Remove infection class

NAMESPACE

@@ -3,19 +3,15 @@
 S3method(c,contacts_intervention)
 S3method(c,rate_intervention)
 S3method(c,vaccination)
-S3method(print,infection)
 S3method(print,intervention)
 S3method(print,population)
 S3method(print,vaccination)
 export(as.intervention)
 export(as.vaccination)
 export(epidemic_size)
 export(get_parameter)
-export(get_transmission_rate)
-export(infection)
 export(intervention)
 export(is_contacts_intervention)
-export(is_infection)
 export(is_intervention)
 export(is_population)
 export(is_rate_intervention)
@@ -28,7 +24,6 @@ export(model_vacamole_r)
 export(new_infections)
 export(no_contacts_intervention)
 export(no_rate_intervention)
-export(no_time_dependence)
 export(no_vaccination)
 export(population)
 export(vaccination)

R/RcppExports.R

@@ -9,72 +9,34 @@
 #' This function is intended to only be called internally from
 #' [model_default_cpp()].
 #'
-#' Allows heterogeneity in social contact patterns, and variable sizes of
-#' demographic groups.
-#' Also allows for group-specific initial proportions in each model
-#' compartment, as well as group-specific vaccination start dates and
-#' vaccination rates, and also group-specific effects of implementing a
-#' non-pharmaceutical intervention.
-#' The model only allows for single, population-wide rates of
-#' transition between the 'susceptible' and 'exposed' compartments, between the
-#' 'exposed' and 'infectious' compartments, and in the recovery rate.
-#'
-#' @param initial_state An `Eigen::MatrixXd` holding the initial state of
-#' each demographic-compartmental combination. Rows must represent demographic
-#' groups, while columns represent compartments in the order S, E, I, R, V.
-#' @param contact_matrix An `Eigen::MatrixXd` holding the population
-#' contact matrix.
-#' @param beta The transmission rate \eqn{\beta}.
-#' @param alpha The rate of transition from exposed to infectious \eqn{\alpha}.
-#' @param gamma The recovery rate \eqn{\gamma}.
-#' @param time_end The maximum time, defaults to 200.0.
-#' @param intervention A non-pharmaceutical intervention applied during the
-#' course of the epidemic, with a start and end time, and age-specific effect
-#' on contacts. See [intervention()].
-#' @param vaccination A vaccination regime followed during the
-#' course of the epidemic, with a start and end time, and age-specific effect
-#' on the transition of individuals from susceptible to vaccinated.
-#' See [vaccination()].
-#' @param increment The increment time, defaults to 0.1.
+#' @param initial_state A matrix for the initial state of the compartments.
+#' @param transmissibility The transmission rate \eqn{\beta} at which
+#' unvaccinated and partially vaccinated individuals are infected by the
+#' disease.
+#' @param infectiousness_rate The rate of transition from exposed to infectious
+#' \eqn{\alpha}.
+#' @param recovery_rate The recovery rate \eqn{\gamma}.
+#' @param contact_matrix The population contact matrix.
+#' @param npi_time_begin The start time of any non-pharmaceutical interventions
+#' .
+#' @param npi_time_end The end time of any non-pharmaceutical interventions.
+#' @param npi_cr The reduction in contacts from any non-pharmaceutical
+#' interventions.
+#' @param vax_time_begin The start time of any vaccination campaigns.
