Title:	Bayesian Estimation of the Force of Infection from Serological Data
Type:	Package
Version:	1.0.3
Description:	Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.
License:	MIT + file LICENSE
URL:	https://github.com/epiverse-trace/serofoi, https://epiverse-trace.github.io/serofoi/
BugReports:	https://github.com/epiverse-trace/serofoi/issues
Depends:	R (≥ 4.1.0)
Imports:	bayesplot, checkmate, config, cowplot, dplyr, ggplot2, glue, graphics, loo, expm, methods, purrr, Rcpp (≥ 0.12.0), rstan (≥ 2.18.1), rstantools (≥ 2.3.1), stats, tibble, tidyr
Suggests:	knitr, rlang, rmarkdown, scales, spelling, testthat (≥ 3.0.0)
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
VignetteBuilder:	knitr
Biarch:	true
Config/Needs/website:	epiverse-trace/epiversetheme
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
NeedsCompilation:	yes
RoxygenNote:	7.3.2
SystemRequirements:	GNU make
Packaged:	2025-03-27 14:51:29 UTC; torresn
Author:	Zulma M. Cucunubá [aut, cre], Nicolás T. Domínguez [aut], Ben Lambert [aut], Pierre Nouvellet [aut], Geraldine Gómez [ctb], Jaime A. Pavlich-Mariscal [ctb], Hugo Gruson [ctb], David Santiago Quevedo [ctb], Miguel Gámez [ctb], Sumali Bajaj [ctb], Everlyn Kamau [ctb], Richard Creswell [ctb], International Development Research Center (IDRC) [fnd], Pontificia Universidad Javeriana [cph]
Maintainer:	Zulma M. Cucunubá <zulma.cucunuba@javeriana.edu.co>
Repository:	CRAN
Date/Publication:	2025-03-31 10:00:02 UTC

The 'serofoi' package.

Description

A DESCRIPTION OF THE PACKAGE

Author(s)

Maintainer: Zulma M. Cucunubá zulma.cucunuba@javeriana.edu.co (ORCID)

Authors:

• Nicolás T. Domínguez ex-ntorres@javeriana.edu.co (ORCID)

• Ben Lambert

• Pierre Nouvellet

Other contributors:

• Geraldine Gómez (ORCID) [contributor]

• Jaime A. Pavlich-Mariscal (ORCID) [contributor]

• Hugo Gruson (ORCID) [contributor]

• David Santiago Quevedo (ORCID) [contributor]

• Miguel Gámez [contributor]

• Sumali Bajaj [contributor]

• Everlyn Kamau [contributor]

• Richard Creswell [contributor]

• International Development Research Center (IDRC) [funder]

• Pontificia Universidad Javeriana [copyright holder]

References

Stan Development Team (NA). RStan: the R interface to Stan. R package version 2.26.22. https://mc-stan.org

#' @keywords internal

See Also

Useful links:

• https://github.com/epiverse-trace/serofoi

• https://epiverse-trace.github.io/serofoi/

• Report bugs at https://github.com/epiverse-trace/serofoi/issues

Adds age group marker to serosurvey

Description

Adds age group marker to serosurvey

Usage

add_age_group_to_serosurvey(serosurvey)

Arguments

Value

serosurvey with additional column specifying age group marker defined as the mean floor between age_min and age_max

Builds stan data for sampling depending on the selected model

Description

Builds stan data for sampling depending on the selected model

Usage

build_stan_data(
  serosurvey,
  model_type = "constant",
  foi_prior = sf_uniform(),
  foi_index = NULL,
  is_log_foi = FALSE,
  foi_sigma_rw = sf_none(),
  is_seroreversion = FALSE,
  seroreversion_prior = sf_none()
)

Arguments

Value

List with necessary data for sampling the specified model

Chagas seroprevalence data in serofoi

Description

Datasets that measure the seroprevalence of IgG antibodies against Trypanosoma cruzi infection in rural areas of Colombia corresponding to a serosurvey conducted in 2012 for a rural indigenous community known to have long-term endemic transmission, where some control interventions have taken place over the years.

