Type:	Package
Title:	Model Infectious Disease Parameters from Serosurveys
Version:	1.1.0
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 (≥ 3.4.0)
RoxygenNote:	7.3.1
Imports:	deSolve, dplyr, ggplot2, locfit, purrr, stringr, magrittr, methods, mgcv, mixdist, patchwork, assertthat, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.4.0), boot, stats4
Suggests:	covr, knitr, rmarkdown, bookdown, testthat (≥ 3.0.0)
Collate:	'data.R' 'mseir_model.R' 'sir_basic_model.R' 'sir_static_model.R' 'sir_subpops_model.R' 'fractional_polynomial_models.R' 'polynomial_models.R' 'utils.R' 'compare_models.R' 'correct_prevalence.R' 'weibull_model.R' 'nonparametric.R' 'semiparametric_models.R' 'mixture_model.R' 'hierarchical_bayesian_model.R' 'serosv.R' 'stanmodels.R' 'plots.R' 'compute_ci.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:	2025-04-09 04:05:46 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:	2025-04-09 04:30:02 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

Authors:

• Nguyen Pham Nguyen The nguyenpnt@oucru.org

• 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

Compare models

Description

Compare models

Usage

compare_models(...)

Arguments

Value

a data.frame of 4 columns

Compute confidence interval

Description

Compute confidence interval

Usage

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

compute_ci.fp_model(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 local polynomial model

Description

Compute confidence interval for local polynomial model

Usage

compute_ci.lp_model(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

compute_ci.mixture_model(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

compute_ci.penalized_spline_model(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

compute_ci.weibull_model(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 Bayesian estimation

Description

Estimate the true sero prevalence using Bayesian estimation

Usage

correct_prevalence(
  data,
  bayesian = TRUE,
  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 2 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 by combining mixture model and regression

Description

Refers to section 11.2 - 11.4

Usage

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

Arguments

Value

a list of class estimated_from_mixture with the following items

See Also

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

The Farrington (1990) model.

Description

Refers to section 6.1.2.

Usage

farrington_model(data, start, fixed = list())

Arguments

Value

a list of class farrington_model with 5 items

See Also

[stats::glm()] for more information on the fitted glm 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 GLM fitted model which has the lowest deviance score.

Description

Refers to section 6.2.

Usage

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

Arguments

Value

list of 3 elements:

Examples

df <- hav_be_1993_1994
best_p <- find_best_fp_powers(
df,
p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
)
best_p

A fractional polynomial model.

Description

Refers to section 6.2.

Usage

fp_model(data, p, link = "logit")

Arguments

Value

a list of class fp_model with 5 items

See Also

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

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

# Reproduce Fig 4.1 (upper left panel), p. 63
age <- hav_be_1993_1994$age
pos <- hav_be_1993_1994$pos
tot <- hav_be_1993_1994$tot
plot(
  age, pos / tot,
  pty = "s", cex = 0.06 * 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

# Reproduce Fig 4.1 (upper right panel), p. 63
library(dplyr)
df <- hav_be_2002 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(
  df$age, df$pos / df$tot,
  pty = "s", cex = 0.06 * df$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

# Reproduce Fig 4.1 (lower panel), p. 63
age <- hav_bg_1964$age
pos <- hav_bg_1964$pos
tot <- hav_bg_1964$tot
plot(
    age, pos / tot,
    pty = "s", cex = 0.08 * 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

# Reproduce Fig 4.2, p. 65
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$tot,
  cex = 0.05 * df$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

# Reproduce Fig 4.3, p. 66
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))

plot(
  hcv_be_2006$dur, hcv_be_2006$pos / hcv_be_2006$tot,
  cex = 0.1 * hcv_be_2006$tot, pch = 16,
  xlab = "duration of injection (years)",
  ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
)

Hierarchical Bayesian Model

Description

Refers to section 10.3

Usage

hierarchical_bayesian_model(
  data,
  type = "far3",
  chains = 1,
  warmup = 1500,
  iter = 5000
)

Arguments

Value

a list of class hierarchical_bayesian_model with 6 items

Examples

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

A local polynomial model.

Description

Refers to section 7.1. and 7.2.

Usage

lp_model(data, kern = "tcub", nn = 0, h = 0, deg = 2)

Arguments

Value

a list of class lp_model with 6 items

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

Refers to section 11.1 - 11.4

Usage

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

Arguments

Value

a list of class mixture_model with the following items

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)

MSEIR model

Description

Refers to section 3.4.

Usage

mseir_model(a, gamma, lambda, sigma, nu)

Arguments

Value

list of class mseir_model with the following parameters

Examples

model <- mseir_model(
  a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo
  gamma=1/0.5, # 6 months in the maternal antibodies
  lambda=0.2,  # 5 years in the susceptible class
  sigma=26.07, # 14 days in the latent class
  nu=36.5      # 10 days in the infected class
)
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

Age group

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

# Reproduce Fig 4.4 (left panel), p. 67
age <- mumps_uk_1986_1987$age
pos <- mumps_uk_1986_1987$pos
tot <- mumps_uk_1986_1987$tot
plot(age, pos / tot,
  cex = 0.008 * 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

# Reproduce Fig 4.5 (left upper panel), p. 68
library(dplyr)
df <- parvob19_be_2001_2003 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.02 * df$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

# Reproduce Fig 4.5 (right upper panel), p. 68
# NB: 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$tot,
  cex = 0.02 * df$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

# Reproduce Fig 4.5 (left bottom panel), p. 68
# NB: 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$tot,
  cex = 0.07 * df$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

