Package 'idmodelr' reference manual

Title:	Infectious Disease Model Library and Utilities
Description:	Explore a range of infectious disease models in a consistent framework. The primary aim of 'idmodelr' is to provide a library of infectious disease models for researchers, students, and other interested individuals. These models can be used to understand the underlying dynamics and as a reference point when developing models for research. 'idmodelr' also provides a range of utilities. These include: plotting functionality; a simulation wrapper; scenario analysis tooling; an interactive dashboard; tools for handling mult-dimensional models; and both model and parameter look up tables. Unlike other modelling packages such as 'pomp' (<https://kingaa.github.io/pomp/>), 'libbi' (<http://libbi.org>) and 'EpiModel' (<http://www.epimodel.org>), 'idmodelr' serves primarily as an educational resource. It is most comparable to epirecipes (<http://epirecip.es/epicookbook/chapters/simple>) but provides a more consistent framework, an R based workflow, and additional utility tooling. After users have explored model dynamics with 'idmodelr' they may then implement their model using one of these packages in order to utilise the model fitting tools they provide. For newer modellers, this package reduces the barrier to entry by containing multiple infectious disease models, providing a consistent framework for simulation and visualisation, and signposting towards other, more research focussed, resources.
Authors:	Sam Abbott [aut, cre] , Akira Endo [aut]
Maintainer:	Sam Abbott <[email protected]>
License:	GPL-3
Version:	0.4.0
Built:	2024-11-20 04:05:39 UTC
Source:	https://github.com/seabbs/idmodelr

Adds Pointer Structure to R objects

Description

Adds Pointer Structure to R objects

Usage

add_pointer_struct(char, length)
add_pointer_struct(char, length)

Arguments

`char`	A character vector.
`length`	The length of the returned object.

Value

A character vector of the concatenated character string with sequential numbering

Examples


## For a single variable
add_pointer_struct("S", 3)

## For multiple variables
add_pointer_struct(c("S", "I"), length = 3)
## For a single variable
add_pointer_struct("S", 3)

## For multiple variables
add_pointer_struct(c("S", "I"), length = 3)

A Function to Apply Multiple Aggregations to Model Output

Description

A Function to Apply Multiple Aggregations to Model Output

Usage

aggregate_model(
  df,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  id_col = NULL,
  groups = NULL,
  new_var = "incidence",
  total_pop = TRUE,
  summary_var = FALSE,
  test = FALSE
)
aggregate_model(
  df,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  id_col = NULL,
  groups = NULL,
  new_var = "incidence",
  total_pop = TRUE,
  summary_var = FALSE,
  test = FALSE
)

Arguments

`df`	A dataframe of Model Output.
`aggregate_to`	A character vector or list specifying the aggregation operations to perform on the model output. Operations are carried out in the order specified. Implemented options are; disease, demographic, and incidence.
`compartments`	A character vector or list specifying the unique compartments to aggregate. May either be specified once for all aggregation functions or for each function separately.
`strat`	The number of stratified groups in the model.
`hold_out_var`	A character vector or list specifying the unique compartments that will not be aggregated. May either be specified once for all aggregation functions or for each function separately. If compartments is set then this argument does not need to be used.
`id_col`	A character string containing the name of the new id column.
`groups`	A character vector with length equal to the level of stratification. Used to name the stratified levels.
`new_var`	A character vector specifying the new variable to add when aggregating incidence.
`total_pop`	A logical (defaults to `TRUE`) indicating if the total population should be calculated when summarising the model demographics.
`summary_var`	A logical (defaults to `FALSE`), specifying whether to add an additional summary variable across all stratified levels.
`test`	Logical defaults to `FALSE`. For testing, returns the processed inputs rather than performing the aggregation.

Value

An aggregated dataframe.

Examples

df <- data.frame(A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)
aggregate_model(df, aggregate_to = "incidence",
                compartments = c("A", "B"), strat = 3,
                summary_var = TRUE)
df <- data.frame(A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)
aggregate_model(df, aggregate_to = "incidence",
                compartments = c("A", "B"), strat = 3,
                summary_var = TRUE)

An Internal Function to Aggregate Model Output Using other Idmodelr functions.

Description

An Internal Function to Aggregate Model Output Using other Idmodelr functions.

Usage

aggregate_model_internal(
  df,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  new_var = "incidence",
  id_col = NULL,
  groups = NULL,
  total_pop = TRUE,
  summary_var = FALSE
)
aggregate_model_internal(
  df,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  new_var = "incidence",
  id_col = NULL,
  groups = NULL,
  total_pop = TRUE,
  summary_var = FALSE
)

Arguments

`df`	A dataframe of Model Output.
`aggregate_to`	A character vector specifying the aggregation function to apply possible values are; disease, demographic, or incidence.
`compartments`	A character vector specifying the unique compartments to aggregate.
`strat`	The number of stratified groups in the model.
`hold_out_var`	A character vector specifying the unique compartments not to aggregate.
`new_var`	A character vector specifying the new variable to add when aggregating incidence.
`id_col`	A character string containing the name of the new id column.
`groups`	A character vector with length equal to the level of stratification. Used to name the stratified levels.
`total_pop`	A logical (defaults to `TRUE`) indicating if the total population should be calculated when summarising the model demographics.
`summary_var`	A logical (defaults to `FALSE`), specifying whether to add an additional summary variable across all stratified levels.

Value

An aggregated dataframe.

Examples


df <- data.frame(time = 1, A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)

## Incidence
aggregate_model_internal(df, aggregate_to = "incidence",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)

## Demographic
aggregate_model_internal(df, aggregate_to = "demographic",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)
## Disease
aggregate_model_internal(df, aggregate_to = "disease",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)
## Tidy (long)
aggregate_model_internal(df, aggregate_to = "tidy",
                        compartments = c("A", "B"), hold_out_var = "time", strat = 3,
                        summary_var = TRUE, id_col = "Age",
                        groups = c("Children", "Young adults", "Adults"))
df <- data.frame(time = 1, A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)

## Incidence
aggregate_model_internal(df, aggregate_to = "incidence",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)

## Demographic
aggregate_model_internal(df, aggregate_to = "demographic",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)
## Disease
aggregate_model_internal(df, aggregate_to = "disease",
                        compartments = c("A", "B"), strat = 3,
                        summary_var = TRUE)
## Tidy (long)
aggregate_model_internal(df, aggregate_to = "tidy",
                        compartments = c("A", "B"), hold_out_var = "time", strat = 3,
                        summary_var = TRUE, id_col = "Age",
                        groups = c("Children", "Young adults", "Adults"))

Reduces the Dimensionality of a Stratified Model

Description

Reduces the dimensions of stratified model output. Default behaviour is to remove stratification for all variables. However, variables to reduce the dimensions of can be selected, as can variables to preserve with there structure intact.