Usage

data(chagas2012)

Format

chagas2012

A ⁠<data.frame>⁠ with 4 rows and 5 columns:

survey_year

Year in which the serosurvey was conducted

n_sample

Number of collected samples per age group

n_seropositive

Number of positive samples per age group

age_min

Age group minimal age

age_max

Age group maximal age

Examples

data(chagas2012)

Check min and max age consistency for validation purposes

Description

Check min and max age consistency for validation purposes

Usage

check_age_constraints(df)

Value

Boolean checking consistency

Chikungunya seroprevalence data in serofoi

Description

Datasets that measure the seroprevalence of IgG antibodies against the Chikungunya virus conducted in Bahia, Brazil in October-December 2015 by Dias et al. (2018). The survey was conducted immediately after a large Chikungunya epidemic in the area.

Usage

data(chik2015)

Format

chik2015

A ⁠<data.frame>⁠ with 4 rows and 5 columns:

survey_year

Year in which the serosurvey was conducted

n_sample

Number of collected samples per age group

n_seropositive

Number of positive samples per age group

age_min

Age group minimal age

age_max

Age group maximal age

Examples

data(chik2015)

Extracts central estimates from stan_fit object for specified parameter

Description

Extracts central estimates from stan_fit object for specified parameter

Usage

extract_central_estimates(
  seromodel,
  serosurvey,
  alpha = 0.05,
  par_name = "foi_vector"
)

Arguments

Value

A dataframe with the following columns

median

Median of the samples computed as the 0.5 quantile

lower

Lower quantile alpha

upper

Upper quantile 1 - alpha

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
central_estimates <- extract_central_estimates(
  seromodel,
  veev2012,
  par_name = "foi"
)

Runs specified stan model for the Force-of-Infection (FoI)

Description

Runs specified stan model for the Force-of-Infection (FoI)

Usage

fit_seromodel(
  serosurvey,
  model_type = "constant",
  is_log_foi = FALSE,
  foi_prior = sf_normal(),
  foi_sigma_rw = sf_none(),
  foi_index = NULL,
  foi_init = NULL,
  is_seroreversion = FALSE,
  seroreversion_prior = sf_normal(),
  ...
)

Arguments

Value

stan_fit object with Force-of-Infection and seroreversion (when applicable) samples

Examples

data(chagas2012)
seromodel <- fit_seromodel(
  serosurvey = chagas2012,
  model_type = "time",
  foi_index = data.frame(
    year = 1935:2011,
    foi_index = c(rep(1, 46), rep(2, 31))
  ),
  iter = 100
)

Generates Force-of-Infection indexes for heterogeneous age groups

Description

Generates a list of integers indexing together the time/age intervals for which FoI values will be estimated in fit_seromodel. The max value in foi_index corresponds to the number of FoI values to be estimated when sampling. The serofoi approach to fitting serological data currently supposes that FoI is piecewise-constant across either groups of years or ages, and this function creates a Data Frame that communicates this grouping to the Stan model

Usage

get_foi_index(serosurvey, group_size, model_type)

Arguments

Value

A Data Frame which describes the grouping of years or ages (dependent on model) into pieces within which the FoI is assumed constant when performing model fitting. A single FoI value will be estimated for ages/years assigned with the same index

Examples

data(chagas2012)
foi_index <- get_foi_index(chagas2012, group_size = 25, model_type = "time")

Generate seropositivity counts by bin given the probability and sample size per age group bin

Description

Generate seropositivity counts by bin given the probability and sample size per age group bin

Usage

get_seroprev_counts_by_bin(
  prob_seroprev_by_age,
  sample_size_by_age_random,
  survey_features
)

Arguments

Plots Force-of-Infection central estimates

Description

Plots Force-of-Infection central estimates

Usage

plot_foi_estimates(
  seromodel,
  serosurvey,
  alpha = 0.05,
  foi_df = NULL,
  foi_max = NULL,
  size_text = 11,
  plot_constant = FALSE,
  x_axis = NA
)

Arguments

Value

ggplot object with estimated FoI

Examples

data(chagas2012)
seromodel <- fit_seromodel(
  serosurvey = chagas2012,
  model_type = "time",
  foi_index = data.frame(
    year = 1935:2011,
    foi_index = c(rep(1, 46), rep(2, 31))
  ),
  iter = 100,
  chains = 2
)
plot_foi_estimates(seromodel, chagas2012)

Plot r-hats convergence criteria for the specified model

Description

Plot r-hats convergence criteria for the specified model

Usage

plot_rhats(
  seromodel,
  serosurvey,
  size_text = 11,
  plot_constant = FALSE,
  x_axis = NA
)

