| Title: | Effective Reproduction Number Estimation |
| Version: | 2.1.2 |
| Maintainer: | David Champredon <david.champredon@canada.ca> |
| Description: | Estimate the effective reproduction number from wastewater and clinical data sources. |
| License: | MIT + file LICENSE |
| Imports: | assertthat, coda, dplyr, EpiEstim, ggplot2, lubridate, patchwork, rjags, runjags, stats, stringr, tibble, tidyr, zoo |
| Suggests: | knitr, rmarkdown, bookdown, purrr, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| Depends: | R (≥ 4.1.0) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2025-01-16 17:31:38 UTC; user5023114 |
| Author: | David Champredon 
[aut, cre],
Warsame Yusuf
[aut],
Irena Papst [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-01-16 18:20:02 UTC |
Infer daily counts from aggregates
Description
Infer daily counts from aggregates
Usage
agg_to_daily(cl.data, dist.gi, prm.daily, silent = FALSE)
Arguments
cl.data |
Data frame. Must have variables:
|
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
prm.daily |
List. Parameters for daily report inference via MCMC. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Value
A list containing a data frame with individual realizations of daily reported cases and the JAGS object.
Examples
# Importing data attached to the `ern` package
# and selecting the Omicron wave in Ontario, Canada.
# This is *weekly* incidence.
data(cl.data)
data = cl.data[cl.data$pt == 'on' &
cl.data$date > as.Date('2021-11-30') &
cl.data$date < as.Date('2021-12-31'),]
head(data)
dist.gi = ern::def_dist(
dist = "gamma",
mean = 6.84,
mean_sd = 0.7486,
shape = 2.39,
shape_sd = 0.3573,
max = 15
)
a = agg_to_daily(
cl.data = data,
dist.gi = dist.gi,
prm.daily = list(
method = "renewal",
popsize = 14e6,
# MCMC parameters.
# small values for computation speed for this example.
# Increase for better accuracy
burn = 100,
iter = 100,
chains = 2,
# - - - - -
prior_R0_shape = 2,
prior_R0_rate = 0.6,
prior_alpha_shape = 1,
prior_alpha_rate = 1
))
# This is a Bayesian inference, so we
# have a posterior distribution of
# daily incidences. Here we just plot
# one single draw:
df = a$df
df1 = df[df$id==1,]
plot(x = df1$t, y = df1$value, typ = 'o',
xlab = 'days', ylab = 'daily incidence',
main = 'Posterior daily incidence infered from weekly incidence')
# Extract of the parameters values from the first chain
a$jags.object[[1]][1:9,1:9]
Attach start date from first observation for aggregated data from time (day number)
Description
Attach start date from first observation for aggregated data from time (day number)
Usage
attach_startdate_agg(x)
Arguments
x |
dataframe. only has columns |
Attach internal time index column
Description
Attach internal time index column
Usage
attach_t(df)
Arguments
df |
Data frame. Must have column |
Value
Tibble.
Attach time index (number of days) column. Exclude first day since we don't necessarily know over which period of time that data was aggregated
Description
Attach time index (number of days) column. Exclude first day since we don't necessarily know over which period of time that data was aggregated
Usage
attach_t_agg(cl.data, prm.daily = NULL, silent = FALSE)
Arguments
cl.data |
Data frame. Must have variables:
|
prm.daily |
List. Parameters for daily report inference via MCMC. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Value
Data frame
Check the format of input clinical data
Description
Check the format of input clinical data
Usage
check_cl.input_format(cl.data, silent = FALSE)
Arguments
cl.data |
Data frame. Must have variables:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Check if input data is already daily
Description
Check if input data is already daily
Usage
check_df.input_daily(df.input)
Arguments
df.input |
Data frame. Must have variables:
|
Value
Logical. Indicates whether input data is already daily.