Usage

combine_strat_model_output(
  df,
  strat = NULL,
  compartments = NULL,
  hold_out_var = NULL
)
combine_strat_model_output(
  df,
  strat = NULL,
  compartments = NULL,
  hold_out_var = NULL
)

Arguments

`df`	A data frame with variables stratified using numeric labels.
`strat`	An integer specifying the number of stratifications to reduce.
`compartments`	A character vector specifying the unique population compartments.
`hold_out_var`	A character vector specifying the variables to keep unchanged. Defaults to NULL

Value

A dataframe of model output that has its dimensionality reduced

Examples


df <- data.frame(S1 = NA, S2 = NA, S3 = NA, time = NA)
combine_strat_model_output(df, 3, compartments = "S", hold_out_var = "time")

df <- data.frame(S1 = NA, S2 = NA, S3 = NA, time = NA)
combine_strat_model_output(df, 3, compartments = "S", hold_out_var = "time")

Combine an Infectious Disease Model To a Demographic Model

Description

Similarly to combine_strat_model_output this functions reduces the dimension of model output into just the demographic components.

Usage

combine_to_age_model(
  df,
  age_com = NULL,
  compartments = NULL,
  hold_out_var = NULL,
  total_pop = TRUE
)
combine_to_age_model(
  df,
  age_com = NULL,
  compartments = NULL,
  hold_out_var = NULL,
  total_pop = TRUE
)

Arguments

`df`	A dataframe of model output.
`age_com`	Integer indicating the number of age compartments.
`compartments`	A character vector of the disease model compartments to combine.
`hold_out_var`	A character vector specifying the variables to keep unchanged. Defaults to NULL
`total_pop`	A logical indicating whether to calculate the total population. Defaults to true.

Value

A dataframe which summarises the demographic process of a model.

Examples


df <- data.frame(S1 = c(1,2), S2 = c(1, 3), E1 = c(4, 1), E2 = c(3, 4), time = c(1, 2))

combine_to_age_model(df, age_com = 2, hold_out_var = "time")
df <- data.frame(S1 = c(1,2), S2 = c(1, 3), E1 = c(4, 1), E2 = c(3, 4), time = c(1, 2))

combine_to_age_model(df, age_com = 2, hold_out_var = "time")

A Function to Estimate a Normal Distribution from Credible or Confidence Intervals

Description

A Function to Estimate a Normal Distribution from Credible or Confidence Intervals

Usage

estimate_norm_dist_from_ci(
  lower_interval = NULL,
  upper_interval = NULL,
  interval = "95%"
)
estimate_norm_dist_from_ci(
  lower_interval = NULL,
  upper_interval = NULL,
  interval = "95%"
)

Arguments

`lower_interval`	Numeric, the lower CI.
`upper_interval`	Numeric, the upper CI
`interval`	A character string indicating the percentage interval the CI represents.

Value

A dataframe containing the mean and standard deviation of the normal distribution summarised by the provided CI's.

Examples


## Run function to estimate normal distribution for a 95% CI of 1, to 2

df <- estimate_norm_dist_from_ci(1,2)

## Check

x <- rnorm(10000, df$mean, df$sd)

quantile(x, c(0.025, 0.975))
## Run function to estimate normal distribution for a 95% CI of 1, to 2

df <- estimate_norm_dist_from_ci(1,2)

## Check

x <- rnorm(10000, df$mean, df$sd)

quantile(x, c(0.025, 0.975))

A Function to Gather Multiple Stratified Variables into a Tidy Format

Description

A Function to Gather Multiple Stratified Variables into a Tidy Format

Usage

gather_strat_multi_variable(
  df,
  id_col,
  compartments = NULL,
  hold_out_var = NULL,
  strat = NULL,
  groups = NULL
)
gather_strat_multi_variable(
  df,
  id_col,
  compartments = NULL,
  hold_out_var = NULL,
  strat = NULL,
  groups = NULL
)

Arguments

`df`	A data frame with variables stratified using numeric labels.
`id_col`	A character string containing the name of the new id column.
`compartments`	A character vector specifying the unique population compartments.
`hold_out_var`	A character vector specifying the variables to keep unchanged. Defaults to NULL
`strat`	An integer specifying the number of stratifications to reduce.
`groups`	A character vector with length equal to the level of stratification. Used to name the stratified levels.

Value

A dataframe of stratified model output with multiple Tidy variables.

Examples



df <- data.frame(time = 0, A1 = 1, A2 = 2, A3 = 3, B1 = 2, B2 = 3, B3 = 0)
gather_strat_multi_variable(df, id_col = "Age", compartment = c("A", "B"), hold_out_var = "time",
                            strat = 3, groups = c("Children", "Young adults", "Adults"))

df <- data.frame(time = 0, A1 = 1, A2 = 2, A3 = 3, B1 = 2, B2 = 3, B3 = 0)
gather_strat_multi_variable(df, id_col = "Age", compartment = c("A", "B"), hold_out_var = "time",
                            strat = 3, groups = c("Children", "Young adults", "Adults"))

A Function to Gather a Stratified Variable into a Tidy Format

Description

A Function to Gather a Stratified Variable into a Tidy Format

Usage

gather_strat_variable(
  df,
  id_col,
  compartment,
  hold_out_var = NULL,
  strat,
  groups = NULL
)
gather_strat_variable(
  df,
  id_col,
  compartment,
  hold_out_var = NULL,
  strat,
  groups = NULL
)

Arguments

`df`	A data frame with variables stratified using numeric labels.
`id_col`	A character string containing the name of the new id column.
`compartment`	The compartment to reduce the dimension of.
`hold_out_var`	A character vector specifying the variables to keep unchanged. Defaults to NULL
`strat`	An integer specifying the number of stratifications to reduce.
`groups`	A character vector with length equal to the level of stratification. Used to name the stratified levels.

Value

A dataframe of stratified model output with a single Tidy variable.

Examples


df <- data.frame(time = 0, A1 = 1, A2 = 2, A3 = 3)
gather_strat_variable(df, id_col = "Age", compartment = "A",
                      strat = 3, groups = c("Children", "Young adults", "Adults"))
df <- data.frame(time = 0, A1 = 1, A2 = 2, A3 = 3)
gather_strat_variable(df, id_col = "Age", compartment = "A",
                      strat = 3, groups = c("Children", "Young adults", "Adults"))

A Function to Generate Parameter Permutations

Description

A function to generate parameter permutations from a generic sampling function (or if not given from the input parameters). This function can be used to rapidly generate new parameter combinations given parameters to be varied, and scenarios to be investigated.

