Welcome to the projections package!

This package uses data on daily incidence, the serial interval (time between onsets of infectors and infectees) and the reproduction number to simulate plausible epidemic trajectories and project future incidence. It relies on a branching process where daily incidence follows a Poisson or a Negative Binomial distribution governed by a force of infection computed (FOI) as:

$\lambda_t = \sum_{s = 1}^{t - 1} R_s y_s w(t - s)$

where:

is the probability mass function (PMF) of the serial interval
is the incidence (by date of onset) at time
is the effective reproduction number (average number of secondary cases by infected case) at time

Alternatively, the FOI can use the instantaneous reproduction number (Cori et al. 2013) instead of the effective reproduction number:

$\lambda_t = R_t \sum_{s = 1}^{t - 1} y_s w(t - s)$

Installing the package

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

install.packages("projections")

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

devtools::install_github("reconhub/projections")

Note that this requires the package devtools installed.

What does it do?

The main features of the package include:

project: a function generating projections from an existing incidence object, a serial interval distribution, and a set of plausible reproduction numbers (R); returns a projections object; two models are implemented: Poisson, and Negative Binomial; both models can either use a constant distribution for R, or use time-varying distributions
plot/print: plotting and printing methods for projections objects.
summary: summary method for projections objects, deriving a range of summary statistics for each day of forecast
get_dates: accessors for projections objects
cumulate: cumulate predicted incidence over time
as.data.frame: conversion from projections objects to data.frame
[: subsetting operator for projections objects, permiting to specify which dates and simulations to retain; uses a syntax similar to matrices, i.e. x[i, j], where x is the projections object, i a subset of dates, and j a subset of simulations
subset: subset a projections object by specifying a time window
build_projections: build a projections object from an input matrix and optional dates
merge_projections: merge several projections objects into one, putting them on a common time frame; useful to combine runs from different simulations and/or perform model averaging
merge_add_projections: puts several projections on the same time frame, and adds incidences to form a new object; objects having less simulations are recycled to match the largest number of simulations; useful to combine cases simulated from different processes

Resources

Worked example

Simulated Ebola outbreak

In this example, we use the simulated Ebola outbreak ebola_sim_clean from the outbreaks package to illustrate the package’s functionalities. We will:

first calculate case incidence
create a serial interval distribution from known mean / standard deviations (e.g. taken from the literature)
project case incidence
summarise the resulting projections
export results as data.frame for further processing, e.g. making custom plots using ggplot2
showcase advanced handling of projections objects (merging/adding projections)

Computing case incidence

Here we use incidence (from the similarly named package) to calculate daily counts of new cases by date of onset:

library(outbreaks)
library(incidence)
library(ggplot2)

linelist <- ebola_sim_clean$linelist
i <- incidence(linelist$date_of_onset)
plot(i) +
  theme_bw() # full outbreak

plot(i[1:160]) +
  theme_bw() # first 160 days

Creating a serial interval

We create a serial interval distribution using distcrete (from the similarly named package); we use published values of the Serial Interval for Ebola with a mean of 15.3 days and a standard deviation of 9.3 days), to build a discretised Gamma distribution. Because the Gamma implementation in R uses shape and scale as parameters, we first need to convert the mean and coefficient of variation into shape and scale, using gamma_mucv2shapescale from the epitrix package.

library(distcrete)
library(epitrix)
mu <- 15.3
sigma <- 9.3
cv <- sigma / mu
params <- gamma_mucv2shapescale(mu, cv)
params
#> $shape
#> [1] 2.706556
#> 
#> $scale
#> [1] 5.652941

si <- distcrete("gamma", shape = params$shape,
                scale = params$scale,
                interval = 1, w = 0.5)
si
#> A discrete distribution
#>   name: gamma
#>   parameters:
#>     shape: 2.70655567117586
#>     scale: 5.65294117647059

si_df <- data.frame(t = 1:50,
                    p = si$d(1:50))
ggplot(si_df, aes(x = t, y = p)) +
  theme_bw() +
  geom_col() +
  labs(title = "Serial interval",
       x = "Days after onset",
       y = "Probability")

