Estimating Infection Rates from Serological Data

Description

This package translates antibody levels measured in a (cross-sectional) population sample into an estimate of the frequency with which seroconversions (infections) occur in the sampled population.

Details

_PACKAGE

Author(s)

• Peter Teunis p.teunis@emory.edu

• Doug Ezra Morrison demorrison@ucdavis.edu

• Kristen Aiemjoy kaiemjoy@ucdavis.edu

• Kristina Lai kwlai@ucdavis.edu

References

Methods for estimating seroincidence

• Teunis, P. F. M., and J. C. H. van Eijkeren. "Estimation of seroconversion rates for infectious diseases: Effects of age and noise." Statistics in Medicine 39, no. 21 (2020): 2799-2814.

• Teunis, P. F. M., J. C. H. van Eijkeren, W. F. de Graaf, A. Bonačić Marinović, and M. E. E. Kretzschmar. "Linking the seroresponse to infection to within-host heterogeneity in antibody production." Epidemics 16 (2016): 33-39.

Applications

• Aiemjoy, Kristen, Jessica C. Seidman, Senjuti Saha, Sira Jam Munira, Mohammad Saiful Islam Sajib, Syed Muktadir Al Sium, Anik Sarkar et al. "Estimating typhoid incidence from community-based serosurveys: a multicohort study." The Lancet Microbe 3, no. 8 (2022): e578-e587.

• Aiemjoy, Kristen, John Rumunu, Juma John Hassen, Kirsten E. Wiens, Denise Garrett, Polina Kamenskaya, Jason B. Harris et al. "Seroincidence of enteric fever, Juba, South Sudan." Emerging infectious diseases 28, no. 11 (2022): 2316.

• Monge, S., Teunis, P. F., Friesema, I., Franz, E., Ang, W., van Pelt, W., Mughini-Gras, L. "Immune response-eliciting exposure to Campylobacter vastly exceeds the incidence of clinically overt campylobacteriosis but is associated with similar risk factors: A nationwide serosurvey in the Netherlands" Journal of Infection, 2018, 1–7, doi:10.1016/j.jinf.2018.04.016

• Kretzschmar, M., Teunis, P. F., Pebody, R. G. "Incidence and reproduction numbers of pertussis: estimates from serological and social contact data in five European countries" PLoS Medicine 7, no. 6 (June 1, 2010):e1000291. doi:10.1371/journal.pmed.1000291.

• Simonsen, J., Strid, M. A., Molbak, K., Krogfelt, K. A., Linneberg, A., Teunis, P. "Sero-epidemiology as a tool to study the incidence of Salmonella infections in humans" Epidemiology and Infection 136, no. 7 (July 1, 2008): 895–902. doi:10.1017/S0950268807009314

• Simonsen, J., Teunis, P. F., van Pelt, W., van Duynhoven, Y., Krogfelt, K. A., Sadkowska-Todys, M., Molbak K. "Usefulness of seroconversion rates for comparing infection pressures between countries" Epidemiology and Infection, April 12, 2010, 1–8. doi:10.1017/S0950268810000750.

• Falkenhorst, G., Simonsen, J., Ceper, T. H., van Pelt, W., de Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Jernberg, C., Rota, M. C., van Duynhoven, Y. T., Teunis, P. F., Krogfelt, K. A., Molbak, K. "Serological cross-sectional studies on salmonella incidence in eight European countries: no correlation with incidence of reported cases" BMC Public Health 12, no. 1 (July 15, 2012): 523–23. doi:10.1186/1471-2458-12-523.

• Teunis, P. F., Falkenhorst, G., Ang, C. W., Strid, M. A., De Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Rota, M. C., Simonsen, J. B., Molbak, K., Van Duynhoven, Y. T., van Pelt, W. "Campylobacter seroconversion rates in selected countries in the European Union" Epidemiology and Infection 141, no. 10 (2013): 2051–57. doi:10.1017/S0950268812002774.

• de Melker, H. E., Versteegh, F. G., Schellekens, J. F., Teunis, P. F., Kretzschmar, M. "The incidence of Bordetella pertussis infections estimated in the population from a combination of serological surveys" The Journal of Infection 53, no. 2 (August 1, 2006): 106–13. doi:10.1016/j.jinf.2005.10.020

Quantification of seroresponse

• de Graaf, W. F., Kretzschmar, M. E., Teunis, P. F., Diekmann, O. "A two-phase within-host model for immune response and its application to serological profiles of pertussis" Epidemics 9 (2014):1–7. doi:10.1016/j.epidem.2014.08.002.

