| Type: | Package |
| Title: | Bayesian Inference and Model Selection for Stochastic Epidemics |
| Version: | 1.1.6 |
| Description: | Bayesian analysis for stochastic extensions of non-linear dynamic systems using advanced computational algorithms. Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <doi:10.48550/arXiv.2211.15229>. |
| License: | GPL (≥ 3) |
| URL: | https://bernadette-eu.github.io/ |
| BugReports: | https://github.com/bernadette-eu/Bernadette/issues/ |
| Depends: | R (≥ 3.0.2) |
| Imports: | ggplot2, grid, gridExtra, magrittr, methods, Rcpp (≥ 1.0.8.3), RcppParallel, rstantools, rstan, scales, stats, utils |
| Suggests: | knitr, rmarkdown, roxygen2, StanHeaders, testthat (≥ 3.0.0) |
| LinkingTo: | BH (≥ 1.78.0-0), Rcpp (≥ 1.0.8.3), RcppEigen (≥ 0.3.3.9.1), RcppParallel (≥ 5.0.1), rstan, StanHeaders |
| VignetteBuilder: | knitr |
| Biarch: | true |
| Encoding: | UTF-8 |
| Language: | en-US |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.2.3 |
| SystemRequirements: | GNU make |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2025-02-28 06:58:12 UTC; lbour |
| Author: | Lampros Bouranis 
[aut, cre, cph],
European Union [fnd] (Funding by the European Union's Horizon 2020
research and innovation programme under the Marie Sklodowska-Curie
grant agreement No 101027218) |
| Maintainer: | Lampros Bouranis <bernadette.aueb@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-02-28 09:40:14 UTC |
Bayesian inference and model selection for stochastic epidemics
Description
Bernadette provides Bayesian analysis for stochastic extensions of dynamic non-linear systems using advanced computational algorithms.
References
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.3. https://mc-stan.org
Country-specific age distribution
Description
Function to extract the age distribution of a country for a given year, broken down by 5-year age bands and gender, following the United Nations 2019 Revision of World Population Prospects.
Usage
age_distribution(country, year)
Arguments
country |
character; country identifier, following the List of United Nations Member States. See countries_un. |
year |
numeric; calendar year. |
Value
An object of class data.frame that contains the age distribution.
References
United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1.
Prem, K., van Zandvoort, K., Klepac, P. et al (2017). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Examples
# Age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
Age distribution of cumulative reported infections for Greece
Description
A dataset containing the age distribution of cumulative reported infections in Greece from 2020-08-31 to 2021-03-28 (30 weeks). The dataset has been extracted from the Hellenic National Public Health Organization database.
Usage
data(age_specific_cusum_infection_counts)
Format
A data frame with 210 rows and 5 variables:
- Date
Date, format; date in the format "2020-08-31"
- Total_Cases
numeric; count of total cumulative reported infections on a given date
- 0-39
numeric; count of cumulative reported infections on a given date for the age group "0-39"
- 40-64
numeric; count of cumulative reported infections on a given date for the age group "40-64"
- 65+
numeric; count of cumulative reported infections on a given date for the age group "65+"
Value
A data.frame object with 210 rows and 5 variables.
References
Sandbird (2022). Daily regional statistics for covid19 cases in Greece.
Source
https://github.com/Sandbird/covid19-Greece/
Age distribution of new reported infections for Greece
Description
A dataset containing the age distribution of new reported infections in Greece from 2020-08-31 to 2021-03-28 (30 weeks). The dataset has been extracted from the Hellenic National Public Health Organization database.
Usage
data(age_specific_infection_counts)
Format
A data frame with 210 rows and 8 variables:
- Index
integer; a sequence of integer numbers from 1 to 210
- Right
numeric; Index + 1
- Date
Date, format; date in the format "2020-08-31"
- Week_ID
numeric; index of the week that each day falls into. A week is assumed to have 7 days
- Total_Cases
numeric; count of total reported infections on a given date
- 0-39
numeric; count of reported infections on a given date for the age group "0-39"
- 40-64
numeric; count of reported infections on a given date for the age group "40-64"
- 65+
numeric; count of reported infections on a given date for the age group "65+"
Value
A data.frame object with 210 rows and 8 variables.
References
Sandbird (2022). Daily regional statistics for covid19 cases in Greece.