Arguments

Value

ggplot object showing the r-hats of the model to be compared with the convergence criteria (horizontal dashed line)

Examples

data(chagas2012)
seromodel <- fit_seromodel(
  serosurvey = chagas2012,
  model_type = "time",
  foi_index = data.frame(
    year = 1935:2011,
    foi_index = c(rep(1, 46), rep(2, 31))
  ),
  iter = 100,
  chains = 2
)
plot_rhats(seromodel, chagas2012)

Visualise results of the provided model

Description

Visualise results of the provided model

Usage

plot_seromodel(
  seromodel,
  serosurvey,
  alpha = 0.05,
  bin_serosurvey = FALSE,
  bin_step = 5,
  foi_df = NULL,
  foi_max = NULL,
  loo_estimate_digits = 1,
  central_estimate_digits = 2,
  seroreversion_digits = 2,
  rhat_digits = 2,
  size_text = 11,
  plot_constant = FALSE,
  x_axis = NA
)

Arguments

Value

seromodel summary plot

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
plot_seromodel(seromodel, veev2012)

Plot seroprevalence estimates on top of the serosurvey

Description

Plot seroprevalence estimates on top of the serosurvey

Usage

plot_seroprev_estimates(
  seromodel,
  serosurvey,
  alpha = 0.05,
  size_text = 11,
  bin_serosurvey = FALSE,
  bin_step = 5
)

Arguments

Value

ggplot object with seroprevalence estimates and serosurveys plots

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
plot_seroprev_estimates(seromodel, veev2012)

Plots seroprevalence from the given serosurvey

Description

Plots seroprevalence from the given serosurvey

Usage

plot_serosurvey(
  serosurvey,
  size_text = 11,
  bin_serosurvey = FALSE,
  bin_step = 5
)

Arguments

Value

ggplot object with seroprevalence plot

Examples

# Chikungunya example serosurvey
data(chik2015)
plot_serosurvey(chik2015)

# VEEV example serosurvey
data(veev2012)
plot_serosurvey(veev2012)

Plots model summary

Description

Plots model summary

Usage

plot_summary(
  seromodel,
  serosurvey,
  loo_estimate_digits = 1,
  central_estimate_digits = 2,
  rhat_digits = 2,
  size_text = 11,
  plot_constant = FALSE
)

Arguments

Value

ggplot object with a summary of the specified model

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
plot_summary(seromodel, veev2012)

Prepares serosurvey for plotting

Description

Adds seroprevalence values with corresponding binomial confidence interval

Usage

prepare_serosurvey_for_plot(serosurvey, alpha = 0.05)

Arguments

Value

serosurvey with additional columns:

seroprev

Seroprevalence computed as the proportion of positive cases n_seropositive in the number of samples n_sample for each age group

seroprev_lower

Lower limit of the binomial confidence interval of seroprev

seroprev_upper

Upper limit of the binomial confidence interval of seroprev

Generate probabilities of seropositivity by age based on an age-varying Force-of-Infection (FoI) model.

Description

This function calculates the probabilities of seropositivity by age based on an age-varying FoI model. It takes into account the FoI and the rate of seroreversion.

Usage

prob_seroprev_age_by_age(foi, seroreversion_rate)

Arguments

Value

A dataframe with columns 'age' and 'seropositivity'.

Generate probabilities of seropositivity by age based on an age-and-time varying Force-of-Infection (FoI) model.

Description

This function calculates the probabilities of seropositivity by age based on an age-and-time-varying FoI model. It takes into account the FoI and the rate of seroreversion.

Usage

prob_seroprev_age_time_by_age(foi, seroreversion_rate)

Arguments

Value

A dataframe with columns 'age' and 'seropositivity'.

Generate probabilities of seropositivity by age based on model choice.

Description

This function generates seropositivity probabilities based on either a time-varying Force-of-Infection (FoI) model, an age-varying FoI model, or an age-and-time-varying FoI model. In all cases, it is possible to optionally include seroreversion.

Usage

prob_seroprev_by_age(model, foi, seroreversion_rate = 0)

Arguments

Value

A dataframe with columns 'age' and 'seropositivity'.

Examples

prob_seroprev_by_age(
  model = "age",
  foi = data.frame(
    age = 1:80,
    foi = rep(0.01, 80)
  )
)

Generate probabilities of seropositivity by age based on a general Force-of-Infection (FoI) model.