• Berbers, G. A., van de Wetering, M. S., van Gageldonk, P. G., Schellekens, J. F., Versteegh, F. G., Teunis, P.F. "A novel method for evaluating natural and vaccine induced serological responses to Bordetella pertussis antigens" Vaccine 31, no. 36 (August 12, 2013): 3732–38. doi:10.1016/j.vaccine.2013.05.073.

• Versteegh, F. G., Mertens, P. L., de Melker, H. E., Roord, J. J., Schellekens, J. F., Teunis, P. F. "Age-specific long-term course of IgG antibodies to pertussis toxin after symptomatic infection with Bordetella pertussis" Epidemiology and Infection 133, no. 4 (August 1, 2005): 737–48.

• Teunis, P. F., van der Heijden, O. G., de Melker, H. E., Schellekens, J. F., Versteegh, F. G., Kretzschmar, M. E. "Kinetics of the IgG antibody response to pertussis toxin after infection with B. pertussis" Epidemiology and Infection 129, no. 3 (December 10, 2002):479. doi:10.1017/S0950268802007896.

Calculate negative log-likelihood

Description

Same as log_likelihood(), except negated and requiring lambda on log scale (used in combination with nlm(), to ensure that the optimization search doesn't stray into negative values of lambda).

Usage

.nll(log.lambda, ...)

Arguments

Value

the negative log-likelihood of the data with the current parameter values

Extract or replace parts of a seroincidence.by object

Description

Extract or replace parts of a seroincidence.by object

Usage

## S3 method for class 'seroincidence.by'
x[i, ...]

Arguments

Value

the subset specified

kinetics of the antibody (ab) response (power function decay)

Description

kinetics of the antibody (ab) response (power function decay)

Usage

ab(t, par, ...)

Arguments

Value

a matrix()

Load antibody decay curve parameter

Description

Load antibody decay curve parameter

Usage

as_curve_params(data, antigen_isos = NULL)

Arguments

Value

a curve_data object (a tibble::tbl_df with extra attribute antigen_isos)

Examples

library(magrittr)
curve_data <-
  serocalculator_example("example_curve_params.csv") %>%
  read.csv() %>%
  as_curve_params()

print(curve_data)

Load noise parameters

Description

Load noise parameters

Usage

as_noise_params(data, antigen_isos = NULL)

Arguments

Value

a noise_params object (a tibble::tbl_df with extra attribute antigen_isos)

Examples

library(magrittr)
noise_data <-
  serocalculator_example("example_noise_params.csv") %>%
  read.csv() %>%
  as_noise_params()

print(noise_data)

Load a cross-sectional antibody survey data set

Description

Load a cross-sectional antibody survey data set

Usage

as_pop_data(
  data,
  antigen_isos = NULL,
  age = "Age",
  value = "result",
  id = "index_id",
  standardize = TRUE
)

Arguments

Value

a pop_data object (a tibble::tbl_df with extra attribute antigen_isos)

Examples

library(magrittr)
xs_data <-
  serocalculator_example("example_pop_data.csv") %>%
  read.csv() %>%
  as_pop_data()

print(xs_data)

graph antibody decay curves by antigen isotype

Description

graph antibody decay curves by antigen isotype

Usage

## S3 method for class 'curve_params'
autoplot(
  object,
  antigen_isos = unique(object$antigen_iso),
  ncol = min(3, length(antigen_isos)),
  ...
)

Arguments

Details

rows_to_graph

Note that if you directly specify rows_to_graph when calling this function, the row numbers are enumerated separately for each antigen isotype; in other words, for the purposes of this argument, row numbers start over at 1 for each antigen isotype. There is currently no way to specify different row numbers for different antigen isotypes; if you want to do that, you could call plot_curve_params_one_ab() directly for each antigen isotype and combine the resulting panels yourself. Or you could subset curve_params manually, before passing it to this function, and set the n_curves argument to Inf.

