Type:	Package
Title:	Model Infectious Disease Parameters from Serosurveys
Version:	1.3.0
Description:	An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.1.0)
RoxygenNote:	7.3.3
Imports:	dplyr, tidyr, janitor, ggplot2, locfit, purrr, stringr, magrittr, methods, mgcv, mixdist, scam, mvtnorm, patchwork, assertthat, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.4.0), boot, pROC, stats4, rlang (≥ 1.1.0)
Suggests:	covr, knitr, rmarkdown, bookdown, testthat (≥ 3.0.0)
Collate:	'data.R' 'fractional_polynomial_models.R' 'polynomial_models.R' 'utils.R' 'compare_models.R' 'correct_prevalence.R' 'weibull_model.R' 'farrington_model.R' 'nonparametric.R' 'semiparametric_models.R' 'mixture_model.R' 'hierarchical_bayesian_model.R' 'to_titer.R' 'serosv.R' 'stanmodels.R' 'plots.R' 'compute_ci.R' 'age_time_model.R' 'predict.R' 'print.R'
Config/testthat/edition:	3
URL:	https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv
VignetteBuilder:	knitr
Biarch:	true
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)
SystemRequirements:	GNU make
BugReports:	https://github.com/OUCRU-Modelling/serosv/issues
NeedsCompilation:	yes
Packaged:	2026-04-07 12:44:58 UTC; anhptq
Author:	Anh Phan Truong Quynh [aut, cre], Nguyen Pham Nguyen The [aut], Long Bui Thanh [aut], Tuyen Huynh [aut], Thinh Ong [aut], Marc Choisy [aut]
Maintainer:	Anh Phan Truong Quynh <anhptq@oucru.org>
Repository:	CRAN
Date/Publication:	2026-04-07 13:20:02 UTC

serosv: model infectious disease parameters

Description

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) doi:10.1007/978-1-4614-4072-7.

Author(s)

Maintainer: Anh Phan Truong Quynh anhptq@oucru.org (ORCID)

Authors:

• Nguyen Pham Nguyen The nguyenpnt@oucru.org (ORCID)

• Long Bui Thanh

• Tuyen Huynh tuyenhn@oucru.org

• Thinh Ong thinhop@oucru.org (ORCID)

• Marc Choisy mchoisy@oucru.org (ORCID)

See Also

Useful links:

• https://oucru-modelling.github.io/serosv/

• https://github.com/OUCRU-Modelling/serosv

• Report bugs at https://github.com/OUCRU-Modelling/serosv/issues

Visualize positive threshold at different dilution factors

Description

Visualize positive threshold at different dilution factors

Usage

add_thresholds(dilution_factors, positive_threshold = 0.1, shift_text = 0.15)

Arguments

Age-time varying seroprevalence

Description

Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or birth cohort) - specific seroprevalence.

Usage

age_time_model(
  data,
  age_col = "age",
  status_col = "status",
  pos_col = "pos",
  tot_col = "tot",
  time_col = "date",
  grouping_col = "group",
  age_correct = F,
  le = 512,
  ci = 0.95,
  monotonize_method = "pava"
)

Arguments

Value

a list of class time_age_model with 4 items

Generate table of metrics for model comparison

Description

Generate table of metrics for model comparison

Usage

compare_models(data, method = "AIC/BIC", ...)

Arguments

Details

Built-in comparison methods include:

• computing AIC and BIC, which returns AIC, BIC values of the model if available

• cross validation (perform k-fold validation), which returns MSE and logloss (negative log Binomial likelihood) for aggregated data, or AUC and logloss (negative log Bernoulli likelihood) for linelisting data

Value

a data.frame with the following columns

Examples

comparison_table <- suppressWarnings(
  compare_models(
    data = hav_bg_1964,
    method = "CV",
    polynomial_mod = ~polynomial_model(.x, k=1),
    penalized_spline = penalized_spline_model,
    farrington = ~farrington_model(.x, start=list(alpha=0.3,beta=0.1,gamma=0.03))
  )
)
# view table of metrics
comparison_table
# view the model fitted with the whole dataset
comparison_table$plots

Compute confidence interval for time age model

Description

Compute confidence interval for time age model

Usage

## S3 method for class 'age_time_model'
compute_ci(x, ci = 0.95, le = 100, ...)

Arguments

Value

confidence interval dataframe with n_group x 3 cols, the columns are 'group', 'sp_df', 'foi_df'

Compute confidence interval for a model of serosv

Description

Compute confidence interval for a model of serosv

Usage

## Default S3 method:
compute_ci(x, ci = 0.95, le = 100, ...)

Arguments

Value

confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Compute confidence interval for fractional polynomial model

Description

Compute confidence interval for fractional polynomial model

Usage

## S3 method for class 'fp_model'
compute_ci(x, ci = 0.95, le = 100, ...)

Arguments

Value

confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Compute 95% credible interval for hierarchical Bayesian model

Description

Compute 95% credible interval for hierarchical Bayesian model

Usage

## S3 method for class 'hierarchical_bayesian_model'
compute_ci(x, ...)

Arguments

Value

list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Compute confidence interval for local polynomial model

Description

Compute confidence interval for local polynomial model

Usage

## S3 method for class 'lp_model'
compute_ci(x, ci = 0.95, ...)

Arguments

Value

confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Compute confidence interval for mixture model

Description

Compute confidence interval for mixture model

Usage

## S3 method for class 'mixture_model'
compute_ci(x, ci = 0.95, ...)

Arguments

Value

list of confidence interval for susceptible and infected. Each confidence interval is a list with 2 items for lower and upper bound of the interval.