Source
https://github.com/Sandbird/covid19-Greece/
Age distribution of new reported deaths for Greece
Description
A dataset containing the age distribution of reported deaths in Greece from 2020-08-31 to 2021-03-28 (30 weeks). The dataset has been extracted from the Hellenic National Public Health Organization database.
Usage
data(age_specific_mortality_counts)
Format
A data frame with 210 rows and 8 variables:
- Index
integer; a sequence of integer numbers from 1 to 210
- Right
numeric; Index + 1
- Date
Date, format; date in the format "2020-08-31"
- Week_ID
numeric; index of the week that each day falls into. A week is assumed to have 7 days
- New_Deaths
numeric; count of new total reported deaths on a given date
- 0-39
numeric; count of new reported deaths on a given date for the age group "0-39"
- 40-64
numeric; count of new reported deaths on a given date for the age group "40-64"
- 65+
numeric; count of new reported deaths on a given date for the age group "65+"
Value
A data.frame object with 210 rows and 8 variables.
References
Sandbird (2022). Daily regional statistics for covid19 cases in Greece.
Source
https://github.com/Sandbird/covid19-Greece/
Aggregate the age distribution matrix
Description
Function to aggregate the age distribution according to user-defined age groups.
Usage
aggregate_age_distribution(x, lookup_table)
Arguments
x |
data.frame; an age distribution matrix. See age_distribution. |
lookup_table |
data.frame; a user-defined dataframe which maps the sixteen 5-year age bands to a new set of age bands. |
Value
An object of class data.frame that contains the aggregated age distribution.
References
United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1.
Prem, K., van Zandvoort, K., Klepac, P. et al (2020). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Examples
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Plot the aggregated age distribution matrix:
plot_age_distribution(aggr_age)
Aggregate a contact matrix
Description
Function to aggregate a contact matrix according to user-defined age groups.
Usage
aggregate_contact_matrix(object, lookup_table, age_distr)
Arguments
object |
data.frame; a contact matrix. See contact_matrix. |
lookup_table |
data.frame; a user-defined data.frame which maps the sixteen 5-year age bands to a new set of age bands. |
age_distr |
data.frame; the aggregated age distribution. See aggregate_contact_matrix. |
Value
An object of class data.frame.
Examples
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Plot the contact matrix:
plot_contact_matrix(aggr_cm)
Aggregate the Infection Fatality Ratio
Description
Function to aggregate the age-specific Infection Fatality Ratio (IFR) estimates reported by the REACT-2 large-scale community study of SARS-CoV-2 seroprevalence in England according to user-defined age groups.
Usage
aggregate_ifr_react(x, user_AgeGrp, data_cases)
Arguments
x |
data.frame; an age distribution matrix. See age_distribution. |
user_AgeGrp |
vector; a user-defined vector which maps the four age groups considered in REACT-2 to a new set of age groups. |
data_cases |
data.frame; time series dataset containing the age-stratified infection counts. See age_specific_infection_counts. |
Value
A list of two data frames that contains the aggregated IFR estimates.
References
Ward, H., Atchison, C., Whitaker, M. et al. (2021). SARS-CoV-2 antibody prevalence in England following the first peak of the pandemic. Nature Communications 12, 905
Examples
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
age_mapping <- c(rep("0-39", 8),
rep("40-64", 5),
rep("65+", 3))
data(age_specific_infection_counts)
# Aggregate the IFR:
aggr_age_ifr <- aggregate_ifr_react(age_distr, age_mapping, age_specific_infection_counts)
Contact matrices per country
Description
A list of 16 by 16 contact matrices for 177 countries. Row i of a column j of a contact matrix corresponds to the number of contacts made by an individual in group i with an individual in group j.
Usage
data(contact_matrices)
Format
A list of 16 by 16 dataframes for 177 countries.
Value
A list object of 16 by 16 dataframes for 177 countries.
References
Prem, K., van Zandvoort, K., Klepac, P. et al (2020). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Country-specific contact matrix
Description
A 16 by 16 contact matrix whose row i of a column j corresponds to the number of contacts made by an individual in group i with an individual in group j.
Usage
contact_matrix(country)
Arguments
country |
A character indicating the country identifier. See country_contact_matrices. |
Value
An object of class "data.frame".