Value

a ggplot2::ggplot() object

Examples

library(dplyr)
library(ggplot2)
library(magrittr)

curve <-
  serocalculator_example("example_curve_params.csv") %>%
  read.csv() %>%
  as_curve_params() %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
  autoplot()

curve

Plot distribution of antibodies

Description

autoplot() method for pop_data objects

Usage

## S3 method for class 'pop_data'
autoplot(object, log = FALSE, type = "density", strata = NULL, ...)

Arguments

Value

a ggplot2::ggplot object

Examples

library(dplyr)
library(ggplot2)
library(magrittr)

xs_data <-
  serocalculator_example("example_pop_data.csv") %>%
  read.csv() %>%
  as_pop_data()

xs_data %>% autoplot(strata = "catchment", type = "density")
xs_data %>% autoplot(strata = "catchment", type = "age-scatter")

Plot the log-likelihood curve for the incidence rate estimate

Description

Plot the log-likelihood curve for the incidence rate estimate

Usage

## S3 method for class 'seroincidence'
autoplot(object, log_x = FALSE, ...)

Arguments

Value

a ggplot2::ggplot()

Examples

library(dplyr)
library(ggplot2)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est1 <- est.incidence(
  pop_data = xs_data,
  curve_param = curve,
  noise_param = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  build_graph = TRUE
)

# Plot the log-likelihood curve
autoplot(est1)

Plot seroincidence.by log-likelihoods

Description

Plots log-likelihood curves by stratum, for seroincidence.by objects

Usage

## S3 method for class 'seroincidence.by'
autoplot(object, ncol = min(3, length(object)), ...)

Arguments

Value

an object of class "ggarrange", which is a ggplot2::ggplot() or a list() of ggplot2::ggplot()s.

Examples

library(dplyr)
library(ggplot2)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est2 <- est.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  #num_cores = 8, #Allow for parallel processing to decrease run time
  build_graph = TRUE
)

# Plot the log-likelihood curve
autoplot(est2)

Plot method for summary.seroincidence.by objects

Description

Plot method for summary.seroincidence.by objects

Usage

## S3 method for class 'summary.seroincidence.by'
autoplot(object, xvar, alpha = 0.7, shape = 1, width = 0.001, ...)

Arguments

Value

a ggplot2::ggplot() object

Examples

library(dplyr)
library(ggplot2)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est2 <- est.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  #num_cores = 8 #Allow for parallel processing to decrease run time
)

est2sum <- summary(est2)

autoplot(est2sum, "catchment")

Check the formatting of a cross-sectional antibody survey dataset.

Description

Check the formatting of a cross-sectional antibody survey dataset.

Usage

check_pop_data(pop_data, verbose = FALSE)

Arguments

Value

NULL (invisibly)

Examples

library(magrittr)

xs_data <-
  serocalculator_example("example_pop_data.csv") %>%
  read.csv() %>%
  as_pop_data()

check_pop_data(xs_data, verbose = TRUE)

Convert a data.frame (or tibble) into a multidimensional array

Description

df.to.array() was renamed to df_to_array() to create a more consistent API.

Usage

df.to.array(df, dim_var_names, value_var_name = "value")

Find the maximum likelihood estimate of the incidence rate parameter

Description

This function models seroincidence using maximum likelihood estimation; that is, it finds the value of the seroincidence parameter which maximizes the likelihood (i.e., joint probability) of the data.

Usage

est.incidence(
  pop_data,
  curve_params,
  noise_params,
  antigen_isos = pop_data$antigen_iso %>% unique(),
  lambda_start = 0.1,
  stepmin = 1e-08,
  stepmax = 3,
  verbose = FALSE,
  build_graph = FALSE,
  print_graph = build_graph & verbose,
  ...
)

Arguments

Value

a "seroincidence" object, which is a stats::nlm() fit object with extra meta-data attributes lambda_start, antigen_isos, and ll_graph

Examples

library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est1 <- est.incidence(
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
)

summary(est1)

Estimate Seroincidence

Description

Function to estimate seroincidences based on cross-sectional serology data and longitudinal response model.

Usage

est.incidence.by(
  pop_data,
  curve_params,
  noise_params,
  strata,
  curve_strata_varnames = strata,
  noise_strata_varnames = strata,
  antigen_isos = pop_data %>% pull("antigen_iso") %>% unique(),
  lambda_start = 0.1,
  build_graph = FALSE,
  num_cores = 1L,
  verbose = FALSE,
  print_graph = FALSE,
  ...
)

Arguments

Details

If strata is left empty, a warning will be produced, recommending that you use est.incidence() for unstratified analyses, and then the data will be passed to est.incidence(). If for some reason you want to use est.incidence.by() with no strata instead of calling est.incidence(), you may use NA, NULL, or "" as the strata argument to avoid that warning.

Value

• if strata has meaningful inputs: An object of class "seroincidence.by"; i.e., a list of "seroincidence" objects from est.incidence(), one for each stratum, with some meta-data attributes.

• if strata is missing, NULL, NA, or "": An object of class "seroincidence".