Compute confidence interval for penalized_spline_model

Description

Compute confidence interval for penalized_spline_model

Usage

## S3 method for class 'penalized_spline_model'
compute_ci(x, ci = 0.95, ...)

Arguments

Value

list of confidence interval for seroprevalence and foi Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Compute confidence interval for Weibull model

Description

Compute confidence interval for Weibull model

Usage

## S3 method for class 'weibull_model'
compute_ci(x, ci = 0.95, ...)

Arguments

Value

confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Estimate the true sero prevalence using Frequentist/Bayesian estimation

Description

Estimate the true sero prevalence using Frequentist/Bayesian estimation

Usage

correct_prevalence(
  data,
  bayesian = TRUE,
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status",
  init_se = 0.95,
  init_sp = 0.8,
  study_size_se = 1000,
  study_size_sp = 1000,
  chains = 1,
  warmup = 1000,
  iter = 2000
)

Arguments

Value

a list of 3 items

Examples

data <- rubella_uk_1986_1987
correct_prevalence(data)

Estimate force of infection

Description

Estimate force of infection

Usage

est_foi(t, sp)

Arguments

Value

computed foi vector

Estimate seroprevalence and FOI from a fixed mixture model

Description

Estimate age-specific seroprevalence and FOI given a fitted mixture model (generated by [serosv::mixture_model()])

Usage

estimate_from_mixture(
  age,
  antibody_level,
  threshold_status = NULL,
  mixture_model,
  s = "ps",
  sp = 83,
  monotonize = TRUE
)

Arguments

Details

Antibody level (denoted Z) is modeled using a 2-component Gaussian mixture model. Each component Z_j (j \in \{I, S\}) represents the antibody level of the latent Infected and Susceptible sub-populations, following density f_j(z_j|\theta_j)

Let \pi_{\text{TRUE}}(a) denotes the age-dependent mixing probability (i.e., the true prevalence), the density of the mixture is formulated as

f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)

The mean E(Z|a) thus equals

\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I

From which true prevalence can be computed as

\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}

And FOI can then be inferred as

\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}

Function [serosv::mixture_model()] fits antibody level data to f_S(z_S|\theta_S) and f_I(z_I|\theta_I)

Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \mu(a) and infer the estimation for \pi_{\text{TRUE}}(a) and \lambda_{TRUE}

Refer to section 11.3. of the the book by Hens et al. (2012) for further details.

Value

a list of class estimated_from_mixture with the following items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[mgcv::gam()] for more information about the fitted gam object

The Farrington (1990) model.

Description

Fit age-stratified seroprevalence data using the Farrington (1990) model, which assumes the force of infection increases linearly with age and subsequently decreases exponentially.

Usage

farrington_model(
  data,
  start,
  fixed = list(),
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status"
)

Arguments

Details

The force of infection is defined as followed

\lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma

Where \gamma is called the long term residual for FOI, as a \rightarrow \infty , \lambda (a) \rightarrow \gamma

The seroprevalence can thus be estimated using the non-linear model

\pi(a) = 1 - exp\{ \frac{\alpha}{\beta}ae^{-\beta a} + \frac{1}{\beta}(\frac{\alpha}{\beta} - \gamma)(e^{-\beta a} - 1) -\gamma a \}

Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.

Value

a list of class farrington_model with 5 items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[stats4::mle()] for more information on the fitted mle object

Examples

df <- rubella_uk_1986_1987
model <- farrington_model(
  df,
  start=list(alpha=0.07,beta=0.1,gamma=0.03)
  )
plot(model)

Returns the powers of the fractional polynomial model which has the lowest deviance score.

Description

Return the best powers for a given degree

Usage

find_best_fp_powers(data, p, mc, degree, link = "logit")

Arguments

Value

list of 3 elements:

A fractional polynomial model.

Description

Fractional polynomial model is a generalization of polynomial models where the power of the terms can be fractions, allowing more flexibility and better fit for data where asymptotic behavior is expected.

Usage

fp_model(
  data,
  p,
  monotonic = FALSE,
  link = "logit",
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status"
)

Arguments

Details

Instead of a polynomial, the linear predictor is now defined as

\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)

Where m is an integer, p_1 \le p_2 \le... \le p_m is a sequence of powers, and H_i(a) is a transformation given by

H_i = \begin{cases} a^{p_i} & \text{ if } p_i \neq p_{i-1}, \\ H_{i-1}(a) \times log(a) & \text{ if } p_i = p_{i-1}, \end{cases}

Refers to section 6.2. of the the book by Hens et al. (2012) for further details.