References
Prem, K., van Zandvoort, K., Klepac, P. et al (2020). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Examples
conmat <- contact_matrix(country = "GRC")
Names of countries with an available age distribution
Description
Function to extract the names of the countries whose discrete age distribution is available by the United Nations.
Usage
countries_un()
Value
A character vector that contains the full names of 201 countries/areas.
References
United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1.
Prem, K., van Zandvoort, K., Klepac, P. et al (2017). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Examples
countries_un()
Names of countries for which a contact matrix is available
Description
Function to extract the names of the countries whose projected contact matrix is available.
Usage
country_contact_matrices()
Value
A character vector of length 177 with the IDs of each of the 177 geographical regions.
References
Prem, K., van Zandvoort, K., Klepac, P. et al (2017). Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era. medRxiv 2020.07.22.20159772; doi: https://doi.org/10.1101/2020.07.22.20159772
Examples
country_contact_matrices()
Distribution of the time between infection and death
Description
Function to discretize the infection-to-death distribution
Usage
itd_distribution(
ts_length,
gamma_mean = 24.19231,
gamma_cv = 0.3987261,
gamma_shape = 6.29,
gamma_rate = 0.26
)
Arguments
ts_length |
integer; time from infection to death in days. |
gamma_mean |
numeric;
mean of a gamma distribution, for a given shape and rate. See also |
gamma_cv |
numeric;
coefficient of variation of a gamma distribution, for a given shape and rate. See also |
gamma_shape |
numeric;
shape parameter of a gamma distribution. See also |
gamma_rate |
numeric;
rate parameter of a gamma distribution. See also |
Value
A vector of length ts_length.
References
Flaxman et al (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584, 257-261.
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
Bar plot of the age distribution
Description
Bar plot of the age distribution
Usage
plot_age_distribution(x)
Arguments
x |
data.frame; the age distribution matrix. See age_distribution and aggregate_age_distribution. |
Value
A ggplot object that can be further customized using the ggplot2 package.
References
United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1.
Examples
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
plot_age_distribution(age_distr)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Plot the aggregated age distribution matrix:
plot_age_distribution(aggr_age)
Contact matrix heatmap
Description
Contact matrix heatmap
Usage
plot_contact_matrix(x)
Arguments
x |
data.frame; a contact matrix. See contact_matrix. |
Value
A ggplot object that can be further customized using the ggplot2 package.
Examples
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
plot_contact_matrix(conmat)
Plot the posterior contact matrix
Description
Plot the posterior contact matrix
Usage
plot_posterior_cm(object, y_data, ...)
Arguments
object |
An object of class |
y_data |
data.frame;
age-specific mortality counts in time. See |
... |
Optional arguments passed to |
Value
A grid.arrange object which can be further customised using the gridExtra package.
References
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
# Visualise the posterior distribution of the random contact matrix:
plot_posterior_cm(object = igbm_fit,
y_data = age_specific_mortality_counts)
Plot the posterior distribution of the infection counts
Description
Plot the posterior distribution of the infection counts
Usage
plot_posterior_infections(
object,
type = c("age-specific", "aggregated"),
xlab = NULL,
ylab = NULL,
...
)
Arguments
object |
A dataframe from |
type |
character; Plot the output for the 'age-specific' infection counts or the 'aggregated' infections. |
xlab |
character; title of x-axis. |
ylab |
character; title of y-axis. |
... |
Optional arguments passed to |
Value
A ggplot object which can be further customised using the ggplot2 package.
See Also
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_inf_summary <- posterior_infections(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the infection counts:
plot_posterior_infections(post_inf_summary, type = "age-specific")
plot_posterior_infections(post_inf_summary, type = "aggregated")
Plot the posterior distribution of the mortality counts
Description
Plot the posterior distribution of the mortality counts
Usage
plot_posterior_mortality(
object,
type = c("age-specific", "aggregated"),
xlab = NULL,
ylab = NULL,
...
)
Arguments
object |
A dataframe from |
type |
character; Plot the output for the 'age-specific' mortality counts or the 'aggregated' mortality counts. |
xlab |
character; title of x-axis. |
ylab |
character; title of y-axis. |
... |
Optional arguments passed to |
Value
A ggplot object which can be further customised using the ggplot2 package.