Examples

library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est2 <- est.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  # num_cores = 8 # Allow for parallel processing to decrease run time
  iterlim = 5 # limit iterations for the purpose of this example
)

summary(est2)

Small example of noise parameters for typhoid

Description

A subset of noise parameter estimates from the SEES study, for examples and testing.

Usage

example_noise_params_pk

Format

example_noise_params_pk

A curve_params object (from as_curve_params()) with 4 rows and 7 columns:

antigen_iso

which antigen and isotype are being measured (data is in long format)

Country

Location for which the noise parameters were estimated

y.low

Lower limit of detection

eps

Measurement noise, defined by a CV (coefficient of variation) as the ratio of the standard deviation to the mean for replicates. Note that the CV should ideally be measured across plates rather than within the same plate.

nu

Biological noise: error from cross-reactivity to other antibodies. It is defined as the 95th percentile of the distribution of antibody responses to the antigen-isotype in a population with no exposure.

y.high

Upper limit of detection

Lab

Lab for which noise was estimated.

Source

https://osf.io/rtw5k

Calculate negative log-likelihood (deviance) for one antigen:isotype pair and several values of incidence

Description

Calculates negative log-likelihood (deviance) for one antigen:isotype pair and several values of incidence (lambda).

Usage

f_dev(lambda, csdata, lnpars, cond)

Arguments

Details

Vectorized version of f_dev0(); interface with C lib serocalc.so

Value

a numeric vector of negative log-likelihoods, corresponding to the elements of input lambda

Examples

library(dplyr)
library(tibble)

# load in longitudinal parameters
curve_params <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

# load in pop data
xs_data <-
  sees_pop_data_pk_100

#Load noise params
noise_params <- tibble(
  antigen_iso = c("HlyE_IgG", "HlyE_IgA"),
  nu = c(0.5, 0.5),                          # Biologic noise (nu)
  eps = c(0, 0),                             # M noise (eps)
  y.low = c(1, 1),                           # low cutoff (llod)
  y.high = c(5e6, 5e6))                      # high cutoff (y.high)

cur_antibody = "HlyE_IgA"

cur_data =
  xs_data %>%
  dplyr::filter(
   .data$catchment == "aku",
   .data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_curve_params =
  curve_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_noise_params =
  noise_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody)

if(!is.element('d', names(cur_curve_params)))
{
  cur_curve_params =
    cur_curve_params %>%
    dplyr::mutate(
      alpha = .data$alpha * 365.25,
      d = .data$r - 1)
}

lambdas = seq(.1, .2, by = .01)
f_dev(
    lambda = lambdas,
    csdata = cur_data,
    lnpars = cur_curve_params,
    cond = cur_noise_params
  )

Calculate negative log-likelihood (deviance) for one antigen:isotype pair and incidence rate

Description

Calculate negative log-likelihood (deviance) for one antigen:isotype pair and incidence rate

Usage

f_dev0(lambda, csdata, lnpars, cond)

Arguments

Details

interface with C lib serocalc.so

Value

a numeric() negative log-likelihood, corresponding to input lambda

Examples

library(dplyr)
library(tibble)

# load in longitudinal parameters
curve_params <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

# load in pop data
xs_data <-
  sees_pop_data_pk_100

#Load noise params
noise_params <- tibble(
  antigen_iso = c("HlyE_IgG", "HlyE_IgA"),
  nu = c(0.5, 0.5),                          # Biologic noise (nu)
  eps = c(0, 0),                             # M noise (eps)
  y.low = c(1, 1),                           # low cutoff (llod)
  y.high = c(5e6, 5e6))                      # high cutoff (y.high)

cur_antibody = "HlyE_IgA"

cur_data <-
  xs_data %>%
  dplyr::filter(
   .data$catchment == "dhaka",
   .data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_curve_params <-
  curve_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody) %>%
  dplyr::slice_head(n = 100)

cur_noise_params <-
  noise_params %>%
  dplyr::filter(.data$antigen_iso == cur_antibody)

if(!is.element('d', names(cur_curve_params)))
{
  cur_curve_params <-
    cur_curve_params %>%
    dplyr::mutate(
      alpha = .data$alpha * 365.25,
      d = .data$r - 1)
}

lambda = 0.1
f_dev0(
    lambda = lambda,
    csdata = cur_data,
    lnpars = cur_curve_params,
    cond = cur_noise_params
  )

Calculate negative log-likelihood (deviance)

Description

fdev() was renamed to f_dev() to create a more consistent API.