Value

a list of class fp_model with 5 items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[stats::glm()] for more information on glm object

[polynomial_models()]

Examples

df <- hav_be_1993_1994
model <- fp_model(
  df,
  p=c(1.5, 1.6), link="cloglog")
plot(model)

Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated)

Description

A study of the prevalence of HAV antibodies conducted in the Flemish Community of Belgium in 1993 and early 1994

Usage

hav_be_1993_1994

Format

A data frame with 3 variables:

age

Age group

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

Source

Beutels, M., Van Damme, P., Aelvoet, W. et al. Prevalence of hepatitis A, B and C in the Flemish population. Eur J Epidemiol 13, 275-280 (1997). doi:10.1023/A:1007393405966

Examples

with(hav_be_1993_1994,
  plot(
    age, pos / tot,
    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
  )
)

Hepatitis A serological data from Belgium in 2002 (line listing)

Description

A subset of the serological dataset of Varicella-Zoster Virus (VZV) and Parvovirus B19 in Belgium where only individuals living in Flanders were selected

Usage

hav_be_2002

Format

A data frame with 2 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

Source

Thiry, N., Beutels, P., Shkedy, Z. et al. The seroepidemiology of primary varicella-zoster virus infection in Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2

Examples

library(dplyr)
df <- hav_be_2002 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())

with(
  df,
  plot(
    age, pos / tot,
    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
  )
)

Hepatitis A serological data from Bulgaria in 1964 (aggregated)

Description

A cross-sectional survey conducted in 1964 in Bulgaria. Samples were collected from schoolchildren and blood donors.

Usage

hav_bg_1964

Format

A data frame with 3 variables:

age

Age group

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

Source

Keiding, Niels. "Age-Specific Incidence and Prevalence: A Statistical Perspective." Journal of the Royal Statistical Society. Series A (Statistics in Society) 154, no. 3 (1991): 371-412. doi:10.2307/2983150

Examples

with(
  hav_bg_1964,
  plot(
    age, pos / tot,
    pty = "s", cex = 0.6 * sqrt(tot), pch = 16, xlab = "age",
    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
  )
)

Hepatitis B serological data from Russia in 1999 (aggregated)

Description

A seroprevalence study conducted in St. Petersburg (more information in the book)

Usage

hbv_ru_1999

Format

A data frame with 4 variables:

age

Age group

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

gender

Gender of cohort (unsure what 1 and 2 means)

Source

Mukomolov, S., L. Shliakhtenko, I. Levakova, and E. Shargorodskaya. Viral hepatitis in Russian federation. An analytical overview. Technical Report 213 (3), 3rd edn. St Petersburg Pasteur Institute, St Petersburg, 2000.

Examples

library(dplyr)
hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1
hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
  hbv_ru_1999$age[hbv_ru_1999$age > 40] / 5
) * 5
df <- hbv_ru_1999 %>%
  group_by(age) %>%
  summarise(pos = sum(pos), tot = sum(tot))

plot(
  df$age, df$pos / df$tot,
  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age",
  ylab = "seroprevalence", xlim = c(0, 72)
)

Hepatitis C serological data from Belgium in 2006 (line listing)

Description

A study of HCV infection among injecting drug users. All injecting drug users were interviewed by means of a standardized face-to-face interview and information on their socio-demographic status, drug use history, drug use, and related risk behavior was recorded

Usage

hcv_be_2006

Format

A data frame with 3 variables:

dur

Duration of injection/Exposure time (years)

seropositive

If the individual is seropositive or not

Source

Mathei, C., Shkedy, Z., Denis, B., Kabali, C., Aerts, M., Molenberghs, G., Van Damme, P. and Buntinx, F. (2006), Evidence for a substantial role of sharing of injecting paraphernalia other than syringes/needles to the spread of hepatitis C among injecting drug users. Journal of Viral Hepatitis, 13: 560-570. doi:10.1111/j.1365-2893.2006.00725.x

Examples

library(dplyr)
# snapping age to aggregated age group
# (credit: https://stackoverflow.com/a/12861810)
groups <- c(0.5:24.5)
range <- 0.5
low <- findInterval(hcv_be_2006$dur, groups)
high <- low + 1
low_diff <- hcv_be_2006$dur - groups[ifelse(low == 0, NA, low)]
high_diff <- groups[ifelse(high == 0, NA, high)] - hcv_be_2006$dur
mins <- pmin(low_diff, high_diff, na.rm = TRUE)
pick <- ifelse(!is.na(low_diff) & mins == low_diff, low, high)
hcv_be_2006$dur <- ifelse(
  mins <= range + .Machine$double.eps, groups[pick], hcv_be_2006$dur
)
hcv_be_2006 <- hcv_be_2006 %>%
  group_by(dur) %>%
  summarise(tot = n(), pos = sum(seropositive))

with(hcv_be_2006,
  plot(
    dur, pos / tot,
    cex = 0.42 * sqrt(tot), pch = 16,
    xlab = "duration of injection (years)",
    ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
  )
)