Usage

generate_parameter_permutations(
  variable_params = NULL,
  fixed_params = NULL,
  sample_params = NULL,
  excluded_params = NULL,
  scenarios = NULL,
  sampling_function = NULL,
  parameter_samples = 1,
  repeat_sample = TRUE,
  rerun = FALSE,
  ...
)
generate_parameter_permutations(
  variable_params = NULL,
  fixed_params = NULL,
  sample_params = NULL,
  excluded_params = NULL,
  scenarios = NULL,
  sampling_function = NULL,
  parameter_samples = 1,
  repeat_sample = TRUE,
  rerun = FALSE,
  ...
)

Arguments

`variable_params`	A dataframe containing any parameter variations to be investigated. If these parameters would normally be sampled then they should be added to the excluded_params argument.
`fixed_params`	A named vector of parameters that are not sampled by the sampling function. If these parameters would usually be sampled then they should be added to the excluded_params argument.
`sample_params`	A named vector of parameters to be sampled. If a sampling function is not supplied these parameters will be used in the final permutation dataframe.
`excluded_params`	A character vector indicating which parameters should have there sampled values kept.
`scenarios`	A dataframe of possible scenarios to investigate. It must contain a scenario variable to identify each separate scenarios. If parameters are included here that would normally be sampled then they should be added to the excluded_params argument
`sampling_function`	A sampling function, this should be designed such that it's input is a matrix with each parameter having a named row. It should return it's output in the same format. If not supplied defaults to passing through parameters, this may not be the required behaviour.
`parameter_samples`	The number of parameter samples to take, defaults to one.
`repeat_sample`	A logical (defaults to `TRUE`) which indicates if each scenario should independently sample from the sampling function. If set to `FALSE` then each scenario will share the same sampled parameter set.
`rerun`	A logical indicating if the function should be rerun or saved results should be loaded. Defaults to `FALSE`.
`...`	Additional arguments to be passed to the sampling_function.

Value

A dataframe containing sampled parameter permutations

Examples


scenarios <- data.frame(scenario = c("test_1", "test_2"), scenario_param = c(0, 1))
variable_params <-  data.frame(variable = c(0, 0.5, 1))
fixed_params <- c(fixed_1 = 2, fixed_2 = c(1, 3, 4))
sample_params <- c(sample_1 = 2, sample_2 = c(2, 1))

generate_parameter_permutations(variable_params, fixed_params, sample_params,
                                excluded_params = c("variable"), scenarios,
                                parameter_samples = 1)
scenarios <- data.frame(scenario = c("test_1", "test_2"), scenario_param = c(0, 1))
variable_params <-  data.frame(variable = c(0, 0.5, 1))
fixed_params <- c(fixed_1 = 2, fixed_2 = c(1, 3, 4))
sample_params <- c(sample_1 = 2, sample_2 = c(2, 1))

generate_parameter_permutations(variable_params, fixed_params, sample_params,
                                excluded_params = c("variable"), scenarios,
                                parameter_samples = 1)

Model Details

Description

Details on models implemented in idmodelr.

Usage

model_details
model_details

Format

A data frame with 22 rows and 14 variables.

model: Name of the model function.
model_family: Name of the model family (i.e. SIR)
time: Discrete or continuous time
type: Deterministic or stochastic
recovered: Does this model included a recovered population (yes/no).
exposed: Does this model included an exposed population (yes/no).
treated: Does this model included a treated population (yes/no).
susceptible: Does this model included a post infection susceptible population (yes/no).
risk_stratified: Is this model risk stratified (yes/no).
non_exponential: Does this model contain non-exponential rates (yes/no).
simple_demographics: Does this model contain simple (i.e. births = deaths) demographics (yes/no).
vaccination: Does this model include vaccination (yes/no).
disease_example: Which diseases (if any) can this model be used for.
language: What language is this model written in (i.e. R, C etc.).
parameters: A list of parameters required by the model

Extracts a Single Column, Summarises if from Simulation

Description

Extracts a Single Column, Summarises if from Simulation

Usage

model_df_to_vector(df, com_var, id_var = NULL, sum_fn = NULL)
model_df_to_vector(df, com_var, id_var = NULL, sum_fn = NULL)

Arguments

`df`	A data frame of dynamic system output.
`com_var`	The vector to be compared; use unquoted name (NSE).
`id_var`	A character string indicating the id variable to summarise over if required.
`sum_fn`	The summary function to be used, defaults to median.

Value

Returns a numeric vector, summarised if required.

Examples

library(dplyr)
## Extract a vector with no repeats
model_df_to_vector(iris, Petal.Length)

## Extract a vector and summarise
df <- bind_rows(iris %>% mutate(sim = 1, id = 1:length(sim)),
 iris %>% mutate(sim = 2 , id = 1:length(sim)))

model_df_to_vector(df, Petal.Length, "id", sum_fn = mean)
library(dplyr)
## Extract a vector with no repeats
model_df_to_vector(iris, Petal.Length)

## Extract a vector and summarise
df <- bind_rows(iris %>% mutate(sim = 1, id = 1:length(sim)),
 iris %>% mutate(sim = 2 , id = 1:length(sim)))

model_df_to_vector(df, Petal.Length, "id", sum_fn = mean)

Parameter Details

Description

Details on the parameters used in models implemented in idmodelr.

Usage

parameter_details
parameter_details

Format

A data frame with 14 rows and 6 variables.

parameter: Name of the parameter.
parameter_family: Descriptive parameter family (i.e. transmission).
description: What is this parameter.
type: What type of parameter is this (i.e rate, proportion, probability etc.).
risk_stratified: Is this parameter used in risk stratified models (yes/no).
non_exponential: Is this parameter a non-exponential rate (yes/no).

Plot Compartment Populations over Time for a Model Simulation

Description

Make separate plots for each model compartment. Assumes model output is structured as that produced from solve_ode.

Usage

plot_model(sim, prev_sim = NULL, model_labels = NULL, facet = TRUE)
plot_model(sim, prev_sim = NULL, model_labels = NULL, facet = TRUE)

Arguments

`sim`	A tibble of model output as formatted by `solve_ode`. Optionally a list of simulations can be passed when comparing multiple model runs.
`prev_sim`	A second tibble of model output formatted as for `sim`. Used to compare to model runs. Can only be supplied if `sim` is not a list.
`model_labels`	A character vector of model names. Defaults to `c("Current", "Previous")` when two model simulations are used and the list names when `sim` is a list. If `sim` is unnamed the index of the list is used.
`facet`	Logical, defaults to `TRUE`. If `FALSE` then the plot will not be faceted otherwise it will be.

Value

A Plot of each model compartments population over time.