Check distributions
Description
Check distributions
Usage
check_dist(x)
Arguments
x |
family of distributions to be checked, as generated by |
Check that deconvolution inputs are compatible
Description
Check that deconvolution inputs are compatible
Usage
check_for_deconv(obs, dist)
Arguments
obs |
Numeric. Vector with observed signal (e.g., case reports) |
dist |
Numeric. Vector of discretized distribution used as the deconvolution kernel (e.g., reporting delay distribution) |
Check parameters for Rt calculation
Description
Check parameters for Rt calculation
Usage
check_prm.R(x, silent = FALSE)
Arguments
x |
List. Parameters for Rt calculation. |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Check parameters for daily data inference
Description
Check parameters for daily data inference
Usage
check_prm.daily(x)
Arguments
x |
List. Parameters for daily data inference. |
Check parameters for daily data inference check
Description
Check parameters for daily data inference check
Usage
check_prm.daily.check(x)
Arguments
x |
List. Parameters for daily data inference check. |
Check parameters for smoothing
Description
Check parameters for smoothing
Usage
check_prm.smooth(x)
Arguments
x |
List that specifies the type of smoothing and the parameters associated with the smoothing method. |
Sample of aggregated clinical reports
Description
A subset of COVID-19 weekly reports in the Government of Canada Health Infobase. See https://health-infobase.canada.ca/covid-19/
Usage
cl.data
Format
cl.data
A data frame with 96 rows and 3 columns:
-
pt: standard two-character abbreviation (lowercase) of the province name (based on Statistics Canada 2021 census abbreviations) -
date: report date -
value: count of reported cases for the previous week
Filter indicating a specific province to extract a sample dataset for use with estimate_R_cl(), e.g.
estimate_R_cl(cl.data = dplyr::filter(cl.data, pt == 'bc'), ...)
Correct underreporting by scaling up
Description
Correct underreporting by scaling up
Usage
correct_underreporting(reports.daily, reporting.fraction)
Arguments
reports.daily |
dataframe of daily reported cases. must at least include |
reporting.fraction |
numeric. proportion of incidence that is reported. |
Wrapper for deconvolution with a given distribution
Description
Wrapper for deconvolution with a given distribution
Usage
deconv(counts, dist, max.iter = 10, silent = FALSE)
Arguments
counts |
Numeric. Vector of daily counts. |
dist |
Numeric. Vector of discrete distributions (daily, truncated) with which we're deconvoluting counts, e.g., produced by |
max.iter |
Numeric. Maximum number of Richardson-Lucy iterations. |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Deconvoluting Wastewater Data to Incidence
Description
Function estimates incidence from smoothed data
Usage
deconv_ww_inc(d, fec, scaling.factor, silent, RL.max.iter)
Arguments
d |
Data frame. Wastewater dataframe. Must include at least |
fec |
List. Parameters for a single fecal shedding distribution, as generated by |
scaling.factor |
Numeric. Scaling from wastewater concentration to prevalence. This value may be assumed or independently calibrated to data. |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
Value
Data frame with deconvoluted incidence
Richardson-Lucy Deconvolution.
Description
Richardson-Lucy Deconvolution.
Usage
deconvolution_RL(
observed,
times,
p_delay,
max_iter = 50,
out_col_name = "RL_result",
right_censor = TRUE,
verbose = FALSE
)
Arguments
observed |
A vector of the number of observed cases, deaths, hospitalizations, etc. per day |
times |
A numeric vector of times corresponding to the entries in observed. Must be the same length as observed. |
p_delay |
A numeric vector whose entries give the probability that the delay from infection to observation is exactly 0, 1, ... N days. p_delay must sum to 1 and to facilitate vectorization should be the same length as observed, and times (the last several entries will most likely be 0s). |
max_iter |
Maximum number of times to iterate. Following Goldstein et al., the algorithm continues to run until the normalized chi2 statistic comparing the observed and expected number of deaths per day falls below 1, or until the maximum number of iterations is reached. |
out_col_name |
String giving the name of the column in which to output the imputed times of infection |
right_censor |
If |
verbose |
Logical. Verbose mode. |
Value
A data frame with columns time and out_col_name (which gives the imputed number of infections per timestep)
Define a family of distributions.
Description
Define a family of distributions.
Usage
def_dist(dist, ...)
Arguments
dist |
distribution type. Distributions currently supported are:
|
... |
a series of distribution parameters. Included should be the following:
|
Value
List with components specified in the parameters.