Usage

fdev(lambda, csdata, lnpars, cond)

Graph estimated antibody decay curve

Description

Graph estimated antibody decay curve

Usage

graph.curve.params(
  curve_params,
  antigen_isos = unique(curve_params$antigen_iso),
  verbose = FALSE
)

Arguments

Value

a ggplot2::ggplot() object

Examples

curve <-
  typhoid_curves_nostrat_100 |>
  dplyr::filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

plot1 <- graph.curve.params(curve)

print(plot1)

Graph log-likelihood of data

Description

Graph log-likelihood of data

Usage

graph_loglik(
  pop_data,
  curve_params,
  noise_params,
  antigen_isos = pop_data %>% get_biomarker_levels(),
  x = 10^seq(-3, 0, by = 0.1),
  highlight_points = NULL,
  highlight_point_names = "highlight_points",
  log_x = FALSE,
  previous_plot = NULL,
  curve_label = paste(antigen_isos, collapse = " + "),
  ...
)

Arguments

Value

a ggplot2::ggplot()

Examples

library(dplyr)
library(tibble)

# Load cross-sectional data
xs_data <-
  sees_pop_data_pk_100

# Load curve parameters and subset for the purposes of this example
curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

# Load noise parameters
cond <- tibble(
  antigen_iso = c("HlyE_IgG", "HlyE_IgA"),
  nu = c(0.5, 0.5),                          # Biologic noise (nu)
  eps = c(0, 0),                             # M noise (eps)
  y.low = c(1, 1),                           # Low cutoff (llod)
  y.high = c(5e6, 5e6))                      # High cutoff (y.high)

# Graph the log likelihood
lik_HlyE_IgA <- # nolint: object_name_linter
  graph_loglik(
    pop_data = xs_data,
    curve_params = curve,
    noise_params = cond,
    antigen_isos = "HlyE_IgA",
    log_x = TRUE
)

lik_HlyE_IgA # nolint: object_name_linter

extract a row from longitudinal parameter set

Description

take a random sample from longitudinal parameter set given age at infection, for a list of antibodies ldpar() was renamed to row_longitudinal_parameter() to create a more consistent API.

Usage

ldpar(age, antigen_isos, nmc, npar, ...)

Arguments

Value

an array of parameters: c(y0,b0,mu0,mu1,c1,alpha,shape)

Calculate log-likelihood

Description

Calculates the log-likelihood of a set of cross-sectional antibody response data, for a given incidence rate (lambda) value.

llik() was renamed to log_likelihood() to create a more consistent API.

Usage

llik(...)

Load antibody decay curve parameter samples

Description

Load antibody decay curve parameter samples

Usage

load_curve_params(file_path, antigen_isos = NULL)

Arguments

Value

a curve_params object (a tibble::tbl_df with extra attribute antigen_isos)

Examples

curve <- load_curve_params(serocalculator_example("example_curve_params.rds"))

print(curve)

Load noise parameters

Description

Load noise parameters

Usage

load_noise_params(file_path, antigen_isos = NULL)

Arguments

Value

a noise object (a tibble::tbl_df with extra attribute antigen_isos)

Examples

noise <- load_noise_params(serocalculator_example("example_noise_params.rds"))
print(noise)

Load a cross-sectional antibody survey data set

Description

Load a cross-sectional antibody survey data set

Usage

load_pop_data(file_path, ...)

Arguments

Value

a pop_data object (a tibble::tbl_df with extra attributes)

Examples

xs_data <- load_pop_data(serocalculator_example("example_pop_data.rds"))

print(xs_data)

Calculate log-likelihood

Description

Calculates the log-likelihood of a set of cross-sectional antibody response data, for a given incidence rate (lambda) value.

Usage

log_likelihood(
  lambda,
  pop_data,
  curve_params,
  noise_params,
  antigen_isos = get_biomarker_levels(pop_data),
  verbose = FALSE,
  ...
)

Arguments

Value

the log-likelihood of the data with the current parameter values

Examples

library(dplyr)
library(tibble)

# Load cross-sectional data
xs_data <-
  sees_pop_data_pk_100

# Load curve parameters and subset for the purposes of this example
curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

# Load noise params
cond <- tibble(
  antigen_iso = c("HlyE_IgG", "HlyE_IgA"),
  nu = c(0.5, 0.5), # Biologic noise (nu)
  eps = c(0, 0), # M noise (eps)
  y.low = c(1, 1), # low cutoff (llod)
  y.high = c(5e6, 5e6)
) # high cutoff (y.high)

# Calculate log-likelihood
ll_AG <- log_likelihood(
  pop_data = xs_data,
  curve_params = curve,
  noise_params = cond,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  lambda = 0.1
) %>% print()

generate random sample from baseline distribution

Description

generate random sample from baseline distribution

Usage

mk_baseline(kab, n = 1, blims, ...)

