[1:120, ]
grouped_dat

out <- fit_curve(grouped_dat, model = "poisson", alpha = 0.05)
out

# plot with a prediction interval but not a confidence interval
plot(out, ci = FALSE, pi=TRUE)
growth_rate(out)

We provide helper functions, is_ok(), is_warning() and is_error() to help filter the output as necessary.

out <- fit_curve(grouped_dat, model = "negbin", alpha = 0.05)
is_warning(out)
unnest(is_warning(out), fitting_warning)

Rolling average

We can add a rolling average, across current and previous intervals, to an incidence2 object with the add_rolling_average() function:

ra <- add_rolling_average(grouped_dat, before = 2) # group observations with the 2 prior
ra
unnest(ra, rolling_average)

plot(ra, color = "white")


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i2extras documentation built on July 8, 2021, 5:08 p.m.