Hierarchical Bayesian Model

Description

Fit age-stratified seroprevalence to parametric hierarchical Bayesian models. Supported models including Farrington model (2 and 3 parameters variants) and Log Logistic model

Usage

hierarchical_bayesian_model(
  data,
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status",
  type = "far3",
  chains = 1,
  warmup = 1500,
  iter = 5000
)

Arguments

Details

Consider a model for prevalence that has a parametric form \pi(a_i, \alpha) where \alpha is a parameter vector

Under a Bayesian framework, we can constraint the parameter space of the prior distribution P(\alpha) to achieve monotonicity of the posterior distribution P(\pi_1, \pi_2, ..., \pi_m|y,n)

Where:

- n = (n_1, n_2, ..., n_m) and n_i is the sample size at age a_i

- y = (y_1, y_2, ..., y_m) and y_i is the number of infected individual from the n_i sampled subjects

For Farrington model with 3 parameters, prevalence is formulated as follow

\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} + \frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}

The likelihood model is defined as y_i \sim Bin(n_i, \pi_i), \text{ for } i = 1,2,3,...m

The constraint on the parameter space can be incorporated by assuming truncated normal distribution for the components of \alpha, \alpha = (\alpha_1, \alpha_2, \alpha_3) in \pi_i = \pi(a_i,\alpha)

The flat hyperpriors are defined as \mu_j \sim \mathcal{N}(0, 10000) and \tau^{-2}_j \sim \Gamma(100,100)

For Farrington model with 2 parameters, it is equivalent to the previous model with \alpha_3 = 0

For Log logistic model, seroprevalence is instead defined as

\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0

The likelihood is similarly defined as y_i \sim Bin(n_i, \pi_i))

The prior model of \alpha_1 is specified as \alpha_1 \sim \text{truncated } \mathcal{N}(\mu_1, \tau_1) with flat hyperpriors as in Farrington model

\beta is constrained to be positive by specifying \alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)

Refer to section Chapter 10.3 of the the book by Hens et al. (2012) for further details.

Value

a list of class hierarchical_bayesian_model with 6 items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

Examples

df <- mumps_uk_1986_1987
model <- hierarchical_bayesian_model(df, type="far3")
model$info
plot(model)

A local polynomial model.

Description

Fit the age-specific seroprevalence to a local polynomial model, where the linear predictor is approximated locally at one particular age.

Usage

lp_model(
  data,
  kern = "tcub",
  nn = 0,
  h = 0,
  deg = 2,
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status"
)

Arguments

Details

Consider a linear predictor \eta(a) approximated locally at one particular value a_0.

For a general degree p, the linear predictor for a neighbor of a_0, labeled a_i is equivalent to the Taylor approximation

\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) + \frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + \cdots + \frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p

\eta(a_i) can be estimated by maximizing

\Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2 \cdots + \beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)

The estimator for the k-th derivative of \eta(a_0), for k = 0,1,\cdots,p (degree of local polynomial) is thus:

\hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0)

The estimator for the prevalence at age a_0 is then given by

\hat{\pi}(a_0) = g^{-1}\{ \hat{\beta}_0(a_0) \}

Where g is the link function

The estimator for the force of infection at age a_0 by assuming p \ge 1 is as followed

\hat{\lambda}(a_0) = \hat{\beta}_1(a_0) \delta \{ \hat{\beta}_0 (a_0) \}

Where \delta \{ \hat{\beta}_0(a_0) \} = \frac{dg^{-1} \{ \hat{\beta}_0(a_0) \} } {d\hat{\beta}_0(a_0)}

Refer to section 7.1 and 7.2. of the the book by Hens et al. (2012) for further details.

Value

a list of class lp_model with 6 items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[locfit::locfit()] for more information on the fitted locfit object

Examples

df <- mumps_uk_1986_1987
model <- lp_model(
  df,
  nn=0.7, kern="tcub"
  )
plot(model)

Fit a mixture model to classify serostatus

Description

Fit the antibody level data to a 2-component Gaussian mixture model

Usage

mixture_model(
  antibody_level,
  breaks = 40,
  pi = c(0.2, 0.8),
  mu = c(2, 6),
  sigma = c(0.5, 1)
)

Arguments

Details

Antibody level (denoted Z) is modeled using a 2-component Gaussian mixture model. Each component Z_j (j \in \{I, S\}) represents the antibody level of the latent Infected and Susceptible sub-populations, following density f_j(z_j|\theta_j)

Let \pi_{\text{TRUE}}(a) denotes the age-dependent mixing probability (i.e., the true prevalence), the density of the mixture is formulated as

f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)

The mean E(Z|a) thus equals

\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I

From which true prevalence can be computed as

\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}

And FOI can then be inferred as

\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}

Function [serosv::mixture_model()] fits antibody level data to f_S(z_S|\theta_S) and f_I(z_I|\theta_I)

Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \mu(a) and infer the estimation for \pi_{\text{TRUE}}(a) and \lambda_{TRUE}

Refer to section 11.3. of the the book by Hens et al. (2012) for further details.