Examples


## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)

plot_model(sim, facet = FALSE)

plot_model(sim, facet = TRUE)

## Compare with an updated model run

#'## Intialise
R_0 = 1.3
beta = R_0
parameters <- as.matrix(c(beta = beta))

new_sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)


plot_model(new_sim,sim, facet = FALSE)

plot_model(new_sim, sim, facet = TRUE)

## Passing in the simulations as a list
plot_model(list("Current" = new_sim, "Previous" = sim), facet = TRUE)
## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)

plot_model(sim, facet = FALSE)

plot_model(sim, facet = TRUE)

## Compare with an updated model run

#'## Intialise
R_0 = 1.3
beta = R_0
parameters <- as.matrix(c(beta = beta))

new_sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)


plot_model(new_sim,sim, facet = FALSE)

plot_model(new_sim, sim, facet = TRUE)

## Passing in the simulations as a list
plot_model(list("Current" = new_sim, "Previous" = sim), facet = TRUE)

Lookup the Details of Parameters Required by a Model

Description

This function simplifies the process of checking which parameters a given idmodelr model depends on. It is effectively an interface to parameter_details via model_details. As fuzzy matching has been used it can also given information of the parameter requirements of a subset of the available models.

Usage

required_parameters(model = NULL)
required_parameters(model = NULL)

Arguments

model

A character string containing the name of the model of interest. Defaults to NULL.

Value

A dataframe extracted from parameter_details containing the details of the parameters required by the model of interest.

Examples


## Check the parameters required by the "SIR_ode" model
required_parameters("SIR_ode")


## Use fizzy matching to look at parameters for all SIR models
required_parameters("SIR")
## Check the parameters required by the "SIR_ode" model
required_parameters("SIR_ode")


## Use fizzy matching to look at parameters for all SIR models
required_parameters("SIR")

A Function to Perform Scenario Analysis for a Generic Model Object.

Description

This function uses parameter permutations produced by generate_parameter_permutations to simulate from a supplied model function. It can be used to examine multiple scenarios, with any number of parameter variations, for multiple samples.

Usage

scenario_analysis(
  parameter_df,
  variable_params = NULL,
  model = NULL,
  sim_fn = NULL,
  summary_fn = NULL,
  cores = 1,
  rerun = FALSE,
  verbose = FALSE,
  by_row = FALSE,
  test = FALSE,
  ...
)
scenario_analysis(
  parameter_df,
  variable_params = NULL,
  model = NULL,
  sim_fn = NULL,
  summary_fn = NULL,
  cores = 1,
  rerun = FALSE,
  verbose = FALSE,
  by_row = FALSE,
  test = FALSE,
  ...
)

Arguments

`parameter_df`	A dataframe of parameter permutations as produced by `generate_parameter_permutations`. Using the default options it will save results when run for the first time, and afterwards load them in.
`variable_params`	A character vector containing the names of the parameters that are varied in `parameter_df`.
`model`	A model compatible with your `sim_fn`.
`sim_fn`	A generic simulation function, with the first argument as the model object, a `params` argument, and a `as.data.frame` argument.
`summary_fn`	A function which accepts a single dataframe argument customised to fit with the standard output of `scenario_analysis` and your `simulate_model` function. Defaults to `NULL` for which no summarisation takes place. Warning: If a previous analysis has been saved, changing this option will not summarise the result. The analysis must be rerun.
`cores`	The number of cores to use for the scenario analysis, defaults to 1.
`rerun`	A logical indicating if the function should be rerun or saved results should be loaded. Defaults to `FALSE`.
`verbose`	Logical (defaults to `FALSE`) indicating if progress information should be printed to the console.
`by_row`	Logical (defaults to `FALSE`) indicating if inputted parameters should be inputted as a block to `sim_fn` or individually. If `TRUE` then function will always return a tibble. Does not currently work with sim_fn that produces multiple simulations for a single parameter set - for this scenario a block based approach or post processing is required.
`test`	A logical (defaults to `FALSE`) if `TRUE` function uses multicore functionality regardless of the number of cores specified.
`...`	Pass additional arguments to sim_fn. Only implemented when a single core is used.

Value

A tidy dataframe containing simulated model trajectories for each scenario varied parameter combination. Use 'tidyr::unnest“ to examine all simulation results.

Examples


scenarios <- data.frame(scenario = c("test_1", "test_2"), scenario_param = c(0, 1))
variable_params <-  data.frame(variable = c(0, 0.5, 1))
fixed_params <- c(fixed_1 = 2, fixed_2 = c(1, 3, 4))
sample_params <- c(sample_1 = 2, sample_2 = c(2, 1))

parameter_df <- generate_parameter_permutations(variable_params, fixed_params, sample_params,
                                                excluded_params = c("variable"), scenarios,
                                                parameter_samples = 10)

## set up dummy simulation function (returning an empty dataframe)
dummy_sim_fn <- function(object, inits, params, times, as.data.frame) {
 x <- tibble::tibble()
 return(x)
}

## Set up dummy summary function
dummy_sum_fn <- function(df){
df <- dplyr::mutate(df, summarised_simulations = simulations)

return(df)
}
dummy_model <- function(){}

## Run scenario analysis
scenario_analysis(parameter_df, variable_params = "variable", model = dummy_model,
                  sim_fn = dummy_sim_fn, cores = 1, summary_fn = dummy_sum_fn)
scenarios <- data.frame(scenario = c("test_1", "test_2"), scenario_param = c(0, 1))
variable_params <-  data.frame(variable = c(0, 0.5, 1))
fixed_params <- c(fixed_1 = 2, fixed_2 = c(1, 3, 4))
sample_params <- c(sample_1 = 2, sample_2 = c(2, 1))

parameter_df <- generate_parameter_permutations(variable_params, fixed_params, sample_params,
                                                excluded_params = c("variable"), scenarios,
                                                parameter_samples = 10)

## set up dummy simulation function (returning an empty dataframe)
dummy_sim_fn <- function(object, inits, params, times, as.data.frame) {
 x <- tibble::tibble()
 return(x)
}

## Set up dummy summary function
dummy_sum_fn <- function(df){
df <- dplyr::mutate(df, summarised_simulations = simulations)

return(df)
}
dummy_model <- function(){}

## Run scenario analysis
scenario_analysis(parameter_df, variable_params = "variable", model = dummy_model,
                  sim_fn = dummy_sim_fn, cores = 1, summary_fn = dummy_sum_fn)

Susceptible-Exposed-Infected Model with Simple Demographics

Description

Susceptible-Exposed-Infected Model with Simple Demographics

Usage

SEI_demographics_ode(t, x, params)
SEI_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
gamma <- 1/2
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0)

SEI_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
gamma <- 1/2
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0)

SEI_demographics_ode(1, inits, parameters)

