Package 'projections'

Subsetting 'projections' objects

Description

Two functions can be used to subset projections objects. The operator "[" can be used as for matrices, using the syntax x[i,j] where 'i' is a subset of dates, and 'j' is a subset of simulations.

Usage

## S3 method for class 'projections'
x[i, j]

## S3 method for class 'projections'
subset(x, ..., from = NULL, to = NULL, sim = TRUE)

Arguments

x	An projections object, generated by the function project.
i	a subset of dates to retain
j	a subset of groups to retain
...	Further arguments passed to other methods (not used).
from	The starting date; data strictly before this date are discarded.
to	The ending date; data strictly after this date are discarded.
sim	(optional) The simulations to retained, indicated as subsets of the columns of x.

Author(s)

Thibaut Jombart [email protected]

See Also

The project function to generate the 'projections' objects.

---

Conversion of projections objects

Description

These functions convert projections objects into other classes.

Usage

## S3 method for class 'projections'
as.matrix(x, ...)

## S3 method for class 'projections'
as.data.frame(x, ..., long = FALSE)

Arguments

x	An projections object, or an object to be converted as projections (see details).
...	Further arguments passed to other functions (no used).
long	A logical indicating if the output data.frame should be 'long', i.e. where a single column containing 'groups' is added in case of data computed on several groups.

Author(s)

Thibaut Jombart [email protected]

See Also

the project function to generate the 'projections' objects.

---

Constructor for projections objects

Description

This function builds a valid projections object from some input simulations and dates.

Usage

build_projections(x, dates = NULL, cumulative = FALSE, order_dates = TRUE)

Arguments

x	A matrix of simulated incidence stored as integers, where rows correspond to dates and columns to simulations.
dates	A vector of dates containing one value per row in x; acceptable formats are: integer, Date, and POSIXct; if NULL, the time steps will be counted, with the first dates corresponding to 0.
cumulative	A logical indicating if data represent cumulative incidence; defaults to FALSE.
order_dates	A logical indicating whether the dates should be ordered, from the oldest to the most recent one; TRUE by default.

Author(s)

Thibaut Jombart [email protected]

See Also

the project function to generate the 'projections' objects.

---

Compute cumulative projections

Description

cumulate is an S3 generic to compute cumulative numbers defined in the package incidence. The method for projections objects turns predicted incidences into cumulative incidences over time.

Usage

## S3 method for class 'projections'
cumulate(x)

Arguments

x	A projections object.

Author(s)

Thibaut Jombart [email protected]

See Also

The project function to generate the projections objects.

Examples

if (require(distcrete) &&
    require(incidence)) {

  ## simulate basic epicurve
  dat <- c(0, 2, 2, 3, 3, 5, 5, 5, 6, 6, 6, 6)
  i <- incidence(dat)


  ## example with a function for SI
  si <- distcrete("gamma", interval = 1L,
                  shape = 1.5,
                  scale = 2, w = 0)
  set.seed(1)
  pred_1 <- project(i, runif(100, 0.8, 1.9), si, n_days = 30)
  plot_1 <- plot(pred_1)

  ## cumulative predictions
  pred_1_cum <- cumulate(pred_1)
  pred_1_cum
  plot(pred_1_cum)
}

---

Access content projections objects

Description

These simple helper functions retrieve content from projections objects. They currently include:

Usage

## S3 method for class 'projections'
get_dates(x, ...)

Arguments

x	A projections object.
...	Further arguments passed to methods; currently not used.

Details

• get_dates: get dates of the predictions.

Author(s)

Thibaut Jombart [email protected]

Examples

if (require(distcrete) && require(incidence)) { withAutoprint({

## prepare input: epicurve and serial interval
dat <- c(0, 2, 2, 3, 3, 5, 5, 5, 6, 6, 6, 6)
i <- incidence(dat)
si <- distcrete("gamma", interval = 1L,
                 shape = 1.5,
                 scale = 2, w = 0)


## make predictions
pred_1 <- project(i, 1.2, si, n_days = 30)
pred_1


## retrieve content
get_dates(pred_1)
max(i$dates) # predictions start 1 day after last incidence

})}

---

Add data of different projections objects

Description

This function adds counts from several projections objects, making sure that they all use the same dates, adding rows of '0' where needed. Simulations (columns) are recycled when needed if some objects have less simulations than others. The same operation is implemented by the + operator.