Value

a list of class mixture_model with the following items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

Examples

df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,]
data <- df$VZVmIUml[order(df$age)]
model <- mixture_model(antibody_level = data)
model$info
plot(model)

Mumps serological data from the UK in 1986 and 1987 (aggregated)

Description

a large survey of prevalence of antibodies to mumps and rubella viruses in the UK. The survey, covering subjects from 1 to over 65 years of age, provides information on the prevalence of antibody by age

Usage

mumps_uk_1986_1987

Format

A data frame with 3 variables:

age

Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

Source

Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance of antibody to measles, mumps, and rubella by age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770

Examples

with(mumps_uk_1986_1987,
  plot(age, pos / tot,
    cex = 0.1 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
    xlim = c(0, 45), ylim = c(0, 1)
  )
)

Parvo B19 serological data from Belgium from 2001-2003 (line listing)

Description

A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on large representative national serum banks in Belgium, England and Wales, Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and were obtained from residual sera submitted for routine laboratory testing.

Usage

parvob19_be_2001_2003

Format

A data frame with 5 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

year

Year surveyed

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

library(dplyr)
df <- parvob19_be_2001_2003 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 82), ylim = c(0, 1)
)

Parvo B19 serological data from England and Wales in 1996 (line listing)

Description

A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on large representative national serum banks in Belgium, England and Wales, Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and were obtained from residual sera submitted for routine laboratory testing.

Usage

parvob19_ew_1996

Format

A data frame with 5 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

year

Year surveyed

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

# Note: This figure will look different to that of in the book, since we
# believe that the original authors has made some errors in specifying
# the sample size of the dots.

library(dplyr)
df <- parvob19_ew_1996 %>%
  mutate(age = round(age)) %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 82), ylim = c(0, 1)
)

Parvo B19 serological data from Finland from 1997-1998 (line listing)

Description

A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on large representative national serum banks in Belgium, England and Wales, Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and were obtained from residual sera submitted for routine laboratory testing.

Usage

parvob19_fi_1997_1998

Format

A data frame with 5 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

year

Year surveyed

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

# Note: This figure will look different to that of in the book, since we
# believe that the original authors has made some errors in specifying
# the sample size of the dots.

library(dplyr)
df <- parvob19_fi_1997_1998 %>%
  mutate(age = round(age)) %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.4 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 82), ylim = c(0, 1)
)

Parvo B19 serological data from Italy from 2003-2004 (line listing)

Description

A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on large representative national serum banks in Belgium, England and Wales, Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and were obtained from residual sera submitted for routine laboratory testing.

Usage

parvob19_it_2003_2004

Format

A data frame with 5 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

year

Year surveyed

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

library(dplyr)
df <- parvob19_it_2003_2004 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 82), ylim = c(0, 1)
)

Parvo B19 serological data from Poland from 1995-2004 (line listing)

Description

A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on large representative national serum banks in Belgium, England and Wales, Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and were obtained from residual sera submitted for routine laboratory testing.

Usage

parvob19_pl_1995_2004

Format

A data frame with 5 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

year

Year surveyed

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

# Note: This figure will look different to that of in the book, since we
# believe that the original authors has made some errors in specifying
# the sample size of the dots.

library(dplyr)
df <- parvob19_pl_1995_2004 %>%
  mutate(age = round(age)) %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 82), ylim = c(0, 1)
)

Monotonize seroprevalence

Description

Monotonize seroprevalence

Usage

pava(pos = pos, tot = rep(1, length(pos)))

Arguments

Value

computed list of 2 items pai1 for original values and pai2 for monotonized value

Penalized Spline model

Description

Fit age-specific seroprevalence to a semi-parametric model where predictor is modeled with penalized splines. The penalized splines can be estimated by either (1) penalized likelihood framework or (2) mixed model framework

Usage

penalized_spline_model(
  data,
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status",
  s = "bs",
  link = "logit",
  framework = "pl",
  sp = NULL
)

Arguments

Details

In the semi-parametric model, the predictor is formulated as a penalized spline with truncated power basis functions of degree p and fixed knots \kappa_1,\cdots, \kappa_k as followed

\eta(a_i) = \beta_0 + \beta_1a_i + \cdots + \beta_p a_i^p + \Sigma_{k=1}^ku_k(a_i - \kappa_k)^p_+

Where:

(a_i - \kappa_k)^p_+ = \begin{cases} 0, & a_i \le \kappa_k \\ (a_i - \kappa_k)^p, & a_i > \kappa_k \end{cases}

FOI can then be derived by

\hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, \cdots, p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))

Where \delta(.) is determined by the link function used in the model

In matrix annotation, the mean structure model for \eta(a_i) becomes

\eta = \textbf{X}\beta + \textbf{Zu}

Where \eta = [\eta(a_i) \cdots \eta(a_N) ]^T, \beta = [\beta_0 \beta_1 \cdots \beta_p]^T, and \textbf{u} = [u_1 u_2 \cdots u_k]^T are the regression with corresponding design matrices