Projecting future incidence

We forecast future incidence based on the incidence data and the serial interval, assuming a reproduction number of 1.5; for the sake of illustration, we start use the first 100 days of data to determine the force of infection, and derive forecasts for 30 days (in practice, forecasts can only be reliable for short term predictions, typically 3 weeks maximum):

library(projections)
set.seed(1)
pred_1 <- project(i[1:100], R = 1.5, si = si, n_days = 30, n_sim = 1000)
pred_1
#> 
#> /// Incidence projections //
#> 
#>   // class: projections, matrix, array
#>   // 30 dates (rows); 1,000 simulations (columns)
#> 
#>  // first rows/columns:
#>            [,1] [,2] [,3] [,4] [,5] [,6]
#> 2014-07-16    4    7    7    7    7   12
#> 2014-07-17    9    4    8   10    4   10
#> 2014-07-18    8    8    4    3   10    4
#> 2014-07-19    8    8   11    6   10    8
#>  .
#>  .
#>  .
#> 
#>  // dates:
#>  [1] "2014-07-16" "2014-07-17" "2014-07-18" "2014-07-19" "2014-07-20"
#>  [6] "2014-07-21" "2014-07-22" "2014-07-23" "2014-07-24" "2014-07-25"
#> [11] "2014-07-26" "2014-07-27" "2014-07-28" "2014-07-29" "2014-07-30"
#> [16] "2014-07-31" "2014-08-01" "2014-08-02" "2014-08-03" "2014-08-04"
#> [21] "2014-08-05" "2014-08-06" "2014-08-07" "2014-08-08" "2014-08-09"
#> [26] "2014-08-10" "2014-08-11" "2014-08-12" "2014-08-13" "2014-08-14"
plot(pred_1) +
  theme_bw() # default plot

pred_1_cum <- cumulate(pred_1) # cumulative predictions
plot(pred_1_cum) +
  theme_bw() # plot cumulative predictions

Forecasts stored in a projections object can also be added to an incidence plot using add_projections, which admits the same options as the plot method. This function is best used with a pipe %>%:

library(magrittr)
plot(i[20:160], alpha = 0.5) %>% 
  add_projections(pred_1, boxplots = FALSE, quantiles = c(0.025, 0.5)) +
  theme_bw()
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Summarising forecasts

The summary function will summarise simulations using several statistics for each day of the forecast, allowing the user to switch off some of the summaries, and specify quantiles. Several options are illustrated below, but more information will be found at ?summary.projections:

## default summary
head(summary(pred_1))
#>        dates  mean       sd min max quantiles.2.5% quantiles.25% quantiles.50%
#> 1 2014-07-16 6.705 2.580821   1  16              2             5           6.5
#> 2 2014-07-17 6.975 2.716202   1  17              2             5           7.0
#> 3 2014-07-18 7.313 2.670236   0  17              2             5           7.0
#> 4 2014-07-19 7.702 2.712666   0  18              3             6           8.0
#> 5 2014-07-20 7.827 2.726996   0  17              3             6           8.0
#> 6 2014-07-21 8.221 2.830224   1  18              3             6           8.0
#>   quantiles.75% quantiles.97.5%
#> 1             8              12
#> 2             9              13
#> 3             9              13
#> 4             9              13
#> 5            10              13
#> 6            10              14
tail(summary(pred_1))
#>         dates   mean       sd min max quantiles.2.5% quantiles.25%
#> 25 2014-08-09 14.020 3.897537   3  28              7         11.00
#> 26 2014-08-10 14.525 4.106853   3  28              7         12.00
#> 27 2014-08-11 14.900 4.279055   4  32              7         12.00
#> 28 2014-08-12 15.693 4.546584   4  33              8         12.75
#> 29 2014-08-13 15.821 4.408673   5  36              8         13.00
#> 30 2014-08-14 16.339 4.501593   2  33              8         13.00
#>    quantiles.50% quantiles.75% quantiles.97.5%
#> 25            14            16              22
#> 26            14            17              23
#> 27            15            18              24
#> 28            15            19              25
#> 29            15            19              25
#> 30            16            19              26