+#' @param vax_time_end The end time of any vaccination campaigns.
+#' @param vax_nu The vaccination rate of any vaccination campaigns.
+#' @param rate_interventions A named list of `<rate_intervention>` objects.
+#' @param time_dependence A named list of functions for parameter time
+#' dependence.
+#' @param time_end The end time of the simulation.
+#' @param increment The time increment of the simulation.
 #' @return A two element list, where the first element is a list of matrices
 #' whose elements correspond to the numbers of individuals in each compartment
 #' as specified in the initial conditions matrix (see [population()]).
 #' The second list element is a vector of timesteps.
 #' @keywords internal
-.model_default_cpp <- function(initial_state, beta, alpha, gamma, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end = 100.0, increment = 1.0) {
-    .Call(`_epidemics_model_default_cpp_internal`, initial_state, beta, alpha, gamma, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end, increment)
-}
-
-#' @title Run an SEIR model with Erlang passage times
-#'
-#' @description Wrapper function for an SEIR compartmental model with Erlang
-#' passage times based on Getz and Dougherty (2017) J. Biological Dynamics,
-#' and developed to model the West African ebola virus disease outbreak of 2014
-#' .
-#'
-#' @param initial_conditions An integer vector of the initial conditions
-#' corresponding to an SEIR model.
-#' @param population_size A single integer for the population size.
-#' @param beta The transmission rate \eqn{\beta}.
-#' @param shape_E A single integer for the shape parameter of the Erlang
-#' distribution of passage times through the exposed compartment.
-#' @param rate_E A single double for the rate parameter of the Erlang
-#' distribution of passage times through the exposed compartment.
-#' @param shape_I A single integer for the shape parameter of the Erlang
-#' distribution of passage times through the infectious compartment.
-#' @param rate_I A single double for the rate parameter of the Erlang
-#' distribution of passage times through the infectious compartment.
-#' @param time_end A single integer for the maximum simulation time; this is
-#' assumed to be in days.
-#' @return A list with two elements, `x`, and integer matrix with as many rows
-#' as the number of timesteps (given by `time_end`), and four columns, one for
-#' each compartment, susceptible, exposed, infectious, recovered, in that order
-#' ; and `times`, a vector of the simulation times, taken to be days.
-#' This output is intended to be passed to [output_to_df()] to be converted
-#' into a data.frame for further analysis.
-#' @keywords internal
-.model_ebola_cpp <- function(initial_state, population_size, beta, shape_E, rate_E, shape_I, rate_I, time_end) {
-    .Call(`_epidemics_model_ebola_cpp_internal`, initial_state, population_size, beta, shape_E, rate_E, shape_I, rate_I, time_end)
+.model_default_cpp <- function(initial_state, transmissibility, infectiousness_rate, recovery_rate, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end = 100.0, increment = 1.0) {
+    .Call(`_epidemics_model_default_cpp_internal`, initial_state, transmissibility, infectiousness_rate, recovery_rate, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end, increment)
 }
 