Susceptible-Exposed-Infected Model

Description

Susceptible-Exposed-Infected Model

Usage

SEI_ode(t, x, params)
SEI_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
gamma = 1/2

parameters <- c(beta = beta, gamma = gamma)
inits <- c(S = S_0, E = E_0, I = I_0)

SEI_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
gamma = 1/2

parameters <- c(beta = beta, gamma = gamma)
inits <- c(S = S_0, E = E_0, I = I_0)

SEI_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Recovered Model with Simple Demographics

Description

Susceptible-Exposed-Infected-Recovered Model with Simple Demographics

Usage

SEIR_demographics_ode(t, x, params)
SEIR_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, tau = tau, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIR_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, tau = tau, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIR_demographics_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Recovered Model

Description

Susceptible-Exposed-Infected-Recovered Model

Usage

SEIR_ode(t, x, params)
SEIR_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2

parameters <- c(beta = beta, gamma = gamma, tau = tau)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIR_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2

parameters <- c(beta = beta, gamma = gamma, tau = tau)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIR_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Recovered-Susceptible Model with Simple Demographics

Description

Susceptible-Exposed-Infected-Recovered-Susceptible Model with Simple Demographics

Usage

SEIRS_demographics_ode(t, x, params)
SEIRS_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
chi <- 0.5
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, chi = chi,
                tau = tau, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIRS_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
chi <- 0.5
mu <- 1/81

parameters <- c(beta = beta, gamma = gamma, chi = chi,
                tau = tau, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIRS_demographics_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Recovered-Susceptible Model

Description

Susceptible-Exposed-Infected-Recovered-Susceptible Model

Usage

SEIRS_ode(t, x, params)
SEIRS_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
chi <- 0.5

parameters <- c(beta = beta, gamma = gamma,
                chi = chi, tau = tau)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIRS_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
R_0 <- 0
beta <- 3
gamma <-  1/2
tau <- 2
chi <- 0.5

parameters <- c(beta = beta, gamma = gamma,
                chi = chi, tau = tau)
inits <- c(S = S_0, E = E_0, I = I_0, R_0 = R_0)

SEIRS_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Susceptible Model with Simple Demographics

Description

Susceptible-Exposed-Infected-Susceptible Model with Simple Demographics

Usage

SEIS_demographics_ode(t, x, params)
SEIS_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
chi <- 0.5
gamma <- 1/2
mu <- 1/81
parameters <- c(beta = beta, gamma = gamma,
 chi = chi, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0)

SEIS_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
chi <- 0.5
gamma <- 1/2
mu <- 1/81
parameters <- c(beta = beta, gamma = gamma,
 chi = chi, mu = mu)
inits <- c(S = S_0, E = E_0, I = I_0)

SEIS_demographics_ode(1, inits, parameters)

Susceptible-Exposed-Infected-Susceptible Model

Description

Susceptible-Exposed-Infected-Susceptible Model

Usage

SEIS_ode(t, x, params)
SEIS_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
chi <- 0.5
gamma = 1/2
parameters <- c(beta = beta, gamma = gamma, chi = chi)
inits <- c(S = S_0, E = E_0, I = I_0)

SEIS_ode(1, inits, parameters)
##Model Input
S_0 <- 989
E_0 <- 10
I_0 <- 1
beta <- 3
chi <- 0.5
gamma = 1/2
parameters <- c(beta = beta, gamma = gamma, chi = chi)
inits <- c(S = S_0, E = E_0, I = I_0)

SEIS_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Recovered Model with Simple Demographics

Description

Susceptible-High-risk-latent-Low-risk-latent-Infected-Recovered Model with Simple Demographics

Usage

SHLIR_demographics_ode(t, x, params)
SHLIR_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
H_0 <- 10
L_0 <- 0
I_0 <- 1
R_0 <- 0
beta = 3 # Rate of transmission
gamma_H = 1/5 # Rate of progression to active symptoms from high risk latent
nu = 1/2 #Rate of progression from high to low risk latent
gamma_L = 1/100 # Rate of progression to active symptoms for low risk latent
tau = 1/2 # Rate of recovery
mu = 1/81 # Rate of natural mortality
parameters <- c(beta = beta, gamma_H = gamma_H, gamma_L = gamma_L, nu = nu, tau = tau, mu = mu)
inits <- c(S = S_0, H = H_0, L = L_0, I = I_0, R_0 = R_0)

SHLIR_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
H_0 <- 10
L_0 <- 0
I_0 <- 1
R_0 <- 0
beta = 3 # Rate of transmission
gamma_H = 1/5 # Rate of progression to active symptoms from high risk latent
nu = 1/2 #Rate of progression from high to low risk latent
gamma_L = 1/100 # Rate of progression to active symptoms for low risk latent
tau = 1/2 # Rate of recovery
mu = 1/81 # Rate of natural mortality
parameters <- c(beta = beta, gamma_H = gamma_H, gamma_L = gamma_L, nu = nu, tau = tau, mu = mu)
inits <- c(S = S_0, H = H_0, L = L_0, I = I_0, R_0 = R_0)

SHLIR_demographics_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Recovered Model

Description

Susceptible-High-risk-latent-Low-risk-latent-Infected-Recovered Model

Usage

SHLIR_ode(t, x, params)
SHLIR_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
H_0 <- 10
L_0 <- 0
I_0 <- 1
R_0 <- 0
beta = 3 # Rate of transmission
gamma_H = 1/5 # Rate of progression to active symptoms from high risk latent
nu = 1/2 #Rate of progression from high to low risk latent
gamma_L = 1/100 # Rate of progression to active symptoms for low risk latent
tau = 1/2 # Rate of recovery

parameters <- c(beta = beta, gamma_H = gamma_H, gamma_L = gamma_L, nu = nu, tau = tau)
inits <- c(S = S_0, H = H_0, L = L_0, I = I_0, R_0 = R_0)

SHLIR_ode(1, inits, parameters)
##Model Input
S_0 <- 989
H_0 <- 10
L_0 <- 0
I_0 <- 1
R_0 <- 0
beta = 3 # Rate of transmission
gamma_H = 1/5 # Rate of progression to active symptoms from high risk latent
nu = 1/2 #Rate of progression from high to low risk latent
gamma_L = 1/100 # Rate of progression to active symptoms for low risk latent
tau = 1/2 # Rate of recovery

parameters <- c(beta = beta, gamma_H = gamma_H, gamma_L = gamma_L, nu = nu, tau = tau)
inits <- c(S = S_0, H = H_0, L = L_0, I = I_0, R_0 = R_0)

SHLIR_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Treated-Recovered Model with Simple Demographics

Description

A more complex SHLIR model flow diagram, treatment,reinfection, and simple demographics for those who have recovered from active disease.