\textbf{X} = \begin{bmatrix} 1 & a_1 & a_1^2 & \cdots & a_1^p \\ 1 & a_2 & a_2^2 & \cdots & a_2^p \\ \vdots & \vdots & \vdots & \dots & \vdots \\ 1 & a_N & a_N^2 & \cdots & a_N^p \end{bmatrix}, \textbf{Z} = \begin{bmatrix} (a_1 - \kappa_1 )_+^p & (a_1 - \kappa_2 )_+^p & \dots & (a_1 - \kappa_k)_+^p \\ (a_2 - \kappa_1 )_+^p & (a_2 - \kappa_2 )_+^p & \dots & (a_2 - \kappa_k)_+^p \\ \vdots & \vdots & \dots & \vdots \\ (a_N - \kappa_1 )_+^p & (a_N - \kappa_2 )_+^p & \dots & (a_N - \kappa_k)_+^p \end{bmatrix}

Under penalized likelihood framework, the model is fitted by maximizing the following likelihood

\phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) - \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2 \begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}

Where:

• X\beta + Zu is the predictor

• D is a known semi-definite penalty matrix

• y is the response vector

• \mathbf{1} the unit vector, c(.) is determined by the link function used

• \lambda is the smoothing parameter (larger values -> smoother curves)

• \phi is the overdispersion parameter and equals 1 if there is no overdispersion

Under the mixed model framework, the model instead treats the coefficients \textbf{u} in the likelihood formulation as random effects with \textbf{u} \sim N(\textbf{0}, \boldsymbol{\sigma}^2_u \textbf{I})

Refer to section 8.1 and 8.2 of the the book by Hens et al. (2012) for further details.

Value

a list of class penalized_spline_model with 6 attributes

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[mgcv::gam()], [mgcv::gamm()] for more information the fitted gam and gamm model

Examples

data <- parvob19_be_2001_2003
data$status <- data$seropositive
model <- penalized_spline_model(data, framework="glmm")
model$info$gam
plot(model)

Plot output for age_time_model

Description

Plot output for age_time_model

Usage

## S3 method for class 'age_time_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for result of estimate_from_mixture

Description

plot() overloading for result of estimate_from_mixture

Usage

## S3 method for class 'estimate_from_mixture'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for Farrington model

Description

plot() overloading for Farrington model

Usage

## S3 method for class 'farrington_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for fractional polynomial model

Description

plot() overloading for fractional polynomial model

Usage

## S3 method for class 'fp_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for hierarchical_bayesian_model

Description

plot() overloading for hierarchical_bayesian_model

Usage

## S3 method for class 'hierarchical_bayesian_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for local polynomial model

Description

plot() overloading for local polynomial model

Usage

## S3 method for class 'lp_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for mixture model

Description

plot() overloading for mixture model

Usage

## S3 method for class 'mixture_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for penalized spline

Description

plot() overloading for penalized spline

Usage

## S3 method for class 'penalized_spline_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for polynomial model

Description

plot() overloading for polynomial model

Usage

## S3 method for class 'polynomial_model'
plot(x, ...)

Arguments

Value

ggplot object

plot() overloading for Weibull model

Description

plot() overloading for Weibull model

Usage

## S3 method for class 'weibull_model'
plot(x, ...)

Arguments

Value

ggplot object

Plot output for corrected_prevalence

Description

Plot output for corrected_prevalence

Usage

plot_corrected_prev(x, y = NULL, facet = FALSE)

Arguments

Value

ggplot object

Plotting GCV values with respect to different nn-s and h-s parameters.

Description

Refers to section 7.2.

Usage

plot_gcv(age, pos, tot, nn_seq, h_seq, kern = "tcub", deg = 2)

Arguments

Value

plot of gcv value

Examples

df <- mumps_uk_1986_1987
plot_gcv(
  df$age, df$pos, df$tot,
  nn_seq = seq(0.2, 0.8, by=0.1),
  h_seq = seq(5, 25, by=1)
)

Visualize standard curves for each plate

Description

Visualize standard curves for each plate

Usage

plot_standard_curve(
  x,
  facet = TRUE,
  xlab = "log10(concentration)",
  ylab = "Optical density",
  datapoint_size = 2
)

Arguments

Quality control plot

Description

Visualize for each sample, whether titer estimates can be computed at the tested dilutions.

Usage

plot_titer_qc(x, n_plates = 18, n_samples = 22, n_dilutions = 3)

Arguments

Details

Each sample is represented by a 'n_estimates x n_dilutions' grid where cell color indicate estimate availability (green = estimate available, orange = result too low, red = result too high)

These sample grids are arranged in columns where each column represent samples from a plate

Polynomial models

Description

Fit age-stratified seroprevalence data to serocatalytic models formulated as polynomials.