Arguments

Value

a numeric() vector

generate random sample from baseline distribution

Description

mkbaseline() was renamed to mk_baseline() to create a more consistent API.

Usage

mkbaseline(kab, n = 1, blims, ...)

Arguments

Value

a numeric() vector

Graph an antibody decay curve model

Description

Graph an antibody decay curve model

Usage

plot_curve_params_one_ab(
  object,
  verbose = FALSE,
  alpha = 0.4,
  n_curves = 100,
  n_points = 1000,
  log_x = FALSE,
  log_y = TRUE,
  rows_to_graph = seq_len(min(n_curves, nrow(object))),
  xlim = c(10^-1, 10^3.1),
  ...
)

Arguments

Details

n_curves and rows_to_graph

In most cases, curve_params will contain too many rows of MCMC samples for all of these samples to be plotted at once.

• Setting the n_curves argument to a value smaller than the number of rows in curve_params will cause this function to select the first n_curves rows to graph.

• Setting n_curves larger than the number of rows in ' will result all curves being plotted.

• If the user directly specifies the rows_to_graph argument, then n_curves has no effect.

Value

a ggplot2::ggplot() object

Examples

library(dplyr) # loads the `%>%` operator and `dplyr::filter()`

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso == ("HlyE_IgG")) %>%
  serocalculator:::plot_curve_params_one_ab()

  curve

Print Method for seroincidence Object

Description

Custom print() function to show output of the seroincidence calculator est.incidence().

Usage

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

Arguments

Value

an invisible copy of input parameter x

Examples

## Not run: 
# Estimate seroincidence
seroincidence <- est.incidence.by(...)

# Print the seroincidence object to the console
print(seroincidence)

# Or simply type (appropriate print method will be invoked automatically)
seroincidence

## End(Not run)

Print Method for seroincidence.by Object

Description

Custom print() function to show output of the seroincidence calculator est.incidence.by().

Usage

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

Arguments

Value

an invisible copy of input parameter x

Examples

## Not run: 
# Estimate seroincidence
seroincidence <- est.incidence.by(...)

# Print the seroincidence object to the console
print(seroincidence)

# Or simply type (appropriate print method will be invoked automatically)
seroincidence

## End(Not run)

Print Method for Seroincidence Summary Object

Description

Custom print() function for "summary.seroincidence.by" objects (constructed by summary.seroincidence.by())

Usage

## S3 method for class 'summary.seroincidence.by'
print(x, ...)

Arguments

Value

an invisible copy of input parameter x

Examples

## Not run: 
# Estimate seroincidence
seroincidence <- est.incidence.by(...)

# Calculate summary statistics for the seroincidence object
seroincidenceSummary <- summary(seroincidence)

# Print the summary of seroincidence object to the console
print(seroincidenceSummary)

# Or simply type (appropriate print method will be invoked automatically)
seroincidenceSummary

## End(Not run)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot

extract a row from longitudinal parameter set

Description

take a random sample from longitudinal parameter set given age at infection, for a list of antibodies

Usage

row_longitudinal_parameter(age, antigen_isos, nmc, npar, ...)

Arguments

Value

an array of parameters: c(y0,b0,mu0,mu1,c1,alpha,shape)

Small example cross-sectional data set

Description

A subset of data from the SEES data, for examples and testing.

Usage

sees_pop_data_pk_100

Format

sees_pop_data_pk_100

A pop_data object (from as_pop_data()) with 200 rows and 8 columns:

id

Observation ID

Country

Country where the participant was living

cluster

survey sampling cluster

catchment

survey catchment area

age

participant's age when sampled, in years

antigen_iso

which antigen and isotype are being measured (data is in long format)

value

concentration of antigen isotype, in ELISA units

Source

https://osf.io/n6cp3

Small example cross-sectional data set

Description

A subset of data from the SEES data, for examples and testing.

Usage

sees_pop_data_pk_100_old_names

Format

sees_pop_data_pk_100_old_names

A pop_data object (from as_pop_data()) with 200 rows and 8 columns:

index_id

Observation ID

Country

Country where the participant was living

cluster

survey sampling cluster

catchment

survey catchment area

Age

participant's age when sampled, in years

antigen_iso

which antigen and isotype are being measured (data is in long format)

result

concentration of antigen isotype, in ELISA units

Source

https://osf.io/n6cp3

Get path to an example file

Description

The serocalculator package comes bundled with a number of sample files in its inst/extdata directory. This serocalculator_example() function make those sample files easy to access.