Description

This function calculates the probabilities of seropositivity by age based on an abstract model of the serocatalytic system.

Usage

prob_seroprev_gen_by_age(
  construct_A_fun,
  calculate_seroprev_fun,
  initial_conditions,
  max_age,
  ...
)

Arguments

Value

A dataframe with columns 'age' and 'seropositivity'.

Examples

# define age- and time-specific multipliers
foi_df_time <- data.frame(
  year = seq(1946, 2025, 1),
  foi = c(rep(0, 40), rep(1, 40))
)

foi_df_age <- data.frame(
  age = 1:80,
  foi = 2 * dlnorm(1:80, meanlog = 3.5, sdlog = 0.5)
)

u <- foi_df_age$foi
v <- foi_df_time$foi

# function to construct A matrix for one piece
construct_A <- function(t, tau, u, v) {
  u_bar <- u[t - tau]
  v_bar <- v[t]

  A <- diag(-1, ncol = 12, nrow = 12)
  A[row(A) == (col(A) + 1)] <- 1
  A[1, 1] <- -u_bar * v_bar
  A[2, 1] <- u_bar * v_bar
  A[12, 12] <- 0

  A
}

# determines the sum of seropositive compartments of those still alive
calculate_seropositivity_fn <- function(Y) {
  sum(Y[2:11]) / (1 - Y[12])
}

# initial conditions in 12D state vector
initial_conditions <- rep(0, 12)
initial_conditions[1] <- 1

# calculate probability
seropositive_hiv <- prob_seroprev_gen_by_age(
  construct_A,
  calculate_seropositivity_fn,
  initial_conditions,
  max_age = 80,
  u,
  v
)

Generate probabilities of seropositivity by age based on a time-varying Force-of-Infection (FoI) model.

Description

This function calculates the probabilities of seropositivity by age based on a time-varying FoI model. It takes into account the FoI and the rate of seroreversion.

Usage

prob_seroprev_time_by_age(foi, seroreversion_rate)

Arguments

Value

A dataframe with columns 'age' and 'seropositivity'.

Computes the probability of being seropositive when Forces-of-Infection (FoIs) vary by age

Description

Computes the probability of being seropositive when Forces-of-Infection (FoIs) vary by age

Usage

probability_exact_age_varying(ages, fois, seroreversion_rate = 0)

Arguments

Value

vector of probabilities of being seropositive for age-varying FoI including seroreversion (ordered from youngest to oldest individuals)

Computes the probability of being seropositive when Forces-of-Infection (FoIs) vary by time

Description

Computes the probability of being seropositive when Forces-of-Infection (FoIs) vary by time

Usage

probability_exact_time_varying(years, fois, seroreversion_rate = 0)

Arguments

Value

vector of probabilities of being seropositive for age-varying FoI including seroreversion (ordered from youngest to oldest individuals)

Sets initialization function for sampling

Description

Sets initialization function for sampling

Usage

set_foi_init(foi_init, is_log_foi, foi_index)

Arguments

Value

Function specifying initialization vector for the Force-of-Infection

Examples

data(chagas2012)
foi_index <- get_foi_index(chagas2012, group_size = 5, model_type = "age")
foi_init <- set_foi_init(
  foi_init = NULL,
  is_log_foi = FALSE,
  foi_index = foi_index
)

Set stan data defaults for sampling

Description

Set stan data defaults for sampling

Usage

set_stan_data_defaults(stan_data, is_log_foi = FALSE, is_seroreversion = FALSE)

Arguments

Value

List with default values of stan data for sampling

Sets Cauchy distribution parameters for sampling

Description

Sets Cauchy distribution parameters for sampling

Usage

sf_cauchy(location = 0, scale = 1)

Arguments

Value

List with specified statistics and name of the distribution

Examples

my_prior <- sf_cauchy()

Sets empty prior distribution

Description

Sets empty prior distribution

Usage

sf_none()

Value

List with the name of the empty distribution

Sets normal distribution parameters for sampling

Description

Sets normal distribution parameters for sampling

Usage

sf_normal(mean = 0, sd = 1)

Arguments

Value

List with specified statistics and name of the model

Examples

my_prior <- sf_normal()

Sets uniform distribution parameters for sampling

Description

Sets uniform distribution parameters for sampling

Usage

sf_uniform(min = 0, max = 10)

Arguments

Value

List with specified statistics and name of the model

Examples

my_prior <- sf_uniform()

Simulate serosurvey data based on various Force-of-Infection (FoI) models.