Examples
d = def_dist(
dist = "gamma",
mean = 3.49,
mean_sd = 0.1477,
shape = 8.5,
shape_sd = 1.8945,
max = 8
)
print(d)
Draw from gamma for a parameter specified in a distribution family list.
Description
Draw from gamma for a parameter specified in a distribution family list.
Usage
draw_from_gamma(par, dist)
Arguments
par |
Character. Name of the parameter to sample. |
dist |
List. Distribution definition, as output by |
Value
Numeric. The sampled parameter value.
Estimate the effective reproduction from clinical report data
Description
Estimate the effective reproduction from clinical report data
Usage
estimate_R_cl(
cl.data,
dist.repdelay,
dist.repfrac,
dist.incub,
dist.gi,
prm.daily = list(method = "linear", popsize = NULL, burn = 500, iter = 2000, chains =
3, prior_R0_shape = 2, prior_R0_rate = 0.6, prior_alpha_shape = 1, prior_alpha_rate =
1, first.agg.period = NULL),
prm.daily.check = list(agg.reldiff.tol = 10),
prm.smooth = list(method = "rollmean", align = "right", window = 7),
prm.R = list(iter = 10, CI = 0.95, window = 7, config.EpiEstim = NULL),
RL.max.iter = 10,
silent = FALSE
)
Arguments
cl.data |
Data frame. Must have variables:
|
dist.repdelay |
List. Parameters for the reporting delay distribution in the same format as returned by |
dist.repfrac |
List. Parameters for the reporting fraction distribution in the same format as returned by |
dist.incub |
List. Parameters for the incubation period distribution in the same format as returned by |
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
prm.daily |
List. Parameters for daily report inference via MCMC. Elements include:
|
prm.daily.check |
List. Parameters for checking aggregated to daily report inference. Elements include:
Set this entire argument to |
prm.smooth |
List. list of smoothing parameters. Parameters should be specified as followed:
Set this entire list to |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Value
List. Elements include:
-
cl.data: original aggregated reports signal -
cl.daily: reports as input for Rt calculation (inferred daily counts, smoothed) -
inferred.agg: inferred daily reports aggregated on the reporting schedule as input incl.data -
R: the effective R estimate (summary from ensemble)
See Also
plot_diagnostic_cl() estimate_R_ww()
Examples
# -- THIS EXAMPLE TAKES ABOUT 30 SECONDS TO RUN --
# Estimate Rt
## Not run:
# Load SARS-CoV-2 reported cases in Quebec
# during the Summer 2021
dat <- (ern::cl.data
|> dplyr::filter(
pt == "qc",
dplyr::between(date, as.Date("2021-06-01"), as.Date("2021-09-01"))
)
)
# distributions
dist.repdelay = ern::def_dist(
dist = 'gamma',
mean = 5,
mean_sd = 1,
sd = 1,
sd_sd = 0.1,
max = 10
)
dist.repfrac = ern::def_dist(
dist = "unif",
min = 0.1,
max = 0.3
)
dist.incub = ern::def_dist(
dist = "gamma",
mean = 3.49,
mean_sd = 0.1477,
shape = 8.5,
shape_sd = 1.8945,
max = 8
)
dist.gi = ern::def_dist(
dist = "gamma",
mean = 6,
mean_sd = 0.75,
shape = 2.4,
shape_sd = 0.3,
max = 10
)
# settings
prm.daily <- list(
method = "renewal",
popsize = 8.5e6, # Q3 (July 1) 2022 estimate for Quebec
burn = 500,
iter = 500,
chains = 2,
prior_R0_shape = 1.1, prior_R0_rate = 0.6,
prior_alpha_shape = 1, prior_alpha_rate = 1
)
prm.daily.check <- list(
agg.reldiff.tol = 10
)
prm.smooth <- list(
method = "rollmean",
align = "center",
window = 7
)
prm.R <- list(
iter = 20,
CI = 0.95,
window = 7,
config.EpiEstim = NULL
)
x <- estimate_R_cl(
dat,
dist.repdelay,
dist.repfrac,
dist.incub,
dist.gi,
prm.daily,
prm.daily.check,
prm.smooth,
prm.R
)
# Rt estimates
print(x$R)
## End(Not run)
Ensemble estimate of Rt
Description
Ensemble estimate of Rt
Usage
estimate_R_cl_rep(
cl.daily,
dist.repfrac,
dist.repdelay,
dist.incub,
dist.gi,
prm.R,
silent = FALSE,
RL.max.iter = 10
)
Arguments
cl.daily |
Dataframe of inferred daily incidence. |
dist.repfrac |