## keeping only mean, min and max
head(summary(pred_1, sd = FALSE, quantiles = FALSE))
#>        dates  mean min max
#> 1 2014-07-16 6.705   1  16
#> 2 2014-07-17 6.975   1  17
#> 3 2014-07-18 7.313   0  17
#> 4 2014-07-19 7.702   0  18
#> 5 2014-07-20 7.827   0  17
#> 6 2014-07-21 8.221   1  18

## using 10%, 50% and 90% quantiles
head(summary(pred_1, quantiles = c(0.1, 0.5, 0.9)))
#>        dates  mean       sd min max quantiles.10% quantiles.50% quantiles.90%
#> 1 2014-07-16 6.705 2.580821   1  16             4           6.5            10
#> 2 2014-07-17 6.975 2.716202   1  17             4           7.0            11
#> 3 2014-07-18 7.313 2.670236   0  17             4           7.0            11
#> 4 2014-07-19 7.702 2.712666   0  18             4           8.0            11
#> 5 2014-07-20 7.827 2.726996   0  17             5           8.0            12
#> 6 2014-07-21 8.221 2.830224   1  18             5           8.0            12

To derive your own summary for each day, you can use apply with custom functions; for instance, to calculate the geometric mean for each day:

## function to calculate geometric mean
geo_mean = function(x, na.rm = TRUE){
  exp(sum(log(x[x > 0]), na.rm = na.rm) / length(x))
}

## raw output
apply(pred_1, 1, geo_mean)
#> 2014-07-16 2014-07-17 2014-07-18 2014-07-19 2014-07-20 2014-07-21 2014-07-22 
#>   6.163843   6.392041   6.759438   7.172451   7.295941   7.674789   7.671474 
#> 2014-07-23 2014-07-24 2014-07-25 2014-07-26 2014-07-27 2014-07-28 2014-07-29 
#>   8.026384   8.522398   8.618595   8.849186   9.182769   9.602397   9.960910 
#> 2014-07-30 2014-07-31 2014-08-01 2014-08-02 2014-08-03 2014-08-04 2014-08-05 
#>  10.244793  10.343175  10.746244  10.945269  11.274515  11.762397  11.945831 
#> 2014-08-06 2014-08-07 2014-08-08 2014-08-09 2014-08-10 2014-08-11 2014-08-12 
#>  12.588968  12.623958  12.924687  13.449257  13.900736  14.261928  15.006552 
#> 2014-08-13 2014-08-14 
#>  15.194230  15.679589

## with some formatting
temp <- apply(pred_1, 1, geo_mean)
data.frame(date = get_dates(pred_1),
           geometric_mean = apply(pred_1, 1, geo_mean),
           row.names = NULL)
#>          date geometric_mean
#> 1  2014-07-16       6.163843
#> 2  2014-07-17       6.392041
#> 3  2014-07-18       6.759438
#> 4  2014-07-19       7.172451
#> 5  2014-07-20       7.295941
#> 6  2014-07-21       7.674789
#> 7  2014-07-22       7.671474
#> 8  2014-07-23       8.026384
#> 9  2014-07-24       8.522398
#> 10 2014-07-25       8.618595
#> 11 2014-07-26       8.849186
#> 12 2014-07-27       9.182769
#> 13 2014-07-28       9.602397
#> 14 2014-07-29       9.960910
#> 15 2014-07-30      10.244793
#> 16 2014-07-31      10.343175
#> 17 2014-08-01      10.746244
#> 18 2014-08-02      10.945269
#> 19 2014-08-03      11.274515
#> 20 2014-08-04      11.762397
#> 21 2014-08-05      11.945831
#> 22 2014-08-06      12.588968
#> 23 2014-08-07      12.623958
#> 24 2014-08-08      12.924687
#> 25 2014-08-09      13.449257
#> 26 2014-08-10      13.900736
#> 27 2014-08-11      14.261928
#> 28 2014-08-12      15.006552
#> 29 2014-08-13      15.194230
#> 30 2014-08-14      15.679589

Exporting results

The function as.data.frame can be handy for further processing of the forecast. The argument long in particular will be handy for further processing using dplyr or ggplot2, as it stores the ‘simulation’ as a 3rd columns, which can be used for grouping and/or aesthetics.