Description

This function generates binned serosurvey data based on either a time-varying FoI model, an age-varying FoI model, or an age-and-time-varying FoI model. In all cases, it is possible to optionally include seroreversion. This function allows construction of serosurveys with binned age groups, and it generates uncertainty in the distribution of a sample size within an age bin through multinomial sampling.

Usage

simulate_serosurvey(model, foi, survey_features, seroreversion_rate = 0)

Arguments

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

# time-varying model
foi_df <- data.frame(
  year = seq(1990, 2009, 1),
  foi = rnorm(20, 0.1, 0.01)
)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey(
model = "time",
foi = foi_df,
survey_features = survey_features)

# age-varying model
foi_df <- data.frame(
  age = seq(1, 20, 1),
  foi = rnorm(20, 0.1, 0.01)
)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey(
model = "age",
foi = foi_df,
survey_features = survey_features)

# age-and-time varying model
foi_df <- expand.grid(
  year = seq(1990, 2009, 1),
  age = seq(1, 20, 1)
)
foi_df$foi <- rnorm(20 * 20, 0.1, 0.01)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey(
model = "age-time",
foi = foi_df,
survey_features = survey_features)

Simulate serosurvey data based on an age-varying Force-of-Infection (FoI) model.

Description

This function generates binned serosurvey data based on an age-varying FoI model, optionally including seroreversion. This function allows construction of serosurveys with binned age groups, and it generates uncertainty in the distribution of a sample size within an age bin through multinomial sampling.

Usage

simulate_serosurvey_age(foi, survey_features, seroreversion_rate = 0)

Arguments

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

# specify FOIs for each year
foi_df <- data.frame(
  age = seq(1, 20, 1),
  foi = rnorm(20, 0.1, 0.01)
)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey_age(
foi_df, survey_features)

Simulate serosurvey data based on an age-and-time-varying Force-of-Infection (FoI) model.

Description

This function generates binned serosurvey data based on an age-and-time-varying FoI model, optionally including seroreversion. This function allows construction of serosurveys with binned age groups, and it generates uncertainty in the distribution of a sample size within an age bin through multinomial sampling.

Usage

simulate_serosurvey_age_time(foi, survey_features, seroreversion_rate = 0)

Arguments

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

# specify FOIs for each year
foi_df <- expand.grid(
  year = seq(1990, 2009, 1),
  age = seq(1, 20, 1)
)
foi_df$foi <- rnorm(20 * 20, 0.1, 0.01)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey_age_time(
foi_df, survey_features)

Simulate serosurvey data based on general serocatalytic model.

Description

This simulation method assumes only that the model system can be written as a piecewise-linear ordinary differential equation system.

Usage

simulate_serosurvey_general(
  construct_A_fun,
  calculate_seroprev_fun,
  initial_conditions,
  survey_features,
  ...
)

Arguments

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

foi_df_time <- data.frame(
  year = seq(1946, 2025, 1),
  foi = c(rep(0, 40), rep(1, 40))
)

foi_df_age <- data.frame(
  age = 1:80,
  foi = 2 * dlnorm(1:80, meanlog = 3.5, sdlog = 0.5)
)

# generate age and time dependent FoI from multipliers
foi_age_time <- expand.grid(
  year = foi_df_time$year,
  age = foi_df_age$age
) |>
  dplyr::left_join(foi_df_age, by = "age") |>
  dplyr::rename(foi_age = foi) |>
  dplyr::left_join(foi_df_time, by = "year") |>
  dplyr::rename(foi_time = foi) |>
  dplyr::mutate(foi = foi_age * foi_time) |>
  dplyr::select(-c("foi_age", "foi_time"))

# create survey features for simulating
max_age <- 80
n_sample <- 50
survey_features <- data.frame(
  age_min = seq(1, max_age, 5),
  age_max = seq(5, max_age, 5)) |>
  dplyr::mutate(n_sample = rep(n_sample, length(age_min))
  )

# simulate survey from age and time FoI
serosurvey <- simulate_serosurvey(
  model = "age-time",
  foi = foi_age_time,
  survey_features = survey_features
)

Simulate serosurvey data based on a time-varying Force-of-Infection (FoI) model.