See Also
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_mortality_summary <- posterior_mortality(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the mortality counts:
plot_posterior_mortality(post_mortality_summary, type = "age-specific")
plot_posterior_mortality(post_mortality_summary, type = "aggregated")
Plot the estimated effective reproduction number trajectory
Description
Plot the estimated effective reproduction number trajectory
Usage
plot_posterior_rt(object, xlab = NULL, ylab = NULL, ...)
Arguments
object |
A data frame from |
xlab |
character; Title of x-axis. |
ylab |
character; Title of y-axis. |
... |
Optional arguments passed to |
Value
A ggplot object which can be further customised using the ggplot2 package.
See Also
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_rt_summary <- posterior_rt(object = igbm_fit,
y_data = age_specific_mortality_counts,
age_distribution_population = aggr_age,
infectious_period = 4)
# Visualise the posterior distribution of the effective reproduction number:
plot_posterior_rt(post_rt_summary)
Plot the estimated age-specific transmission rate
Description
Plot the estimated age-specific transmission rate
Usage
plot_posterior_transmrate(object, xlab = NULL, ylab = NULL, ...)
Arguments
object |
A dataframe from |
xlab |
character; Title of x-axis. |
ylab |
character; Title of y-axis. |
... |
Optional arguments passed to |
Value
A ggplot object which can be further customised using the ggplot2 package.
See Also
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_transmrate_summary <- posterior_transmrate(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the age-specific transmission rate:
plot_posterior_transmrate(post_transmrate_summary)
Summarize the posterior distribution of the infection counts
Description
Summarize the posterior distribution of the infection counts
Usage
posterior_infections(object, y_data)
Arguments
object |
An object of class |
y_data |
data.frame;
age-specific mortality counts in time. See |
Value
A named list with elements Age_specific and Aggregated which can be visualized using plot_posterior_infections.
References
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_inf_summary <- posterior_infections(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the infection counts:
plot_posterior_infections(post_inf_summary, type = "age-specific")
plot_posterior_infections(post_inf_summary, type = "aggregated")
Summarize the posterior distribution of the mortality counts
Description
Summarize the posterior distribution of the mortality counts
Usage
posterior_mortality(object, y_data)
Arguments
object |
An object of class |
y_data |
data.frame;
age-specific mortality counts in time. See |
Value
#' A named list with elements Age_specific and Aggregated which can be visualised using plot_posterior_mortality.
References
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_mortality_summary <- posterior_mortality(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the mortality counts:
plot_posterior_mortality(post_mortality_summary, type = "age-specific")
plot_posterior_mortality(post_mortality_summary, type = "aggregated")
Estimate the effective reproduction number with the next generation matrix approach
Description
Estimate the effective reproduction number with the next generation matrix approach
Usage
posterior_rt(object, y_data, age_distribution_population, infectious_period)
Arguments
object |
An object of class |
y_data |
data.frame;
age-specific mortality counts in time. See |
age_distribution_population |
data.frame;
the age distribution of a given population. See |
infectious_period |
integer; length of infectious period in days. Must be >=1. |
Value
A data.frame which can be visualised using plot_posterior_rt.
References
Diekmann, O., Heesterbeek, J., and Roberts, M. (2010). The construction of next-generation matrices for compartmental epidemic models. J. R. Soc. Interface, 7, 873–-885.
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_rt_summary <- posterior_rt(object = igbm_fit,
y_data = age_specific_mortality_counts,
age_distribution_population = aggr_age,
infectious_period = 4)
# Visualise the posterior distribution of the effective reproduction number:
plot_posterior_rt(post_rt_summary)
Estimate the age-specific transmission rate
Description
Estimate the age-specific transmission rate
Usage
posterior_transmrate(object, y_data)
Arguments
object |
An object of class |
y_data |
data.frame;
age-specific mortality counts in time. See |
Value
A data.frame which can be visualised using plot_posterior_transmrate.
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
post_transmrate_summary <- posterior_transmrate(object = igbm_fit,
y_data = age_specific_mortality_counts)
# Visualise the posterior distribution of the age-specific transmission rate:
plot_posterior_transmrate(post_transmrate_summary)
Prior distributions and options
Description
The functions described on this page are used to specify the prior-related arguments of the modeling functions in the Bernadette package.
The default priors used in the Bernadette modeling functions
are intended to be weakly informative. For many applications the
defaults will perform well, but prudent use of more informative priors is
encouraged. Uniform prior distributions are possible (e.g. by setting
stan_igbm's prior argument to NULL) but, unless
the data is very strong, they are not recommended and are not
non-informative, giving the same probability mass to implausible values as
plausible ones.