Usage

polynomial_model(
  data,
  k,
  link = "log",
  age_col = "age",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status"
)

Arguments

Details

The seroprevalence is assumed to follow the general format

\pi(a) = 1 - e^{-\Sigma_{i=1}^k \beta_i a^i}

Which implies the force of infection to be \lambda(a) = \Sigma_{i=1}^k \beta_i i a^{i-1}

Where:

- \pi is the seroprevalence at age a

- a is the variable age

- k is the degree of the polynomial

The seroprevalence \pi(a) is fitted using a GLM with log link with the linear predictor \eta(a) = \Sigma_{i=1}^k \beta_i a^{i}

Muench (1934) model is equivalent to a degree 1 (k=1) linear predictor

Griffith model is equivalent to a degree 2 (k=2) linear predictor

Grenfell & Anderson (1985) suggested a higher order polynomials (k \geq 3)

Refer to section 6.1.1. of the the book by Hens et al. (2012) for further details.

Value

a list of class polynomial_model with 5 items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of Age-Related Rates of Infection from Case Notifications and Serological Data.” The Journal of Hygiene 95 (2): 419–36. doi:10.1017/s0022172400062859.

Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the Catalytic Curve.” Journal of the American Statistical Association 29 (185): 25–38. doi:10.1080/01621459.1934.10502684.

Examples

data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
aggregated <- transform_data(data, stratum_col = "age", status_col="seropositive")

# fit with aggregated data
model <- polynomial_model(aggregated, k = 1)
# fit with linelisting data
model <- polynomial_model(data,
    status_col = "seropositive",
    k = 1)
plot(model)

Predict from the age_time_mdoel

Description

Predict from the age_time_mdoel

Usage

## S3 method for class 'age_time_model'
predict(object, newdata, modtype = "monotonized", ...)

Arguments

Value

confidence interval dataframe with n_group x 3 cols, the columns are 'group', 'sp_df', 'foi_df'

Prediction for serosv Farrington model

Description

Prediction for serosv Farrington model

Usage

## S3 method for class 'farrington_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

Prediction for serosv fractional polynomial model

Description

Prediction for serosv fractional polynomial model

Usage

## S3 method for class 'fp_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

See Also

[stats::predict.glm()] for more information on the predict function

Predict from an hierarchical bayesian model

Description

Predict from an hierarchical bayesian model

Usage

## S3 method for class 'hierarchical_bayesian_model'
predict(object, newdata = NULL, ...)

Arguments

Value

list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval

Prediction for serosv local polynomial model

Description

Prediction for serosv local polynomial model

Usage

## S3 method for class 'lp_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

Prediction for serosv penalized spline model

Description

Prediction for serosv penalized spline model

Usage

## S3 method for class 'penalized_spline_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

See Also

[mgcv::predict.gam()] for more information on the predict function

Prediction for serosv polynomial model

Description

A wrapper of predict.glm for direct prediction from polynomial_model object

Usage

## S3 method for class 'polynomial_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

See Also

[stats::predict.glm()] for more information on the predict function

Prediction for serosv Weibull model

Description

Prediction for serosv Weibull model

Usage

## S3 method for class 'weibull_model'
predict(object, newdata = NULL, ...)

Arguments

Value

prediction output

See Also

[stats::predict.glm()] for more information on the predict function

Rubella - Mumps data from the UK (aggregated)

Description

Rubella - Mumps data from the UK (aggregated)

Usage

rubella_mumps_uk

Format

A data frame with 5 variables:

age

Age group

NN

Number of individuals negative to rubella and mumps

NP

Number of individuals negative to rubella and positive to mumps

PN

Number of individuals positive to rubella and negative to mumps

PP

Number of individuals positive to rubella and mumps

Source

Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance of antibody to measles, mumps, and rubella by age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770

Rubella serological data from the UK in 1986 and 1987 (aggregated)

Description

Prevalence of rubella in the UK, obtained from a large survey of prevalence of antibodies to both mumps and rubella viruses.

Usage

rubella_uk_1986_1987

Format

A data frame with 3 variables:

age

Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

Source

Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance of antibody to measles, mumps, and rubella by age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770

Examples

with(rubella_uk_1986_1987,
  plot(age, pos / tot,
    cex = 0.2 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
    xlim = c(0, 45), ylim = c(0, 1)
  )
)

Helper to adjust styling of a plot

Description

Helper to adjust styling of a plot

Usage

set_plot_style(
  sero = "blueviolet",
  ci = "royalblue1",
  foi = "#fc0328",
  sero_line = "solid",
  foi_line = "dashed",
  xlabel = "Age"
)

Arguments

Value

list of updated aesthetic values

Standardize raw serological test data for titer conversion

Description

Validate and prepare raw serological test results for use with 'to_titer()'

Usage

standardize_data(
  df,
  plate_id_col = "PLATE_ID",
  id_col = "SAMPLE_ID",
  result_col = "RESULT",
  dilution_fct_col = "DILUTION_FACTORS",
  antitoxin_label = "Anti_toxin",
  negative_col = "^NEGATIVE_*"
)

Arguments

Value

a standardized data.frame that can be passed to 'to_titer()'

Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)

Description

A study of tuberculosis conducted in the Netherlands. Schoolchildren, aged between 6 and 18 years, were tested using the tuberculin skin test.