Usage

merge_add_projections(x)

## S3 method for class 'projections'
a + b

Arguments

x	A list of projections objects to be added.
a	A projections object.
b	A projections object.

Author(s)

Thibaut Jombart

Examples

if (require(incidence)) {

  ## make toy data and projections 
  set.seed(1)
  i <- incidence::incidence(as.Date('2020-01-01') +
                          sample(1:20, 50, replace = TRUE))
  si <- c(0.2, 0.5, 0.2, 0.1)

  x_1 <- project(x = i[1:10],
                 si = si,
                 R = 3.5,
                 n_sim = 200,
                 n_days = 5)

  x_2 <- project(x = i[11:20],
                 si = si,
                 R = 1.8,
                 n_sim = 300,
                 n_days = 10
                 )

  ## check simulations
  x_1 # first type
  x_2 # other simulations
  y <- x_1 + x_2 # add simulations
  plot(y)

}

---

Merge a list of projections objects

Description

This function merges projections objects, binding them by columns, making sure that they all use the same dates, adding rows of '0' where needed.

Usage

merge_projections(x)

Arguments

x	A list of projections objects to be merged.

Author(s)

Thibaut Jombart

Examples

## generate toy data
dates <- Sys.Date() + c(0, 0, 2, 5, 6, 6, 7)
i <- incidence::incidence(dates)
si <- c(0.2, 0.5, 0.2, 0.1)
R0 <- 3.5

## make several projections objects
x <- lapply(1:10,
            function(j)
              project(x = i,
                      si = si,
                      R = R0,
                      n_sim = 2 * j,
                      R_fix_within = TRUE,
                      n_days = j,
                      model = "poisson"
                      ))
## see all dimensions
lapply(x, dim)
merge_projections(x)

---

Plot projections objects

Description

The plot method of projections objects (output by the function project) shows quantiles of predicted incidence over time. The function add_projections can be used to add a similar plot to an existing incidence plot. This latter function is piping friendly (see examples).

Usage

## S3 method for class 'projections'
plot(x, ylab = NULL, title = NULL, ...)

add_projections(
  p,
  x,
  quantiles = c(0.01, 0.05, 0.1, 0.5),
  ribbon = TRUE,
  boxplots = FALSE,
  palette = quantile_pal,
  quantiles_alpha = 1,
  linetype = 1,
  linesize = 0.5,
  ribbon_quantiles = NULL,
  ribbon_color = NULL,
  ribbon_alpha = 0.3,
  boxplots_color = "#47476b",
  boxplots_fill = "grey",
  boxplots_alpha = 0.8,
  outliers = TRUE
)

Arguments

x	A projections object.
ylab	An optional label for the y-axis. If missing will default to "predicted incidence" or, if cumulative, "predicted cumulative incidence"
title	An optional title.
...	Further arguments to be passed to add_projections.
p	A previous incidence plot to which projections should be added.
quantiles	A vector of quantiles to plot, automatically completed to be symmetric around the median.
ribbon	A logical indicating if a ribbon should be drawn; defaults to TRUE.
boxplots	A logical indicating if boxplots should be drawn.
palette	A color palette to be used for plotting the quantile lines; defaults to quantile_pal.
quantiles_alpha	A number used to control the transparency of the quantile lines, from 0 (full transparency) to 1 (full opacity); defaults to 1.
linetype	An integer indicating the type of line used for plotting the quantiles; defaults to 1 for a plain line.
linesize	An integer indicating the size of line used for plotting the quantiles; defaults to 0.5.
ribbon_quantiles	A vector of 2 quantiles to be used to determine the limits of the ribbon; if NULL (default); uses the most extreme quantiles if available; if quantiles are not provided, the daily range will be used.
ribbon_color	Any valid color, used for the ribbon.
ribbon_alpha	A number used to control the transparency of the ribbon, from 0 (full transparency) to 1 (full opacity); defaults to 0.3.
boxplots_color	Any valid color, used for the boxplot.
boxplots_fill	Any valid color, used for filling the boxplot.
boxplots_alpha	A number used to control the transparency of the boxplots, from 0 (full transparency) to 1 (full opacity); defaults to 0.8.
outliers	A logical indicating if outliers should be displayed alongside the boxplots; defaults to TRUE.