Usage

serocalculator_example(file = NULL)

Arguments

Details

Adapted from readr::readr_example() following the guidance in https://r-pkgs.org/data.html#sec-data-example-path-helper.

Value

a character string providing the path to the file specified by file, or a vector or available files if file = NULL.

Examples

serocalculator_example()
serocalculator_example("example_pop_data.csv")

Simulate a cross-sectional serosurvey with noise

Description

Makes a cross-sectional data set (age, y(t) set) and adds noise, if desired.

Usage

sim.cs(
  lambda = 0.1,
  n.smpl = 100,
  age.rng = c(0, 20),
  age.fx = NA,
  antigen_isos,
  n.mc = 0,
  renew.params = FALSE,
  add.noise = FALSE,
  curve_params,
  noise_limits,
  format = "wide",
  verbose = FALSE,
  ...
)

Arguments

Value

a tibble::tbl_df containing simulated cross-sectional serosurvey data, with columns:

• age: age (in days)

• one column for each element in the antigen_iso input argument

Examples

# Load curve parameters
curve <-
  typhoid_curves_nostrat_100

# Specify the antibody-isotype responses to include in analyses
antibodies <- c("HlyE_IgA", "HlyE_IgG")

# Set seed to reproduce results
set.seed(54321)

# Simulated incidence rate per person-year
lambda <- 0.2;

# Range covered in simulations
lifespan <- c(0, 10);

# Cross-sectional sample size
nrep <- 100

# Biologic noise distribution
dlims <- rbind(
  "HlyE_IgA" = c(min = 0, max = 0.5),
  "HlyE_IgG" = c(min = 0, max = 0.5)
)

# Generate cross-sectional data
csdata <- sim.cs(
  curve_params = curve,
  lambda = lambda,
  n.smpl = nrep,
  age.rng = lifespan,
  antigen_isos = antibodies,
  n.mc = 0,
  renew.params = TRUE,
  add.noise = TRUE,
  noise_limits = dlims,
  format = "long"
)

Simulate multiple data sets

Description

Simulate multiple data sets

Usage

sim.cs.multi(
  nclus = 10,
  lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3),
  num_cores = max(1, parallel::detectCores() - 1),
  rng_seed = 1234,
  renew.params = TRUE,
  add.noise = TRUE,
  verbose = FALSE,
  ...
)

Arguments

Value

a tibble::tibble()

collect cross-sectional data

Description

output: (age, y(t) set)

Usage

simcs.tinf(
  lambda,
  n.smpl,
  age.rng,
  age.fx = NA,
  antigen_isos,
  n.mc = 0,
  renew.params = FALSE,
  ...
)

Arguments

Value

an array()

simulate antibody kinetics of y over a time interval

Description

simulate antibody kinetics of y over a time interval

Usage

simresp.tinf(
  lambda,
  t.end,
  age.fx,
  antigen_isos,
  n.mc = 0,
  renew.params,
  predpar,
  ...
)

Arguments

Value

This function returns a list() with:

• t = times (in days, birth at day 0),

• b = bacteria level, for each antibody signal (not used; probably meaningless),

• y = antibody level, for each antibody signal

• smp = whether an infection involves a big jump or a small jump

• t.inf = times when infections have occurred.

Extract strata from an object

Description

Generic method for extracting strata from objects. See strata.seroincidence.by()

Usage

strata(x)

Arguments

Value

the strata of x

Extract the Strata attribute from an object, if present

Description

Extract the Strata attribute from an object, if present

Usage

## S3 method for class 'seroincidence.by'
strata(x)

Arguments

Value

• a tibble::tibble() with strata in rows, or

• NULL if x does not have a "strata" attribute

Split data by stratum

Description

Split biomarker data, decay curve parameters, and noise parameters to prepare for stratified incidence estimation.

Usage

stratify_data(
  data,
  curve_params,
  noise_params,
  strata_varnames = "",
  curve_strata_varnames = NULL,
  noise_strata_varnames = NULL,
  antigen_isos = data %>% attr("antigen_isos")
)

Arguments

Value

a "biomarker_data_and_params.list" object (a list with extra attributes "strata", "antigen_isos", etc)

Examples

## Not run: 
library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

stratified_data =
  stratify_data(
   data = xs_data,
   curve_params = curve,
   noise_params = noise,
   strata_varnames = "catchment",
   curve_strata_varnames = NULL,
   noise_strata_varnames = NULL
   )

## End(Not run)

Summarize cross-sectional antibody survey data

Description

summary() method for pop_data objects

Usage

## S3 method for class 'pop_data'
summary(object, strata = NULL, ...)

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

Arguments

Value

a summary.pop_data object, which is a list containing two summary tables:

• age_summary summarizing age

• ab_summary summarizing value, stratified by antigen_iso

Examples

library(dplyr)

xs_data <-
  sees_pop_data_pk_100
summary(xs_data, strata = "catchment")

Summarizing fitted seroincidence models

Description

This function is a summary() method for seroincidence objects.