Usage

tb_nl_1966_1973

Format

A data frame with 5 variables:

age

Age group

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

gender

Gender of cohort (unsure what 0 and 1 means)

birthyr

Birth year of cohort

Source

Nagelkerke, N., Heisterkamp, S., Borgdorff, M., Broekmans, J. and Van Houwelingen, H. (1999), Semi-parametric estimation of age-time specific infection incidence from serial prevalence data. Statist. Med., 18: 307-320. doi:10.1002/(SICI)1097-0258(19990215)18:3<307::AID-SIM15>3.0.CO;2-Z

Examples

with(tb_nl_1966_1973,
  plot(age, pos / tot,
    pch = 16, cex = 0.01 * sqrt(tot), xlab = "age",
    ylab = "prevalence", xlim = c(6, 18)
  )
)

with(tb_nl_1966_1973,
  plot(birthyr, pos / tot,
    pch = 16, cex = 0.01 * sqrt(tot), xlab = "year", ylab = "prevalence"
  )
)

Convert assay readings to titers

Description

to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by fitting a calibrating model

Usage

to_titer(
  df,
  model = "4PL",
  positive_threshold = NULL,
  ci = 0.95,
  negative_control = TRUE
)

Arguments

Value

a data.frame with 8 columns

Aggregate data

Description

Generate a dataframe with 't', 'pos' and 'tot' columns from 't' and 'seropositive' vectors.

Usage

transform_data(data, stratum_col = "age", status_col = "status")

Arguments

Value

dataframe in aggregated format

Examples

df <- hcv_be_2006
hcv_df <- transform_data(df, stratum_col="dur", status_col="seropositive")
hcv_df

VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated)

Description

Age-specific seroprevalence of VZV antibodies, assessed in Flanders (Belgium) between October 1999 and April 2000. This population was stratified by age in order to obtain approximately 100 observations per age group.

Usage

vzv_be_1999_2000

Format

A data frame with 3 variables:

age

Age group

pos

Number of seropositive individuals

tot

Total number of individuals surveyed

Source

Thiry, N., Beutels, P., Shkedy, Z. et al. The seroepidemiology of primary varicella-zoster virus infection in Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2

Examples

with(vzv_be_1999_2000,
  plot(age, pos / tot,
    cex = 0.1 * sqrt(tot), pch = 19, xlab = "age", ylab = "seroprevalence",
    xlim = c(0, 45), ylim = c(0, 1)
  )
)

VZV serological data from Belgium from 2001-2003 (line listing)

Description

The survey is the same as the one used to study the seroprevalence of parvovirus B19 in Belgium, as described above.

Usage

vzv_be_2001_2003

Format

A data frame with 4 variables:

age

Age of individual

seropositive

If the individual is seropositive or not

gender

Gender of individual

VZVmIUml

VZV milli international units per ml

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

Examples

library(dplyr)
df <- vzv_be_2001_2003 %>%
  mutate(age = round(age)) %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.1 * sqrt(df$tot), pch = 19, xlab = "age", ylab = "seroprevalence",
  xlim = c(0, 45), ylim = c(0, 1)
)

VZV and Parvovirus B19 serological data in Belgium (line listing)

Description

VZV and Parvovirus B19 serological data in Belgium (line listing)

Usage

vzv_parvo_be

Format

A data frame with 7 variables:

id

ID of individual

age

Age of individual

gender

Gender of individual

parvouml

Parvo B19 antibody units per ml

parvo_res

If an individual is positive for parvovirus B19

VZVmUIml

VZV milli international units per ml

vzv_res

If an individual is positive for VZV

Source

MOSSONG, J., N. HENS, V. FRIEDERICHS, I. DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. "Parvovirus B19 Infection in Five European Countries: Seroepidemiology, Force of Infection and Maternal Risk of Infection." Epidemiology and Infection 136, no. 8 (2008): 1059-68. doi:10.1017/S0950268807009661

The Weibull model.

Description

Model seroprevalence as a function of duration since vaccination using the Weibull model, where the force of infection is assumed to vary monotonically with duration.

Usage

weibull_model(
  data,
  t_lab = "t",
  pos_col = "pos",
  tot_col = "tot",
  status_col = "status"
)

Arguments

Details

For a Weibull model, the prevalence is given by

\pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}}

Where d is exposure time (difference between age of vaccination and age at test)

Which implies the force of infection to be the monotonic function

\lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}

Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.

Value

list of class weibull_model with the following items

References

Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective. tatistics for Biology and Health. Springer New York. doi:10.1007/978-1-4614-4072-7.

See Also

[stats::glm()] for more information on the fitted "glm" object

Examples

df <- hcv_be_2006[order(hcv_be_2006$dur), ]
df$t <- df$dur
df$status <- df$seropositive
model <- weibull_model(df, t_lab="dur", status_col="seropositive")
plot(model)