List. Parameters for the reporting fraction distribution in the same format as returned by |
dist.repdelay |
List. Parameters for the reporting delay distribution in the same format as returned by |
dist.incub |
List. Parameters for the incubation period distribution in the same format as returned by |
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
Value
A summary of individual Rt realizations with 95% confidence intervals
See Also
A single realization of the Rt estimate
Description
A single realization of the Rt estimate
Usage
estimate_R_cl_single(
cl.daily,
dist.repfrac,
dist.repdelay,
dist.incub,
dist.gi,
prm.R,
silent = FALSE,
RL.max.iter = 10
)
Arguments
cl.daily |
Dataframe of inferred daily incidence. |
dist.repfrac |
List. Parameters for the reporting fraction distribution in the same format as returned by |
dist.repdelay |
List. Parameters for the reporting delay distribution in the same format as returned by |
dist.incub |
List. Parameters for the incubation period distribution in the same format as returned by |
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
Estimate the effective reproduction from wastewater concentration data.
Description
Estimate the effective reproduction from wastewater concentration data.
Usage
estimate_R_ww(
ww.conc,
dist.fec,
dist.gi,
scaling.factor = 1,
prm.smooth = list(window = 14, align = "center", method = "loess", span = 0.2),
prm.R = list(iter = 10, CI = 0.95, window = 7, config.EpiEstim = NULL),
silent = FALSE,
RL.max.iter = 9
)
Arguments
ww.conc |
Data frame. Must have variables:
|
dist.fec |
List. Parameters for the fecal shedding distribution in the same format as returned by |
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
scaling.factor |
Numeric. Scaling from wastewater concentration to prevalence. This value may be assumed or independently calibrated to data. |
prm.smooth |
List. list of smoothing parameters. Parameters should be specified as followed:
Set this entire list to |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
Value
List. Elements include:
-
ww.conc: original wastewater signal -
ww.smooth: smoothed wastewater signal -
inc: inferred incidence -
R: the effective reproduction number estimate
See Also
plot_diagnostic_ww() estimate_R_cl()
Examples
# Load data of viral concentration in wastewater
data("ww.data")
# Run the estimation of Rt based on the wastewater data
x = estimate_R_ww(
ww.conc = ww.data,
dist.fec = ern::def_dist(
dist = "gamma",
mean = 12.90215,
mean_sd = 1.136829,
shape = 1.759937,
shape_sd = 0.2665988,
max = 33
),
dist.gi = ern::def_dist(
dist = "gamma",
mean = 6.84,
mean_sd = 0.7486,
shape = 2.39,
shape_sd = 0.3573,
max = 15
),
silent = TRUE
)
# Rt estimates
head(x$R)
# inferred daily incidence
head(x$inc)
Extract MCMC chains from a JAGS object
Description
Extract MCMC chains from a JAGS object
Usage
extract_mcmc_values(chain, jags.obj)
Arguments
chain |
Integer. Chain number. |
jags.obj |
JAGS object as returned by |
Value
A dataframe of the chain values for selected parameters.
Fit JAGS model to aggregated data
Description
Fit JAGS model to aggregated data
Usage
fit_jags_aggreg(obs.times, Y, g, N, n.days = NULL, prm.daily, silent = FALSE)
Arguments
obs.times |
Numeric. Vector of observation times. |
Y |
Numeric. Vector of aggregated counts. |
g |
Numeric. Vector of discretized generation interval density. |
N |
Numeric. Scalar population size. |
n.days |
Numeric. Total number of days. if |
prm.daily |
List. Parameters for daily report inference via MCMC. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Get a discretized, truncated version of a distribution
Description
Get a discretized, truncated version of a distribution
Usage
get_discrete_dist(params)
Arguments
params |
distribution params (output of |
Value
Numeric. Vector with discretized density.