Author(s)

Thibaut Jombart [email protected]

See Also

project to generate projections

Examples

if (require(outbreaks) &&
    require(distcrete) &&
    require(incidence) &&
    require(magrittr)) {

si <- distcrete("gamma",
                 interval = 1L,
                 shape = 2.4,
                 scale = 4.7,
                 w = 0.5)

i <- incidence(ebola_sim$linelist$date_of_onset)
plot(i)

## add projections after the first 100 days, over 60 days
set.seed(1)
proj <- project(x = i[1:100], R = 1.4, si = si, n_days = 60)

## plotting projections: different options
plot(proj)
plot(proj, quantiles = c(.025, .5)) # 95% CI
plot(proj, ribbon_color = "red", quantiles = FALSE) # range
plot(proj, ribbon_color = "red", quantiles = FALSE,
     ribbon_quantiles = c(.025, .5))
plot(proj, boxplots = TRUE, quantiles = FALSE, ribbon = FALSE)
plot(proj, boxplots = TRUE, quantiles = FALSE, outliers = FALSE)
plot(proj, linetype = 3)

## adding them to incidence plot
plot(i) %>% add_projections(proj)
plot(i[1:160]) %>% add_projections(proj)
plot(i[1:160]) %>% add_projections(proj, boxplots = FALSE)
plot(i[1:160]) %>%
  add_projections(proj, boxplots_alpha = .3, boxplots_color = "red")

## same, with customised quantiles and colors
quantiles <- c(.001, .01, 0.05, .1, .2, .3, .4, .5)
pal <- colorRampPalette(c("#b3c6ff", "#00e64d", "#cc0066"))
plot(i[1:200]) %>%
  add_projections(proj, quantiles, palette = pal)

}

---

Print method for projections objects

Description

This method prints the content of projections objects.

Usage

## S3 method for class 'projections'
print(x, ...)

Arguments

x	A projections object.
...	further parameters to be passed to other methods (currently not used)

Author(s)

Thibaut Jombart ([email protected])

---

Project future incidence

Description

This function simulates future incidence based on past incidence data, a selection of plausible reproduction numbers (R), and the distribution of the serial interval (time from primary onset to secondary onset).

Usage

project(
  x,
  R,
  si,
  n_sim = 100,
  n_days = 7,
  R_fix_within = FALSE,
  model = c("poisson", "negbin"),
  size = 0.03,
  time_change = NULL,
  instantaneous_R = FALSE
)

Arguments

x	An incidence object containing daily incidence; other time intervals will trigger an error.
R	A vector of numbers representing plausible reproduction numbers; for instance, these can be samples from a posterior distribution using the earlyR or EpiEstim packages. If time_change is provided, then it must be a vector (for fixed values of R per time window) or a list of vectors (for separate distributions of R per time window), with one element more than the number of dates in time_change.
si	A function computing the serial interval, or a numeric vector providing its mass function, starting a day 1, so that si[i] is the PMF for serial interval of i. The model implicitly assumes that si[0] = 0. For functions, we strongly recommend using the RECON package distcrete to obtain such distribution (see example).
n_sim	The number of epicurves to simulate. Defaults to 100.
n_days	The number of days to run simulations for. Defaults to 14.
R_fix_within	A logical indicating if R should be fixed within simulations (but still varying across simulations). If FALSE, R is drawn for every simulation and every time step. Fixing values within simulations favours more extreme predictions (see details)
model	Distribution to be used for projections. Must be one of "poisson" or "negbin" (negative binomial process). Defaults to poisson
size	size parameter of negative binomial distribition. Ignored if model is poisson
time_change	an optional vector of times at which the simulations should use a different sample of reproduction numbers, provided in days into the simulation (so that day '1' is the first day after the input incidence object); if provided, n dates in time_change will produce n+1 time windows, in which case R should be a list of vectors of n+1 R values, one per each time window.
instantaneous_R	a boolean specifying whether to assume R is the case reproduction number (instantaneous_R = FALSE, the default), or the instantaneous reproduction number (instantaneous_R = TRUE). If instantaneous_R = FALSE then values of R at time t will govern the mean number of secondary cases of all cases infected at time t, even if those secondary cases appear after t. In other words, R will characterise onwards transmission from infectors depending on their date of infection. If instantaneous_R = TRUE then values of R at time t will govern the mean number of secondary cases made at time t by all cases infected before t. In other words, R will characterise onwards transmission at a given time.