Usage
normal(location = 0, scale = NULL)
student_t(df = 1, location = 0, scale = NULL)
cauchy(location = 0, scale = NULL)
gamma(shape = 2, rate = 1)
exponential(rate = 1)
Arguments
location |
Prior location. In most cases, this is the prior mean, but
for |
scale |
Prior scale. The default depends on the family (see Details). |
df |
Degrees of freedom. The default is |
shape |
Prior shape for the gamma distribution. Defaults to |
rate |
Prior rate for the exponential distribution. Defaults to
|
Details
The details depend on the family of the prior being used:
Student t family
Family members:
-
normal(location, scale) -
student_t(df, location, scale) -
cauchy(location, scale)
As the degrees of freedom approaches infinity, the Student t distribution
approaches the normal distribution and if the degrees of freedom are one,
then the Student t distribution is the Cauchy distribution.
If scale is not specified it will default to 2.5.
Value
A named list to be used internally by the Bernadette model fitting functions.
See Also
The vignette for the Bernadette package discusses the use of some of the supported prior distributions.
Examples
# Age-specific mortality/incidence count time series:
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Can assign priors to names:
N05 <- normal(0, 5)
Gamma22 <- gamma(2,2)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
likelihood_variance_type = "quadratic",
prior_volatility = N05,
prior_nb_dispersion = Gamma22,
algorithm_inference = "optimizing")
Bayesian diffusion-driven multi-type epidemic models via Stan
Description
A Bayesian evidence synthesis approach to model the age-specific transmission dynamics of COVID-19 based on daily age-stratified mortality counts. The temporal evolution of transmission rates in populations containing multiple types of individual is reconstructed via independent diffusion processes assigned to the key epidemiological parameters. A suitably tailored Susceptible-Exposed-Infected-Removed (SEIR) compartmental model is used to capture the latent counts of infections and to account for fluctuations in transmission influenced by phenomena like public health interventions and changes in human behaviour.
Usage
stan_igbm(
y_data,
contact_matrix,
age_distribution_population,
age_specific_ifr,
itd_distr,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = c("quadratic", "linear"),
ecr_changes = 1,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 2.5),
prior_nb_dispersion = gamma(shape = 2, rate = 1),
algorithm_inference = c("sampling", "optimizing", "meanfield", "fullrank"),
nBurn = 500,
nPost = 500,
nThin = 1,
adapt_delta = 0.8,
max_treedepth = 14,
seed = 1,
...
)
stan_igbm.fit(
standata_preprocessed,
prior_volatility,
prior_nb_dispersion,
algorithm,
nBurn,
nPost,
nThin,
adapt_delta = NULL,
max_treedepth = NULL,
seed,
...
)
Arguments
y_data |
data.frame;
age-specific mortality counts in time. See |
contact_matrix |
matrix; a squared matrix representing the the number of contacts between age groups. |
age_distribution_population |
data.frame;
the age distribution of a given population. See |
age_specific_ifr |
data.frame;
time-varying age-specific infection-fatality ratio. See |
itd_distr |
vector; Infection-to-death distribution. A vector of length ts_length. |
incubation_period |
integer; length of incubation period in days. Must be >=1. |
infectious_period |
integer; length of infectious period in days. Must be >=1. |
likelihood_variance_type |
integer;
If |
ecr_changes |
integer; between 1 and 7, defaults to 1. Expresses the number of changes of the effective contact rate during the course of 7 days. |
prior_scale_x0 |
double;
scale parameter of a Normal prior distribution assigned to the age-specific log(transmissibility) at time |
prior_scale_x1 |
double;
scale parameter of a Normal prior distribution assigned to the age-specific log(transmissibility) at time |
prior_scale_contactmatrix |
double; defaults to 0.05. A positive number that scales the informative Normal prior distribution assigned to the random contact matrix. |
pi_perc |
numeric;
between 0 and 1. It represents the proportion of Exposed individuals in each age group of a given population at time |
prior_volatility |
Prior distribution for the volatility parameters of the age-specific diffusion processes.
|
prior_nb_dispersion |
Prior distribution for the dispersion parameter |
algorithm_inference |
One of the sampling algorithms that are implemented in Stan. See |
nBurn |
integer;
number of burn-in iterations at the beginning of an MCMC run. See |
nPost |
integer;
number of MCMC iterations after burn-in. See |
nThin |
integer;
a positive integer specifying the period for saving samples. The default is 1, which is usually the recommended value.