Examples
# Define distributions
fec = ern::def_dist(
dist = "gamma",
mean = 12.90215,
mean_sd = 1.136829,
shape = 1.759937,
shape_sd = 0.2665988,
max = 33
)
gi = ern::def_dist(
dist = "gamma",
mean = 6.84,
mean_sd = 0.7486,
shape = 2.39,
shape_sd = 0.3573,
max = 15
)
# Get their (discretized) densities
d.fec = get_discrete_dist(fec)
d.gi = get_discrete_dist(gi)
print(d.fec)
print(d.gi)
Retrieve realizations for aggregated to daily inference
Description
Retrieve realizations for aggregated to daily inference
Usage
get_realizations(fit.reports.daily, reports)
Arguments
fit.reports.daily |
Data frame. Realizations from daily report inference. Must at least have |
reports |
Data frame. Original aggregated reports. Must at least have |
Value
Data frame
See Also
Get dates for which to use (trust) inferred daily reports
Description
Get dates for which to use (trust) inferred daily reports
Usage
get_use_dates(cl.daily, cl.data, prm.daily.check, dates.only = TRUE)
Arguments
cl.daily |
Data frame. Output of |
cl.data |
Data frame. Must have variables:
|
prm.daily.check |
List. Parameters for checking aggregated to daily report inference. Elements include:
Set this entire argument to |
dates.only |
Logical. Return use dates only or all columns of |
Value
Data frame or vector, depending on dates.only
Helper function. Converts wastewater to Rt after sampling one fecal shedding and one generation interval distribution.
Description
Helper function. Converts wastewater to Rt after sampling one fecal shedding and one generation interval distribution.
Usage
inc2R_one_iter(
i,
dist.fec,
dist.gi,
ww.conc,
scaling.factor,
prm.R,
silent,
RL.max.iter
)
Arguments
i |
Numeric. Iteration index. (not used but required when using
|
dist.fec |
List. Parameters for the fecal shedding distribution in the same format as returned by |
dist.gi |
List. Parameters for the generation interval distribution in the same format as returned by |
ww.conc |
Data frame. Must have variables:
|
scaling.factor |
Numeric. Scaling from wastewater concentration to prevalence. This value may be assumed or independently calibrated to data. |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
RL.max.iter |
Integer. Maximum of iterations for the Richardson-Lucy deconvolution algorithm. |
Value
List. Elements include inc (incidence) and rt
(reproduction number)
Estimate Rt using EpiEstim
Description
Estimate Rt using EpiEstim
Usage
incidence_to_R(incidence, generation.interval, prm.R)
Arguments
incidence |
Data frame. Estimated incidence. Must include at least
|
generation.interval |
List. Parameters for a single generation interval distribution, as generated by |
prm.R |
List. Settings for the ensemble when calculating Rt. Elements include:
|
See Also
Daily incidence from linear interpolation
Description
Daily incidence from linear interpolation
Usage
linear_int_daily(cl.data)
Arguments
cl.data |
Aggregated incidence. |
Value
A dataframe of daily incidence
Replace NAs with 0s in vector
Description
Replace NAs with 0s in vector
Usage
na_to_0(vec)
Arguments
vec |
Vector. |
Diagnostic plot for R estimation from clinical report data
Description
Diagnostic plot for R estimation from clinical report data
Usage
plot_diagnostic_cl(r.estim, caption = NULL, wrap.plots = TRUE)
Arguments
r.estim |
List. Output of |
caption |
String. Caption to be inserted in the plot.
Default is |
wrap.plots |
Logical. Wrap the plots together into a single ggplot object?
If |
Value
Plots of the clinical data used, the inferred daily incidence and
Rt estimates. If wrap.plots = TRUE (the default) will return
wrapped plots (with x-axis aligned to facilitate the comaprison)
in a single object,
else will return a list of separate ggplot objects.
A ggplot object (or a list of ggplot objects
if wrap.plots = FALSE).