 #' @title Run the RIVM Vacamole model
@@ -86,61 +48,48 @@
 #' Model code: https://github.com/kylieainslie/vacamole
 #' Manuscript describing the model and its application:
 #' https://doi.org/10.2807/1560-7917.ES.2022.27.44.2101090
+#' This function is intended to only be called internally from
+#' [model_vacamole_cpp()].
 #'
-#' @details The original model has 8 conceptual compartments - four
-#' epidemiological compartments (SEIR), three hospitalisation compartments
-#' (H, ICU, ICU2H), and death - see the manuscript in Eurosurveillance.
-#' Only infected individuals can enter the hospitalisation or death
-#' compartments.
-#' Vacamole was implemented as a stand-alone R package, and some versions have
-#' been used to generate scenarios for the ECDC Covid-19 Scenario Hub.
-#'
-#' Individuals from the susceptible compartment may be vaccinated partially
-#' or fully (assuming a two dose regimen), with only the second dose reducing
-#' their probability of being infected, and of being hospitalised or dying.
-#'
-#' @param population An object of the `population` class, which holds a
-#' population contact matrix, a demography vector, and the initial conditions
-#' of each demographic group. See [population()].
-#' @param beta The transmission rate \eqn{\beta} at which unvaccinated and
-#' partially vaccinated individuals are infected by the disease.
-#' @param beta_v The transmission rate \eqn{\beta_V} at which individuals who
-#' have received two vaccine doses are infected by the disease.
-#' @param alpha The rate of transition from exposed to infectious \eqn{\alpha}.
+#' @param initial_state A matrix for the initial state of the compartments.
+#' @param transmissibility The transmission rate \eqn{\beta} at which
+#' unvaccinated and partially vaccinated individuals are infected by the
+#' disease.
+#' @param transmissibility_vax The transmission rate \eqn{\beta_V} at which
+#' individuals who have received two vaccine doses are infected by the disease.
+#' @param infectiousness_rate The rate of transition from exposed to infectious
+#' \eqn{\alpha}.
 #' This is common to fully susceptible, partially vaccinated, and fully
 #' vaccinated individuals (where fully vaccinated represents two doses).
-#' @param omega The mortality rate of fully susceptible and partially
-#' vaccinated and unprotected individuals.
-#' @param omega_v The mortality rate of individuals who are protected by
-#' vaccination.
-#' @param eta The hospitalisation rate of fully susceptible and partially
+#' @param mortality_rate The mortality rate of fully susceptible and partially
 #' vaccinated and unprotected individuals.
-#' @param eta_v The hospitalisation rate of individuals who are protected by
-#' vaccination.
-#' @param gamma The recovery rate \eqn{\gamma}.
-#' @param time_end The maximum time. Defaults to 100.0.
-#' @param intervention A non-pharmaceutical intervention applied during the
-#' course of the epidemic, with a start and end time, and age-specific effect
-#' on contacts. See [intervention()].
-#' @param vaccination A vaccination regime followed during the
-#' course of the epidemic, with a group- and dose-specific start and end time,
-#' and age-specific rates of delivery of first and second doses.
-#' See [vaccination()].
-#' @param increment The increment time, defaults to 0.1.
+#' @param mortality_rate_vax The mortality rate of individuals who are
+#' protected by vaccination.
+#' @param hospitalisation_rate The hospitalisation rate of fully susceptible
+#' and partially vaccinated and unprotected individuals.
+#' @param hospitalisation_rate_vax The hospitalisation rate of individuals who
+#' are protected by vaccination.
+#' @param recovery_rate The recovery rate \eqn{\gamma}.
+#' @param contact_matrix The population contact matrix.
+#' @param npi_time_begin The start time of any non-pharmaceutical interventions
+#' .
+#' @param npi_time_end The end time of any non-pharmaceutical interventions.
+#' @param npi_cr The reduction in contacts from any non-pharmaceutical
+#' interventions.
+#' @param vax_time_begin The start time of any vaccination campaigns.
+#' @param vax_time_end The end time of any vaccination campaigns.
+#' @param vax_nu The vaccination rate of any vaccination campaigns.
+#' @param rate_interventions A named list of `<rate_intervention>` objects.
+#' @param time_dependence A named list of functions for parameter time
+#' dependence.
+#' @param time_end The end time of the simulation.
+#' @param increment The time increment of the simulation.
 #' @return A two element list, where the first element is a list of matrices
 #' whose elements correspond to the numbers of individuals in each compartment
 #' as specified in the initial conditions matrix (see [population()]).
 #' The second list element is a vector of timesteps.
 #' @keywords internal
-.model_vacamole_cpp <- function(initial_state, beta, beta_v, alpha, omega, omega_v, eta, eta_v, gamma, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end = 100.0, increment = 1.0) {
-    .Call(`_epidemics_model_vacamole_cpp_internal`, initial_state, beta, beta_v, alpha, omega, omega_v, eta, eta_v, gamma, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end, increment)
-}
-
-#' @title Compute the discrete probability of the truncated Erlang distribution
-#' @name prob_discrete_erlang
-#' @rdname prob_discrete_erlang
-#'
-prob_discrete_erlang_cpp <- function(shape, rate) {
-    .Call(`_epidemics_prob_discrete_erlang_cpp`, shape, rate)
+.model_vacamole_cpp <- function(initial_state, transmissibility, transmissibility_vax, infectiousness_rate, mortality_rate, mortality_rate_vax, hospitalisation_rate, hospitalisation_rate_vax, recovery_rate, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end = 100.0, increment = 1.0) {
+    .Call(`_epidemics_model_vacamole_cpp_internal`, initial_state, transmissibility, transmissibility_vax, infectiousness_rate, mortality_rate, mortality_rate_vax, hospitalisation_rate, hospitalisation_rate_vax, recovery_rate, contact_matrix, npi_time_begin, npi_time_end, npi_cr, vax_time_begin, vax_time_end, vax_nu, rate_interventions, time_dependence, time_end, increment)
 }
 