Here is an example with ggplot2 to produce an alternative plot:

df <- as.data.frame(pred_1, long = TRUE)
head(df)
#>         date incidence sim
#> 1 2014-07-16         4   1
#> 2 2014-07-17         9   1
#> 3 2014-07-18         8   1
#> 4 2014-07-19         8   1
#> 5 2014-07-20         5   1
#> 6 2014-07-21         8   1
ggplot(df, aes(x = date, y = incidence)) +
  theme_bw() +
  geom_jitter(alpha = 0.05, size = 4) +
  geom_smooth()
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Advanced handling

projections objects can also be combined in two ways:

merge different sets of simulations, using merge_projections; this can be useful e.g. for model averaging, where different models produce separate sets of forecasts which need combining
add forecasts from different simulation sets, using +, or merge_add_projections; this can be useful for simulating cases from different, complementary processes

We illustrate case 1, where we produce a second set of forecasts pred_2 using a different serial interval distribution, which we combine to pred_1. For the sake of example, we use a made-up serial interval which is much shorter than the one used in pred_1, with an average of 4 days, and a standard deviation of 2 days.

mu <- 4
sigma <- 2
cv <- sigma / mu
params <- gamma_mucv2shapescale(mu, cv)
params
#> $shape
#> [1] 4
#> 
#> $scale
#> [1] 1

si_short <- distcrete("gamma", shape = params$shape,
                      scale = params$scale,
                      interval = 1, w = 0.5)
si_short
#> A discrete distribution
#>   name: gamma
#>   parameters:
#>     shape: 4
#>     scale: 1

si_short_df <- data.frame(t = 1:20,
                          p = si_short$d(1:20))
ggplot(si_short_df, aes(x = t, y = p)) +
  theme_bw() +
  geom_col() +
  labs(title = "Other serial interval",
       x = "Days after onset",
       y = "Probability")

We now use this serial interval to produce a second set of forecasts. We compare them to the initial one, and the combined forecasts:

set.seed(1)
pred_2 <- project(i[1:100], R = 1.5, si = si_short, n_days = 30, n_sim = 1000)
pred_2 # 1000 simulations
#> 
#> /// Incidence projections //
#> 
#>   // class: projections, matrix, array
#>   // 30 dates (rows); 1,000 simulations (columns)
#> 
#>  // first rows/columns:
#>            [,1] [,2] [,3] [,4] [,5] [,6]
#> 2014-07-16    6   10   11   10   10   16
#> 2014-07-17   13   12    7   12   10   12
#> 2014-07-18   15   12   12   12   14   15
#> 2014-07-19   13   11   20    9   15   16
#>  .
#>  .
#>  .
#> 
#>  // dates:
#>  [1] "2014-07-16" "2014-07-17" "2014-07-18" "2014-07-19" "2014-07-20"
#>  [6] "2014-07-21" "2014-07-22" "2014-07-23" "2014-07-24" "2014-07-25"
#> [11] "2014-07-26" "2014-07-27" "2014-07-28" "2014-07-29" "2014-07-30"
#> [16] "2014-07-31" "2014-08-01" "2014-08-02" "2014-08-03" "2014-08-04"
#> [21] "2014-08-05" "2014-08-06" "2014-08-07" "2014-08-08" "2014-08-09"
#> [26] "2014-08-10" "2014-08-11" "2014-08-12" "2014-08-13" "2014-08-14"
plot(pred_2) +
  theme_bw() # default plot