See Also
Examples
# -- THIS EXAMPLE TAKES ABOUT 30 SECONDS TO RUN --
# Estimate Rt
## Not run:
# Load SARS-CoV-2 reported cases in Quebec
# during the Summer 2021
dat <- (ern::cl.data
|> dplyr::filter(
pt == "qc",
dplyr::between(date, as.Date("2021-06-01"), as.Date("2021-09-01"))
)
)
# distributions
dist.repdelay = ern::def_dist(
dist = 'gamma',
mean = 5,
mean_sd = 1,
sd = 1,
sd_sd = 0.1,
max = 10
)
dist.repfrac = ern::def_dist(
dist = "unif",
min = 0.1,
max = 0.3
)
dist.incub = ern::def_dist(
dist = "gamma",
mean = 3.49,
mean_sd = 0.1477,
shape = 8.5,
shape_sd = 1.8945,
max = 8
)
dist.gi = ern::def_dist(
dist = "gamma",
mean = 6,
mean_sd = 0.75,
shape = 2.4,
shape_sd = 0.3,
max = 10
)
# settings
prm.daily <- list(
method = "renewal",
popsize = 8.5e6, # Q3 (July 1) 2022 estimate for Quebec
burn = 500,
iter = 500,
chains = 2,
prior_R0_shape = 1.1, prior_R0_rate = 0.6,
prior_alpha_shape = 1, prior_alpha_rate = 1
)
prm.daily.check <- list(
agg.reldiff.tol = 10
)
prm.smooth <- list(
method = "rollmean",
align = "center",
window = 7
)
prm.R <- list(
iter = 20,
CI = 0.95,
window = 7,
config.EpiEstim = NULL
)
x <- estimate_R_cl(
dat,
dist.repdelay,
dist.repfrac,
dist.incub,
dist.gi,
prm.daily,
prm.daily.check,
prm.smooth,
prm.R
)
# Diagnostic plot for Rt estimates
# from clinical data
g = plot_diagnostic_cl(x)
plot(g)
g2 = plot_diagnostic_cl(x, caption = 'This is your caption', wrap.plots = FALSE)
plot(g2$clinical_data)
plot(g2$inferred_incidence)
plot(g2$Rt)
## End(Not run)
Diagnostic plot for R estimation from wastewater data
Description
Diagnostic plot for R estimation from wastewater data
Usage
plot_diagnostic_ww(r.estim, caption = NULL, wrap.plots = TRUE)
Arguments
r.estim |
List. Output of |
caption |
Character. Optional plot caption. |
wrap.plots |
Logical.
Wrap all diagnostic plots into one single ggplot object (default = |
Value
A ggplot object.
See Also
estimate_R_ww() plot_diagnostic_cl()
Examples
# Load data of viral concentration in wastewater
data("ww.data")
# Estimate Rt based on wastewater data
x = estimate_R_ww(
ww.conc = ww.data,
dist.fec = ern::def_dist(
dist = "gamma",
mean = 12.9,
mean_sd = 1.13,
shape = 1.75,
shape_sd = 0.26,
max = 33
),
dist.gi = ern::def_dist(
dist = "gamma",
mean = 6.84,
mean_sd = 0.74,
shape = 2.39,
shape_sd = 0.35,
max = 15
),
silent = TRUE
)
# Diagnostic plot
g = plot_diagnostic_ww(x)
plot(g)
g2 = plot_diagnostic_ww(x, wrap.plots = FALSE, caption = "This is your caption")
plot(g2$wastewater_data)
plot(g2$inferred_incidence)
plot(g2$Rt)
Plot a distribution
Description
Plot a distribution
Usage
plot_dist(d)
Arguments
d |
List that defines the distribution (as returned by |
Value
A ggplot object.
Examples
# Define a `ern` distribution:
gi = ern::def_dist(
dist = "gamma",
mean = 6.84,
mean_sd = 0.7486,
shape = 2.39,
shape_sd = 0.3573,
max = 15
)
# Plot can be customized like any `ggplot` object:
g = plot_dist(gi) + ggplot2::labs(subtitle = 'your subtitle')
plot(g)
Plot the Gelman Rubin statistic for all parameters.
Description
Plot the Gelman Rubin statistic for all parameters.
Usage
plot_gelman_rubin(jags.obj)
Arguments
jags.obj |
JAGS object as returned by |
Value
A ggplot plot.