Details

The decision to fix R values within simulations (R_fix_within) reflects two alternative views of the uncertainty associated with R. When drawing R values at random from the provided sample, (R_fix_within set to FALSE), it is assumed that R varies naturally, and can be treated as a random variable with a given distribution. When fixing values within simulations (R_fix_within set to TRUE), R is treated as a fixed parameter, and the uncertainty is merely a consequence of the estimation of R. In other words, the first view is rather Bayesian, while the second is more frequentist.

Author(s)

Pierre Nouvellet (original model), Thibaut Jombart (bulk of the code), Sangeeta Bhatia (Negative Binomial model), Stephane Ghozzi (bug fixes time varying R)

Examples

## example using simulated Ebola outbreak
if (require(outbreaks) &&
    require(distcrete) &&
    require(incidence) &&
    require(magrittr)) {

si <- distcrete("gamma", interval = 1L,
                 shape = 2.4,
                 scale = 4.7,
                 w = 0.5)

i <- incidence(ebola_sim$linelist$date_of_onset)
plot(i)


## projections after the first 100 days, over 60 days, fixed R to 2.1

set.seed(1)
proj_1 <- project(x = i[1:100], R = 2.1, si = si, n_days = 60)
plot(proj_1)

## add projections to incidence plot
plot(i[1:160]) %>% add_projections(proj_1)


## projections after the first 100 days, over 60 days,
## using a sample of R

set.seed(1)
R <- rnorm(100, 1.8, 0.2)
hist(R, col = "grey", border = "white", main = "Distribution of R")
proj_2 <- project(x = i[1:100], R = R, si = si, n_days = 60)

## add projections to incidence plot
plot(i[1:160]) %>% add_projections(proj_2)


## same with R constant per simulation (more variability)

set.seed(1)
proj_3 <- project(x = i[1:100], R = R, si = si, n_days = 60,
                  R_fix_within = TRUE)

## add projections to incidence plot
plot(i[1:160]) %>% add_projections(proj_3)


## time-varying R, 2 periods, R is 2.1 then 0.5
set.seed(1)
proj_4 <- project(i,
                  R = c(2.1, 0.5),
                  si = si,
                  n_days = 60,
                  time_change = 40,
                  n_sim = 100)
plot(proj_4)


## time-varying R, 2 periods, separate distributions of R for each period
set.seed(1)
R_period_1 <- runif(100, min = 1.1, max = 3)
R_period_2 <- runif(100, min = 0.6, max = .9)

proj_5 <- project(i,
                  R = list(R_period_1, R_period_2),
                  si = si,
                  n_days = 60,
                  time_change = 20,
                  n_sim = 100)
plot(proj_5)

}

---

Summary for projections objects

Description

This method summarises predicted epidemic trajectories contained in a projections object by days, deriving the mean, standard deviation, and user-specified quantiles for each day.

Usage

## S3 method for class 'projections'
summary(
  object,
  quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975),
  mean = TRUE,
  sd = TRUE,
  min = TRUE,
  max = TRUE,
  ...
)

Arguments

object	A projections object to summarise
quantiles	A numeric vector indicating which quantiles should be computed; ignored if FALSE or of length 0
mean	a logical indicating of the mean should be computed
sd	a logical indicating of the standard deviation should be computed
min	a logical indicating of the minimum should be computed
max	a logical indicating of the maximum should be computed
...	only preesnt for compatibility with the generic if (require(incidence)) i <- incidence::incidence(as.Date('2020-01-23')) si <- c(0.2, 0.5, 0.2, 0.1) R0 <- 2 p <- project(x = i, si = si, R = R0, n_sim = 2, R_fix_within = TRUE, n_days = 10, model = "poisson" ) summary(p)

Author(s)

Thibaut Jombart

---