Usage

SHLITR_demographics_ode(t, x, params)
SHLITR_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples


## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0
)

parameters <- c(
beta = 3, # Rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
mu = 1/81 # Rate of natural mortality
)

SHLITR_demographics_ode(1, inits, parameters)

## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0
)

parameters <- c(
beta = 3, # Rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
mu = 1/81 # Rate of natural mortality
)

SHLITR_demographics_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Treated-Recovered Model

Description

A more complex SHLIR model flow diagram, treatment, and reinfection for those who have recovered from active disease.

Usage

SHLITR_ode(t, x, params)
SHLITR_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples


## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0
)

parameters <- c(
beta = 3, # Rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2 # Rate of recovery
)

SHLITR_ode(1, inits, parameters)

## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0
)

parameters <- c(
beta = 3, # Rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2 # Rate of recovery
)

SHLITR_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Treated-Recovered Model with Demographics, Stratified by Risk

Description

A more complex SHLIR model flow diagram, with risk groups, treatment, and reinfection for those who have recovered from active disease

Usage

SHLITR_risk_demographics_ode(t, x, params)
SHLITR_risk_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples


## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0,
## High risk population
S_H = 199,
H_H = 0,
L_H = 0,
I_H = 1,
Tr_H = 0,
R_H = 0
)

parameters <- c(
beta = 3, # Rate of transmission
beta_H = 6, # High risk rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
mu = 1/81, # Rate of natural mortality
p = 0.2, # proportion of new births that are high risk
M = 0.2 # Between group mixing
)

SHLITR_risk_demographics_ode(1, inits, parameters)

## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0,
## High risk population
S_H = 199,
H_H = 0,
L_H = 0,
I_H = 1,
Tr_H = 0,
R_H = 0
)

parameters <- c(
beta = 3, # Rate of transmission
beta_H = 6, # High risk rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
mu = 1/81, # Rate of natural mortality
p = 0.2, # proportion of new births that are high risk
M = 0.2 # Between group mixing
)

SHLITR_risk_demographics_ode(1, inits, parameters)

Susceptible-High-risk-latent-Low-risk-latent-Infected-Treated-Recovered Model, Stratified by Risk

Description

A more complex SHLIR model flow diagram, with risk groups, treatment, and reinfection for those who have recovered from active disease

Usage

SHLITR_risk_ode(t, x, params)
SHLITR_risk_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples


## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0,
## High risk population
S_H = 199,
H_H = 0,
L_H = 0,
I_H = 1,
Tr_H = 0,
R_H = 0
)

parameters <- c(
beta = 3, # Rate of transmission
beta_H = 6, # High risk rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
M = 0.2 # Between group mixing
)

SHLITR_risk_ode(1, inits, parameters)

## initialise
inits <- c(
# General population
S = 800,
H = 0,
L = 0,
I = 0,
Tr = 0,
R = 0,
## High risk population
S_H = 199,
H_H = 0,
L_H = 0,
I_H = 1,
Tr_H = 0,
R_H = 0
)

parameters <- c(
beta = 3, # Rate of transmission
beta_H = 6, # High risk rate of transmission
gamma_H = 1/5, # Rate of progression to active symptoms from high risk latent
nu = 1/2, #Rate of progression from high to low risk latent
gamma_L = 1/100, # Rate of progression to active symptoms for low risk latent
epsilon = 1/3, # Rate of treatment
tau = 1/2, # Rate of recovery
M = 0.2 # Between group mixing
)

SHLITR_risk_ode(1, inits, parameters)

Susceptible-Infected Model with Simple Demographics

Description

Susceptible-Infected Model with Simple Demographics

Usage

SI_demographics_ode(t, x, params)
SI_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
mu <- 1/81

parameters <- c(beta = beta, mu = mu)
inits <- c(S = S_0, I = I_0)

SI_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
mu <- 1/81

parameters <- c(beta = beta, mu = mu)
inits <- c(S = S_0, I = I_0)

SI_demographics_ode(1, inits, parameters)

Susceptible-Infected Model

Description

Susceptible-Infected Model

Usage

SI_ode(t, x, params)
SI_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
parameters <- c(beta = beta)
inits <- c(S = S_0, I = I_0)

SI_ode(1, inits, parameters)
##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
parameters <- c(beta = beta)
inits <- c(S = S_0, I = I_0)

SI_ode(1, inits, parameters)

A Function to Simulate a Model from a Generic Simulation Function, with Pre and Post Processing

Description

A Function to Simulate a Model from a Generic Simulation Function, with Pre and Post Processing

Usage

simulate_model(
  model,
  sim_fn,
  inits = NULL,
  params = NULL,
  times = NULL,
  as_tibble = TRUE,
  by_row = FALSE,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  new_var = "incidence",
  total_pop = TRUE,
  summary_var = FALSE,
  verbose = FALSE,
  ...
)
simulate_model(
  model,
  sim_fn,
  inits = NULL,
  params = NULL,
  times = NULL,
  as_tibble = TRUE,
  by_row = FALSE,
  aggregate_to = NULL,
  compartments = NULL,
  strat = NULL,
  hold_out_var = NULL,
  new_var = "incidence",
  total_pop = TRUE,
  summary_var = FALSE,
  verbose = FALSE,
  ...
)

Arguments

`model`	A model compatible with your `sim_fn`.
`sim_fn`	A generic simulation function, with the first argument as the model object, a `params` argument, and a `as.data.frame` argument.
`inits`	A dataframe of initial conditions, optionally a named vector can be used.
`params`	A dataframe of parameters, with each parameter as a variable. Optionally a named vector can be used.
`times`	A vector of the times to sample the model for, from a starting time to a final time.
`as_tibble`	Logical (defaults to `TRUE`) indicating if the output should be returned as a tibble, otherwise returned as the default `sim_fn` output.
`by_row`	Logical (defaults to `FALSE`) indicating if inputted parameters should be inputted as a block to `sim_fn` or individually. If `TRUE` then function will always return a tibble. Does not currently work with sim_fn that produces multiple simulations for a single parameter set - for this scenario a block based approach or post processing is required.
`aggregate_to`	A character vector or list specifying the aggregation operations to perform on the model output. Operations are carried out in the order specified. Implemented options are; disease, demographic, and incidence.
`compartments`	A character vector or list specifying the unique compartments to aggregate. May either be specified once for all aggregation functions or for each function separately.
`strat`	The number of stratified groups in the model.
`hold_out_var`	A character vector or list specifying the unique compartments that will not be aggregated. May either be specified once for all aggregation functions or for each function separately. If compartments is set then this argument does not need to be used.
`new_var`	A character vector specifying the new variable to add when aggregating incidence.
`total_pop`	A logical (defaults to `TRUE`) indicating if the total population should be calculated when summarising the model demographics.
`summary_var`	A logical (defaults to `FALSE`), specifying whether to add an additional summary variable across all stratified levels.
`verbose`	Logical (defaults to `FALSE`) indicating if progress information should be printed to the console.
`...`	Additional arguments to pass to `sim_fn`

Value

Trajectories as a tibble, optionally returns the default sim_fn output.