See |
adapt_delta |
double;
between 0 and 1, defaults to 0.8. See |
max_treedepth |
integer;
defaults to 14. See |
seed |
integer;
seed for the random number generator. See |
... |
Additional arguments, to be passed to lower-level functions. |
standata_preprocessed |
A named list providing the data for the model. See |
algorithm |
See |
Details
The stan_igbm function performs full Bayesian estimation (if
algorithm_inference is "sampling") via MCMC. The Bayesian model adds
priors (i) on the diffusion processes used to express the time-varying transmissibility
of the virus, the probability that a contact between an infectious person in age
group alpha and a susceptible person in age group alpha leads to
transmission at time t and (ii) on a random contact matrix which represents
the average number of contacts between individuals of age group alpha and
age group alpha' The stan_igbm function calls the workhorse
stan_igbm.fit function.
Value
An object of class stanigbm representing the fitted results. Slot mode for this object indicates if the sampling is done or not.
An object of S4 class stanfit representing the fitted results. Slot mode for this object indicates if the sampling is done or not.
References
Bouranis, L., Demiris, N. Kalogeropoulos, K. and Ntzoufras, I. (2022). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19. arXiv: https://arxiv.org/abs/2211.15229
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
# print_summary <- summary(object = igbm_fit, y_data = age_specific_mortality_counts)$summary
Summary of stanigbm posterior output
Description
This function summarizes the MCMC output for stanigbm objects.
Usage
## S3 method for class 'stanigbm'
summary(object, y_data, ...)
Arguments
object |
An |
y_data |
data.frame;
age-specific mortality counts in time. See |
... |
Additional arguments, to be passed to lower-level functions. |
Value
A named list with elements summary and c_summary,
which contain summaries for for all Markov chains merged and individual chains,
respectively. See stanfit-method-summary.
Examples
# Age-specific mortality/incidence count time series:
data(age_specific_mortality_counts)
data(age_specific_cusum_infection_counts)
# Import the age distribution for Greece in 2020:
age_distr <- age_distribution(country = "Greece", year = 2020)
# Lookup table:
lookup_table <- data.frame(Initial = age_distr$AgeGrp,
Mapping = c(rep("0-39", 8),
rep("40-64", 5),
rep("65+" , 3)))
# Aggregate the age distribution table:
aggr_age <- aggregate_age_distribution(age_distr, lookup_table)
# Import the projected contact matrix for Greece:
conmat <- contact_matrix(country = "GRC")
# Aggregate the contact matrix:
aggr_cm <- aggregate_contact_matrix(conmat, lookup_table, aggr_age)
# Aggregate the IFR:
ifr_mapping <- c(rep("0-39", 8), rep("40-64", 5), rep("65+", 3))
aggr_age_ifr <- aggregate_ifr_react(age_distr, ifr_mapping, age_specific_cusum_infection_counts)
# Infection-to-death distribution:
ditd <- itd_distribution(ts_length = nrow(age_specific_mortality_counts),
gamma_mean = 24.19231,
gamma_cv = 0.3987261)
# Posterior sampling:
rstan::rstan_options(auto_write = TRUE)
chains <- 1
options(mc.cores = chains)
igbm_fit <- stan_igbm(y_data = age_specific_mortality_counts,
contact_matrix = aggr_cm,
age_distribution_population = aggr_age,
age_specific_ifr = aggr_age_ifr[[3]],
itd_distr = ditd,
incubation_period = 3,
infectious_period = 4,
likelihood_variance_type = "linear",
ecr_changes = 7,
prior_scale_x0 = 1,
prior_scale_x1 = 1,
prior_scale_contactmatrix = 0.05,
pi_perc = 0.1,
prior_volatility = normal(location = 0, scale = 1),
prior_nb_dispersion = exponential(rate = 1/5),
algorithm_inference = "sampling",
nBurn = 10,
nPost = 30,
nThin = 1,
chains = chains,
adapt_delta = 0.6,
max_treedepth = 14,
seed = 1)
# print_summary <- summary(object = igbm_fit, y_data = age_specific_mortality_counts)$summary