## combine the objects; note that any number of objects can be combined
pred_combined <- merge_projections(list(pred_1, pred_2))
pred_combined # 2000 simulations
#> 
#> /// Incidence projections //
#> 
#>   // class: projections, matrix, array
#>   // 30 dates (rows); 2,000 simulations (columns)
#> 
#>  // first rows/columns:
#>            sim_1 sim_2 sim_3 sim_4 sim_5 sim_6
#> 2014-07-16     4     7     7     7     7    12
#> 2014-07-17     9     4     8    10     4    10
#> 2014-07-18     8     8     4     3    10     4
#> 2014-07-19     8     8    11     6    10     8
#>  .
#>  .
#>  .
#> 
#>  // dates:
#>  [1] "2014-07-16" "2014-07-17" "2014-07-18" "2014-07-19" "2014-07-20"
#>  [6] "2014-07-21" "2014-07-22" "2014-07-23" "2014-07-24" "2014-07-25"
#> [11] "2014-07-26" "2014-07-27" "2014-07-28" "2014-07-29" "2014-07-30"
#> [16] "2014-07-31" "2014-08-01" "2014-08-02" "2014-08-03" "2014-08-04"
#> [21] "2014-08-05" "2014-08-06" "2014-08-07" "2014-08-08" "2014-08-09"
#> [26] "2014-08-10" "2014-08-11" "2014-08-12" "2014-08-13" "2014-08-14"

list_plots <- list(
  plot(pred_1) + theme_bw() + labs(title = "Forecast with initial SI"),
  plot(pred_2,) + theme_bw() + labs(title = "Forecast with short SI"),
  plot(pred_combined) + theme_bw() + labs(title = "Combined forecasts")
)

library(cowplot)
plot_grid(plotlist = list_plots,
          ncol = 1)

To illustrate case 2 (not only merging, but adding projections objects), we artificially split the dataset by hospitals, derive separate forecasts for each, and add forecasts of two hospitals in the example below.

## calculate incidence by hospital
i_hosp <- incidence(linelist$date_of_onset, groups = linelist$hospital)
plot(i_hosp) +
  theme_bw() +
  theme(legend.position = "bottom")

## derive predictions for each hospital
n_groups <- ncol(get_counts(i_hosp))

pred_hosp <- lapply(
  seq_len(n_groups),
  function(j)
    project(i_hosp[1:100, j],
            R = 1.5,
            si = si,
            n_days = 60,
            n_sim = 500))
names(pred_hosp) <- colnames(get_counts(i_hosp))


## we combine forecasts for Connaught and Rokupa hospitals
pred_connaught_rokupa <- pred_hosp$`Connaught Hospital` + pred_hosp$`Rokupa Hospital`


list_plots <- list(
  plot(pred_hosp$`Connaught Hospital`) +
    theme_bw() +
    ylim(c(0, 30)) +
    labs(title = "Connaught hospital"),
  plot(pred_hosp$`Rokupa Hospital`) +
    theme_bw() +
    ylim(c(0, 30)) +
    labs(title = "Rokupa hospital"),
  plot(pred_connaught_rokupa) +
    theme_bw() +
    ylim(c(0, 30)) +
    labs(title = "Connaught + Rokupa")
)

plot_grid(plotlist = list_plots,
          ncol = 1)

Note that to add more than 2 projections objects, one can use merge_add_projections. Also note that the + operator can also be used with a numeric, e.g. for adding an offset. For instance:

plot(pred_connaught_rokupa + 1000) +
  theme_bw() +
  labs(title = "Connaught + Rokupa with 1000 added cases")

Vignettes

projections does not currently have a dedicated vignette. This README will eventually be converted into separate vignettes.

Websites

A dedicated website can be found at: http://www.repidemicsconsortium.org/projections.

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/

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.

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README.md

README.md

Welcome to the projections package!

Installing the package

What does it do?

Resources

Worked example

Simulated Ebola outbreak

Computing case incidence

Creating a serial interval

Projecting future incidence

Summarising forecasts

Exporting results

Advanced handling

Vignettes

Websites

Getting help online

Files

README.md

Latest commit

History

README.md

File metadata and controls

Welcome to the projections package!

Installing the package

What does it do?

Resources

Worked example

Simulated Ebola outbreak

Computing case incidence

Creating a serial interval

Projecting future incidence

Summarising forecasts

Exporting results

Advanced handling

Vignettes

Websites

Getting help online