Examples


## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- data.frame(beta = beta)
inits <- data.frame(S = S_0, I = I_0)

SI_sim <- simulate_model(model = SI_ode, sim_fn = solve_ode, inits, parameters, times)
## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- data.frame(beta = beta)
inits <- data.frame(S = S_0, I = I_0)

SI_sim <- simulate_model(model = SI_ode, sim_fn = solve_ode, inits, parameters, times)

Susceptible-Infected-Recovered Model with Simple Demographics

Description

Susceptible-Infected-Recovered Model with Simple Demographics

Usage

SIR_demographics_ode(t, x, params)
SIR_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
tau <- 2
mu <- 1/81


parameters <- c(beta = beta, tau = tau, mu = mu)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIR_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
tau <- 2
mu <- 1/81


parameters <- c(beta = beta, tau = tau, mu = mu)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIR_demographics_ode(1, inits, parameters)

Susceptible-Infected-Recovered Model

Description

Susceptible-Infected-Recovered Model

Usage

SIR_ode(t, x, params)
SIR_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
tau <- 2

parameters <- c(beta = beta, tau = tau)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIR_ode(1, inits, parameters)
##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
tau <- 2

parameters <- c(beta = beta, tau = tau)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIR_ode(1, inits, parameters)

Susceptible-Infected-Recovered Model with Simple Demographics and Vaccination

Description

Susceptible-Infected-Recovered Model with Simple Demographics and Vaccination

Usage

SIR_vaccination_demographics_ode(t, x, params)
SIR_vaccination_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
mu <- 1/81
alpha <- 0.8
lambda <- 0.7

parameters <- c(beta = beta, tau = tau, mu = mu,
                alpha = 0.8, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIR_vaccination_demographics_ode(1, inits, parameters)
##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
mu <- 1/81
alpha <- 0.8
lambda <- 0.7

parameters <- c(beta = beta, tau = tau, mu = mu,
                alpha = 0.8, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIR_vaccination_demographics_ode(1, inits, parameters)

Susceptible-Infected-Recovered Model with Vaccination

Description

Susceptible-Infected-Recovered Model with Vaccination

Usage

SIR_vaccination_ode(t, x, params)
SIR_vaccination_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
lambda <- 0.7

parameters <- c(beta = beta, tau = tau,
                lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIR_vaccination_ode(1, inits, parameters)
##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
lambda <- 0.7

parameters <- c(beta = beta, tau = tau,
                lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIR_vaccination_ode(1, inits, parameters)

Susceptible-Infected-Recovered-Susceptible Model with Simple Demographics

Description

Susceptible-Infected-Recovered-Susceptible Model with Simple Demographics

Usage

SIRS_demographics_ode(t, x, params)
SIRS_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
mu <- 1/81
dt <- 1

parameters <- c(beta = beta, tau = tau, mu = mu)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIRS_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
mu <- 1/81
dt <- 1

parameters <- c(beta = beta, tau = tau, mu = mu)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIRS_demographics_ode(1, inits, parameters)

Susceptible-Infected-Recovered-Susceptible Model

Description

Susceptible-Infected-Recovered-Susceptible Model

Usage

SIRS_ode(t, x, params)
SIRS_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
dt <- 1

parameters <- c(beta = beta, tau = tau, chi = chi)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIRS_ode(1, inits, parameters)
##Model Input
S_0 <- 989
I_0 <- 1
R_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
dt <- 1

parameters <- c(beta = beta, tau = tau, chi = chi)
inits <- c(S = S_0, I = I_0, R_0 = R_0)

SIRS_ode(1, inits, parameters)

Susceptible-Infected-Recovered-Susceptible Model with Simple Demographics and Vaccination

Description

Susceptible-Infected-Recovered-Susceptible Model with Simple Demographics and Vaccination

Usage

SIRS_vaccination_demographics_ode(t, x, params)
SIRS_vaccination_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
chi <- 0.5
mu <- 1/81
alpha <- 0.8
lambda <- 0.7
dt <- 1

parameters <- c(beta = beta, tau = tau, mu = mu,
                alpha = 0.8, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIRS_vaccination_demographics_ode(1, inits, parameters)
##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
tau <- 2
chi <- 0.5
mu <- 1/81
alpha <- 0.8
lambda <- 0.7
dt <- 1

parameters <- c(beta = beta, tau = tau, mu = mu,
                alpha = 0.8, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIRS_vaccination_demographics_ode(1, inits, parameters)

Susceptible-Infected-Recovered-Susceptible Model with Vaccination

Description

Susceptible-Infected-Recovered-Susceptible Model with Vaccination

Usage

SIRS_vaccination_ode(t, x, params)
SIRS_vaccination_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
lambda <- 0.7
dt <- 1

parameters <- c(beta = beta, tau = tau,
                chi = chi, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIRS_vaccination_ode(1, inits, parameters)
##Model Input
S_u_0 <- 989
I_u_0 <- 1
R_u_0 <- 0
S_v_0 <- 0
I_v_0 <- 0
R_v_0 <- 0
beta <- 3
chi <- 0.5
tau <- 2
lambda <- 0.7
dt <- 1

parameters <- c(beta = beta, tau = tau,
                chi = chi, lambda = 0.7)
inits <- c(S_u = S_u_0, I_u = I_u_0, R_u_0 = R_u_0,
           S_v = S_v_0, I_v = I_v_0, R_v_0 = R_v_0)

SIRS_vaccination_ode(1, inits, parameters)

Susceptible-Infected-Susceptible Model with Simple Demographics

Description

Susceptible-Infected-Susceptible Model with Simple Demographics

Usage

SIS_demographics_ode(t, x, params)
SIS_demographics_ode(t, x, params)

Arguments

`t`	The timestep over which to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
chi <- 2
mu <- 1/81
dt <- 1
parameters <- c(beta = beta, mu = mu)
inits <- c(S = S_0, I = I_0)

SIS_demographics_ode(1, inits, parameters)
##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
chi <- 2
mu <- 1/81
dt <- 1
parameters <- c(beta = beta, mu = mu)
inits <- c(S = S_0, I = I_0)

SIS_demographics_ode(1, inits, parameters)

Susceptible-Infected-Susceptible Model

Description

Susceptible-Infected-Susceptible Model

Usage

SIS_ode(t, x, params)
SIS_ode(t, x, params)

Arguments

`t`	The timestep overwhich to calculate derivatives
`x`	A numeric vector of compartment populations.
`params`	A named vector of parameter values.