Usage

## S3 method for class 'seroincidence'
summary(object, coverage = 0.95, ...)

Arguments

Value

a tibble::tibble() containing the following:

• est.start: the starting guess for incidence rate

• ageCat: the age category we are analyzing

• incidence.rate: the estimated incidence rate, per person year

• CI.lwr: lower limit of confidence interval for incidence rate

• CI.upr: upper limit of confidence interval for incidence rate

• coverage: coverage probability

• log.lik: log-likelihood of the data used in the call to est.incidence(), evaluated at the maximum-likelihood estimate of lambda (i.e., at incidence.rate)

• iterations: the number of iterations used

• antigen_isos: a list of antigen isotypes used in the analysis

• nlm.convergence.code: information about convergence of the likelihood maximization procedure performed by nlm() (see "Value" section of stats::nlm(), component code); codes 3-5 indicate issues:

  • 1: relative gradient is close to zero, current iterate is probably solution.

  • 2: successive iterates within tolerance, current iterate is probably solution.

  • 3: Last global step failed to locate a point lower than x. Either x is an approximate local minimum of the function, the function is too non-linear for this algorithm, or stepmin in est.incidence() (a.k.a., steptol in stats::nlm()) is too large.

  • 4: iteration limit exceeded; increase iterlim.

  • 5: maximum step size stepmax exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or stepmax is too small.

Examples

library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

est1 <- est.incidence(
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA")
)

summary(est1)

Summary Method for "seroincidence.by" Objects

Description

Calculate seroincidence from output of the seroincidence calculator est.incidence.by().

Usage

## S3 method for class 'seroincidence.by'
summary(
  object,
  confidence_level = 0.95,
  showDeviance = TRUE,
  showConvergence = TRUE,
  ...
)

Arguments

Value

A summary.seroincidence.by object, which is a tibble::tibble, with the following columns:

• incidence.rate maximum likelihood estimate of lambda (seroincidence)

• CI.lwr lower confidence bound for lambda

• CI.upr upper confidence bound for lambda

• Deviance (included if showDeviance = TRUE) Negative log likelihood (NLL) at estimated (maximum likelihood) lambda)

  • nlm.convergence.code (included if showConvergence = TRUE) Convergence information returned by stats::nlm() The object also has the following metadata (accessible through base::attr()):

• antigen_isos Character vector with names of input antigen isotypes used in est.incidence.by()

• Strata Character with names of strata used in est.incidence.by()

Examples

library(dplyr)

xs_data <-
  sees_pop_data_pk_100

curve <-
  typhoid_curves_nostrat_100 %>%
  filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG"))

noise <-
  example_noise_params_pk

# estimate seroincidence
est2 <- est.incidence.by(
  strata = c("catchment"),
  pop_data = xs_data,
  curve_params = curve,
  noise_params = noise,
  antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
  #num_cores = 8 # Allow for parallel processing to decrease run time
)

# calculate summary statistics for the seroincidence object
summary(est2)

Small example of antibody response curve parameters for typhoid

Description

A subset of data from the SEES study, for examples and testing.

Usage

typhoid_curves_nostrat_100

Format

typhoid_curves_nostrat_100

A curve_params object (from as_curve_params()) with 500 rows and 7 columns:

antigen_iso

which antigen and isotype are being measured (data is in long format)

iter

MCMC iteration

y0

Antibody concentration at t = 0 (start of active infection)

y1

Antibody concentration at t = t1 (end of active infection)

t1

Duration of active infection

alpha

Antibody decay rate coefficient

r

Antibody decay rate exponent parameter

Source

https://osf.io/rtw5k

Warn about missing stratifying variables in a dataset

Description

Warn about missing stratifying variables in a dataset

Usage

warn.missing.strata(data, strata, dataname)

Arguments

Value

a character() vector of the subset of stratifying variables that are present in pop_data

Examples

## Not run: 
expected_strata <- data.frame(Species = "banana", type = "orchid")

warn.missing.strata(iris, expected_strata, dataname = "iris")

## End(Not run)