# Reproduce Fig 4.5 (middle bottom panel), p. 68
# NB: 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_it_2003_2004 %>%
  group_by(age) %>%
  summarise(pos = sum(seropositive), tot = n())
plot(df$age, df$pos / df$tot,
  cex = 0.07 * df$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

# Reproduce Fig 4.5 (right bottom panel), p. 68
# NB: 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$tot,
  cex = 0.07 * df$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

Penalized Spline model

Usage

penalized_spline_model(
  data,
  s = "bs",
  link = "logit",
  framework = "pl",
  sp = NULL
)

Arguments

Value

a list of class penalized_spline_model with 6 attributes

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() 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 MSEIR model

Description

plot() overloading for MSEIR model

Usage

## S3 method for class 'mseir_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 SIR model

Description

plot() overloading for SIR model

Usage

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

Arguments

Value

ggplot object

plot() overloading for SIR static model

Description

plot() overloading for SIR static model

Usage

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

Arguments

Value

ggplot object

plot() overloading for SIR sub populations model

Description

plot() overloading for SIR sub populations model

Usage

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

Arguments

Value

list of ggplot objects, each object is the plot for the corresponding subpopulation

plot() overloading for Weibull model

Description

plot() overloading for Weibull model

Usage

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

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)
)

Polynomial models

Description

Refers to section 6.1.1

Usage

polynomial_model(data, k, type, link = "log")

Arguments

Value

a list of class polynomial_model with 5 items

Examples

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

# fit with aggregated data
model <- polynomial_model(aggregated, type = "Muench")
# fit with linelisting data
model <- polynomial_model(data, type = "Muench")
plot(model)

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

Age group

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

# Reproduce Fig 4.4 (middle panel), p. 67
age <- rubella_uk_1986_1987$age
pos <- rubella_uk_1986_1987$pos
tot <- rubella_uk_1986_1987$tot
plot(age, pos / tot,
  cex = 0.008 * 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

Basic SIR model

Description

Refers to section 3.1.3.

Usage

sir_basic_model(times, state, parameters)

Arguments

Details

In state:

- S: number of susceptible

- I: number of infected

- R: number of recovered

In parameters:

- alpha: disease-related death rate

- mu: natural death rate (= 1/life expectancy)

- beta: transmission rate

- nu: recovery rate

- p: percent of population vaccinated at birth

Value

list of class sir_basic_model with the following items

Examples

state <- c(S=4999, I=1, R=0)
parameters <- c(
  mu=1/75, # 1 divided by life expectancy (75 years old)
  alpha=0, # no disease-related death
  beta=0.0005, # transmission rate
  nu=1, # 1 year for infected to recover
  p=0 # no vaccination at birth
)
times <- seq(0, 250, by=0.1)
model <- sir_basic_model(times, state, parameters)
model

SIR static model (age-heterogeneous, endemic equilibrium)

Description

Refers to section 3.2.2.

Usage

sir_static_model(a, state, parameters)

Arguments

Details

In state:

- s: proportion susceptible

- i: proportion infected

- r: proportion recovered

In parameters:

- lambda: natural death rate

- nu: recovery rate

Value

list of class sir_static_model with the following items

Examples

state <- c(s=0.99,i=0.01,r=0)
parameters <- c(
  lambda = 0.05,
  nu=1/(14/365) # 2 weeks to recover
)
ages<-seq(0, 90, by=0.01)
model = sir_static_model(ages, state, parameters)
model

SIR Model with Interacting Subpopulations

Description

Refers to section 3.5.1.

Usage

sir_subpops_model(times, state, parameters)

Arguments

Details

In state:

- s: Percent susceptible

- i: Percent infected

- r: Percent recovered

In parameters:

- mu: natural death rate (1/L).

- beta: transmission rate w.r.t population (beta tilde)

- nu: recovery rate

- k: number of subpopulations

Value

list of class sir_subpops_model with the following items

Examples

k <- 2
state <- c(
  s = c(0.8, 0.8),
  i = c(0.2, 0.2),
  r = c(  0,   0)
)
beta_matrix <- c(
  c(0.05, 0.00),
  c(0.00, 0.05)
)
parameters <- list(
  beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
  nu = c(1/30, 1/30),
  mu = 0.001,
  k = k
)
times<-seq(0,10000,by=0.5)
model <- sir_subpops_model(times, state, parameters)
model

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

# Reproduce Fig 4.6, p.70
age <- tb_nl_1966_1973$age
birthyr <- tb_nl_1966_1973$birthyr
pos <- tb_nl_1966_1973$pos
tot <- tb_nl_1966_1973$tot
# left panel
plot(age, pos / tot,
  pch = 16, cex = 0.00005 * tot, xlab = "age",
  ylab = "prevalence", xlim = c(6, 18)
)
# right panel
plot(birthyr, pos / tot,
  pch = 16, cex = 0.00005 * tot, xlab = "year", ylab = "prevalence"
)

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

Description

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

Usage

transform_data(t, spos, heterogeneity_col = "t")

Arguments

Value

dataframe in aggregated format

Examples

df <- hcv_be_2006
hcv_df <- transform_data(df$dur, df$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

# Reproduce Fig 4.7 (left panel), p.71
age <- vzv_be_1999_2000$age
pos <- vzv_be_1999_2000$pos
tot <- vzv_be_1999_2000$tot
plot(age, pos / tot,
  cex = 0.036 * 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

# Reproduce Fig 4.7 (right panel), p.71
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$tot,
  cex = 0.036 * df$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

Refers to section 6.1.2.

Usage

weibull_model(data)

Arguments

Value

list of class weibull_model with the following items

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)
plot(model)