Value

A vector of derivatives

Examples

##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
chi <- 2
parameters <- c(beta = beta, chi = chi)
inits <- c(S = S_0, I = I_0)

SIS_ode(1, inits, parameters)
##Model Input
S_0 <- 999
I_0 <- 1
beta <- 3
chi <- 2
parameters <- c(beta = beta, chi = chi)
inits <- c(S = S_0, I = I_0)

SIS_ode(1, inits, parameters)

A Simple Wrapper for lsoda

Description

This function acts as a simple wrapper for lsoda, allowing for multiple parameter sets and initial conditions. It also allows lsoda to be used within the idmodelr framework.

Usage

solve_ode(
  model = NULL,
  inits = NULL,
  params = NULL,
  times = NULL,
  as.data.frame = TRUE,
  ...
)
solve_ode(
  model = NULL,
  inits = NULL,
  params = NULL,
  times = NULL,
  as.data.frame = TRUE,
  ...
)

Arguments

`model`	A model formatted as required by `lsoda`, see `SI_ode` for an example.
`inits`	The initial state (states) of the model. Can either be supplied as a named vector or as a matrix with each row representing a parameter.
`params`	A named vector or matrix of parameters. The matrix must have a row for each parameter and if `inits` is specified as a matrix then `params` must have the same number of columns
`times`	A numeric vector of the times for which explicit model estimates are required, this does not effect the timestep used by the solver
`as.data.frame`	A logical (defaults to `TRUE`) indicating if the results should be returned as a data frame.
`...`	Additional arguments to pass to `lsoda`.

Value

A dataframe or lsoda object containing a single or multiple model trajectories

Examples

## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)
## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)

Summarise a Model Simulation

Description

Provides simple summary statistics for a model produced using solve_ode. These include the final population sizes, the time and size of the maximum epidemic peak, and the duration of the epidemic.

Usage

summarise_model(sim)
summarise_model(sim)

Arguments

sim

A tibble of model output as produced by solve_ode.

Value

A tibble of summary information for a model simulation.

Examples


## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)

summarise_model(sim)
## Intialise
N = 100000
I_0 = 1
S_0 = N - I_0
R_0 = 1.1
beta = R_0

##Time for model to run over
tbegin = 0
tend = 50
times <- seq(tbegin, tend, 1)

##Vectorise input
parameters <- as.matrix(c(beta = beta))
inits <- as.matrix(c(S = S_0, I = I_0))

sim <- solve_ode(model = SI_ode, inits, parameters, times, as.data.frame = TRUE)

summarise_model(sim)

Sum a Stratified Variable

Description

Sum a Stratified Variable

Usage

summarise_strat_var(df, vars, strat = NULL, new_var = "sum")
summarise_strat_var(df, vars, strat = NULL, new_var = "sum")

Arguments

`df`	A dataframe of model output.
`vars`	A character vector containing the unstratified variables to summarise
`strat`	The number of stratifications present in the data set
`new_var`	The name of the summarised variable

Value

Returns the original dataframe with an additional summarised variable

Examples

df <- dplyr::mutate(iris, Petal.Length1 = Petal.Length, Petal.Length2 = Petal.Length)
df <- tibble::as_tibble(df)

summarise_strat_var(df, vars = c("Petal.Length"), strat = 2, new_var = "sum")
df <- dplyr::mutate(iris, Petal.Length1 = Petal.Length, Petal.Length2 = Petal.Length)
df <- tibble::as_tibble(df)

summarise_strat_var(df, vars = c("Petal.Length"), strat = 2, new_var = "sum")

Sum a Stratified Variable by Stratification Level

Description

Sum a Stratified Variable by Stratification Level

Usage

summarise_var_by_strat(df, vars, strat = NULL, new_var, summary_var = FALSE)
summarise_var_by_strat(df, vars, strat = NULL, new_var, summary_var = FALSE)

Arguments

`df`	A dataframe of model output.
`vars`	A character vector containing the unstratified variables to summarise
`strat`	The number of stratifications present in the data set
`new_var`	The name of the summarised variable
`summary_var`	A logical (defaults to `FALSE`), specifying whether to add an additional summary variable across all stratified levels when aggregating incidence.

Details

Takes compartmental infectious disease output and adds summary statistics for each stratified population, optionally adding a final summary statistic for the whole population.

Value

An updated data frame containing the summarised variable for each stratified level and for the whole population.

Examples

df <- data.frame(A = 1, B = 2)
summarise_var_by_strat(df, vars = c("A", "B"), new_var = "C")

df <- data.frame(A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)
summarise_var_by_strat(df, vars = c("A", "B"), strat = 3, new_var = "C")
summarise_var_by_strat(df, vars = c("A", "B"), strat = 3, new_var = "C", summary_var = TRUE)
df <- data.frame(A = 1, B = 2)
summarise_var_by_strat(df, vars = c("A", "B"), new_var = "C")

df <- data.frame(A1 = 1, B1 = 1, A2 = 1, B2 = 1, A3 = 1, B3 = 1)
summarise_var_by_strat(df, vars = c("A", "B"), strat = 3, new_var = "C")
summarise_var_by_strat(df, vars = c("A", "B"), strat = 3, new_var = "C", summary_var = TRUE)

Package 'idmodelr'

Help Index

Adds Pointer Structure to R objects

Description

Usage

Arguments

Value

Examples

A Function to Apply Multiple Aggregations to Model Output

Description

Usage

Arguments

Value

See Also

Examples

An Internal Function to Aggregate Model Output Using other Idmodelr functions.

Description

Usage

Arguments

Value

See Also

Examples

Reduces the Dimensionality of a Stratified Model

Description

Usage

Arguments

Value

See Also

Examples

Combine an Infectious Disease Model To a Demographic Model

Description

Usage

Arguments

Value

See Also

Examples

A Function to Estimate a Normal Distribution from Credible or Confidence Intervals

Description

Usage

Arguments

Value

Examples

A Function to Gather Multiple Stratified Variables into a Tidy Format

Description

Usage

Arguments

Value

Examples

A Function to Gather a Stratified Variable into a Tidy Format

Description

Usage

Arguments

Value

Examples

A Function to Generate Parameter Permutations

Description

Usage

Arguments

Value

Examples

Model Details

Description

Usage

Format

Extracts a Single Column, Summarises if from Simulation

Description

Usage

Arguments

Value

Examples

Parameter Details

Description

Usage

Format

Plot Compartment Populations over Time for a Model Simulation

Description

Usage

Arguments

Value

Examples