Plot MCMC traces
Description
Plot MCMC traces
Usage
plot_traces(jags.obj)
Arguments
jags.obj |
JAGS object as returned by |
Value
A ggplot plot.
Infer incidence from reports via a series of deconvolutions
Description
Infer incidence from reports via a series of deconvolutions
Usage
reports_to_incidence(
reports.daily,
reporting.delay,
incubation.period,
max.iter = 9,
silent = FALSE
)
Arguments
reports.daily |
Data frame. Daily report counts. Includes at least |
reporting.delay |
List. Parameters for a single reporting delay distribution, as generated by |
incubation.period |
List. Parameters for a single incubation period distribution, as generated by |
max.iter |
Numeric. Maximum number of Richardson-Lucy iterations. |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Reshape JAGS fit object
Description
Reshape JAGS fit object
Usage
reshape_fit_jags(x)
Arguments
x |
Data frame. JAGS output from |
Sample parameters for a single distribution from a family of distributions, assuming parameters come from a Gamma distribution.
Description
Sample parameters for a single distribution from a family of distributions, assuming parameters come from a Gamma distribution.
Usage
sample_a_dist(dist)
Arguments
dist |
List. A list of distribution parameters, as defined by
|
Sample from a distribution (currently only implemented for a uniform distribution)
Description
Sample from a distribution (currently only implemented for a uniform distribution)
Usage
sample_from_dist(n, params)
Arguments
n |
number of samples to draw |
params |
distribution parameters |
Helper function - sample from EpiEstim's R posterior distribution
Description
Helper function - sample from EpiEstim's R posterior distribution
Usage
sample_post_ee(window, objee, n)
Arguments
window |
integer. Time slice used to estimate Rt. |
objee |
List. EpiEstim object as returned by EpiEstim::estimate_R |
n |
integer. Number of posterior samples drawn. |
Smooth realizations from estimating daily reports
Description
Smooth realizations from estimating daily reports
Usage
smooth_cl(cl.daily, prm.smooth)
Arguments
cl.daily |
Data frame. Output of |
prm.smooth |
List. list of smoothing parameters. Parameters should be specified as followed:
Set this entire list to |
Value
Data frame
Smooth wastewater data (using pre-processed wastewater)
Description
Smooth wastewater data (using pre-processed wastewater)
Usage
smooth_ww(ww.conc, prm.smooth, silent = FALSE)
Arguments
ww.conc |
Data frame. Must have variables:
|
prm.smooth |
List. list of smoothing parameters. Parameters should be specified as followed:
Set this entire list to |
silent |
Logical. Flag to suppress all output messages, warnings, and progress bars. |
Value
Data frame
Summarise observations by date for raw iterations from an ensemble
Description
Summarise observations by date for raw iterations from an ensemble
Usage
summarise_by_date_iters(df)
Arguments
df |
Data frame. Must have |
Summarise daily inferred reports based on original reporting schedule and calculate error
Description
Summarise daily inferred reports based on original reporting schedule and calculate error
Usage
summarise_report_counts(df, prm.daily.check)
Arguments
df |
Data frame. As output by |
prm.daily.check |
List. Parameters for checking aggregated to daily report inference. Elements include:
Set this entire argument to |
Value
Data frame
Helper function that summarises posterior samples of Rt from EpiEstim
Description
Helper function that summarises posterior samples of Rt from EpiEstim
Usage
summary_postsamples(x, prm.R)
Arguments
x |
dataframe |
prm.R |
list |
Value
summarised dataframe
Sample of wastewater concentration
Description
A subset of SARS-CoV-2 (N2 gene) concentration data in wastewater sampled from the Iona Island wastewater treatment plant in Vancouver between 7 July 2023 and 5 November 2023. Units are in N2 gene copies per milliliter of wastewater. Concentration was measured using RT-qPCR assays; RNA was extracted from suspended solids. See https://health-infobase.canada.ca/covid-19/wastewater/
Usage
ww.data
Format
ww.data
A data frame with 47 rows and 3 columns:
-
date: sampling date -
value: mean sample concentration between multiple replicates