Description

This function generates binned serosurvey data based on a time-varying FoI model, optionally including seroreversion. This function allows construction of serosurveys with binned age groups, and it generates uncertainty in the distribution of a sample size within an age bin through multinomial sampling.

Usage

simulate_serosurvey_time(foi, survey_features, seroreversion_rate = 0)

Arguments

Value

A dataframe with simulated serosurvey data, including age group information, overall sample sizes, the number of seropositive individuals, and other survey features.

Examples

# specify FOIs for each year
foi_df <- data.frame(
  year = seq(1990, 2009, 1),
  foi = rnorm(20, 0.1, 0.01)
)
survey_features <- data.frame(
  age_min = c(1, 3, 15),
  age_max = c(2, 14, 20),
  n_sample = c(1000, 2000, 1500))
serosurvey <- simulate_serosurvey_time(
foi_df, survey_features)

Summarise central estimate

Description

Summarise central estimate

Usage

summarise_central_estimate(
  seromodel,
  serosurvey,
  alpha,
  par_name = "seroreversion_rate",
  central_estimate_digits = 2
)

Arguments

Value

Text summarising specified central estimate

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
summarise_central_estimate(
  seromodel,
  veev2012,
  alpha = 0.05,
  par_name = "foi"
  )

Extract specified loo estimate

Description

Extract specified loo estimate

Usage

summarise_loo_estimate(
  seromodel,
  par_loo_estimate = "elpd_loo",
  loo_estimate_digits = 2
)

Arguments

Value

Text summarising specified loo estimate

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
summarise_loo_estimate(seromodel)

Summarise specified model

Description

Summarise specified model

Usage

summarise_seromodel(
  seromodel,
  serosurvey,
  alpha = 0.05,
  par_loo_estimate = "elpd_loo",
  loo_estimate_digits = 1,
  central_estimate_digits = 2,
  rhat_digits = 2
)

Arguments

Value

A list summarising the specified model

model_name

Name of the model

elpd

elpd and its standard deviation

foi

Estimated foi with credible interval (for 'constant' model)

foi_rhat

foi rhat value (for 'constant' model)

seroreversion_rate

Estimated seroreversion rate

seroreversion_rate_rhat

Seroreversion rate rhat value

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
summarise_seromodel(seromodel, veev2012)

Helper function to validate FoI structure for simulation

Description

Helper function to validate FoI structure for simulation

Usage

validate_foi_df(foi_df, cnames_additional)

Value

None

Helper function to validate FoI index consistency

Description

Helper function to validate FoI index consistency

Usage

validate_foi_index(foi_index, serosurvey, model_type)

Value

foi_index

Helper function to validate whether the current plot corresponds to a constant model

Description

Helper function to validate whether the current plot corresponds to a constant model

Usage

validate_plot_constant(plot_constant, x_axis, model_name, error_msg_x_axis)

Value

TRUE

Helper function to validate seroreversion rate properties for simulation

Description

Helper function to validate seroreversion rate properties for simulation

Usage

validate_seroreversion_rate(seroreversion_rate)

Value

None

Helper function to validate serosurvey structure

Description

Helper function to validate serosurvey structure

Usage

validate_serosurvey(serosurvey)

Helper function to validate consistency between the FoI and the survey features for simulation

Description

Helper function to validate consistency between the FoI and the survey features for simulation

Usage

validate_simulation_age(survey_features, foi_df)

Value

None

Helper function to validate consistency between the FoI and the survey features for simulation of age- and time-varying model

Description

Helper function to validate consistency between the FoI and the survey features for simulation of age- and time-varying model

Usage

validate_simulation_age_time(survey_features, foi_df)

Value

None

Helper function to validate serosurvey features for simulation

Description

Helper function to validate serosurvey features for simulation

Usage

validate_survey_features(survey_features)

Value

None

Venezuelan Equine Encephalitis Virus (VEEV) seroprevalence data in serofoi

Description

Datasets that measure the seroprevalence of IgG antibodies against VEEV in a rural village in Panamá in 2012 [Carrera2020].

Usage

data(veev2012)

Format

veev2012

A ⁠<data.frame>⁠ with 4 rows and 5 columns:

survey_year

Year in which the serosurvey was conducted

n_sample

Number of collected samples per age group

n_seropositive

Number of positive samples per age group

age_min

Age group minimal age

age_max

Age group maximal age

Examples

data(veev2012)