R/check_args_default.R

@@ -8,14 +8,6 @@
 #' `.check_args_model_default()` adds an empty `<intervention>` and
 #' `<vaccination>` object if these are missing from the model arguments.
 #'
-#' `.prepare_args_epidemics_default()` prepares arguments for
-#' [.model_default_cpp()], which is the C++ function that solves the default
-#' ODE system using a Boost _odeint_ solver.
-#' `.prepare_args_epidemics_default()` converts the arguments collected in
-#' `mod_args` into simpler structures such as lists and numeric or integer
-#' vectors that can be interpreted as C++ types such as `Rcpp::List`,
-#' `Rcpp::NumericVector`, or `Eigen::MatrixXd`.
-#'
 #' @return
 #'
 #' `.check_args_model_default()` invisibly returns the model arguments passed
@@ -29,9 +21,9 @@
 #'  - `initial_state`: the initial conditions modified to represent absolute
 #' rather than proportional values;
 #'
-#'  - `beta`, `alpha`, `gamma`: three numbers representing the transmission rate
-#' of the infection, the rate of transition from exposed to infectious, and the
-#' recovery rate, respectively;
+#'  - `transmissibility`, `infectiousness_rate`, `recovery_rate`: three numbers
+#' representing the transmission rate of the infection, the rate of transition
+#' from exposed to infectious, and the recovery rate, respectively;
 #'
 #'  - `contact_matrix`, a numeric matrix for the population contact matrix
 #' scaled by the largest real eigenvalue and by the size of each groups;
@@ -51,19 +43,23 @@
 #' is incremented.
 #'
 #' @keywords internal
+#' @details
+#' `.prepare_args_model_default()` prepares arguments for
+#' [.model_default_cpp()], which is the C++ function that solves the default
+#' ODE system using a Boost _odeint_ solver, and for [.ode_model_default()],
+#' which is passed to [deSolve::lsoda()] in [model_default_r()].
+#'
+#' `.prepare_args_model_default()` converts the arguments collected in
+#' `mod_args` into simpler structures such as lists and numeric or integer
+#' vectors that can be interpreted as C++ types such as `Rcpp::List`,
+#' `Rcpp::NumericVector`, or `Eigen::MatrixXd`.
 .check_args_model_default <- function(mod_args) {
   # check that arguments list has expected names
   checkmate::assert_names(
     names(mod_args),
     type = "unique"
   )
 
-  # input checking on the infection object
-  assert_infection(
-    mod_args[["infection"]],
-    extra_parameters = "preinfectious_period"
-  )
-
   # load number of compartments to check initial conditions matrix
   compartments_default <- c(
     "susceptible", "exposed", "infectious", "recovered", "vaccinated"
@@ -89,7 +85,9 @@
     # check for any other intervention list element names
     checkmate::assert_names(
       names(mod_args[["intervention"]]),
-      subset.of = c("beta", "gamma", "alpha", "contacts")
+      subset.of = c(
+        "transmissibility", "infectiousness_rate", "recovery_rate", "contacts"
+      )
     )
 
     # if a contacts intervention is passed, check it
@@ -107,18 +105,18 @@
     }
 
     # if there is only an intervention on contacts, add a dummy intervention
-    # on the transmission rate beta
+    # on the transmissibility
     if (identical(names(mod_args[["intervention"]]), "contacts")) {
-      mod_args[["intervention"]]$beta <- no_rate_intervention()
+      mod_args[["intervention"]]$transmissibility <- no_rate_intervention()
     }
   } else {
-    # add as a list element named "contacts", and one named "beta"
+    # add as a list element named "contacts", and one named "transmissibility"
     mod_args[["intervention"]] <- list(
       contacts = no_contacts_intervention(
         mod_args[["population"]]
       ),
-      # a dummy intervention on the rate parameter beta
-      beta = no_rate_intervention()
+      # a dummy intervention on the rate parameter transmissibility
+      transmissibility = no_rate_intervention()
     )
   }
 
@@ -163,12 +161,6 @@
     get_parameter(mod_args[["population"]], "initial_conditions") *
       get_parameter(mod_args[["population"]], "demography_vector")
 
-  # calculate infection parameters
-  gamma <- 1.0 / get_parameter(mod_args[["infection"]], "infectious_period")
-  alpha <- 1.0 / get_parameter(mod_args[["infection"]], "preinfectious_period")
-  beta <- get_parameter(mod_args[["infection"]], "r0") /
-    get_parameter(mod_args[["infection"]], "infectious_period")
-
   # get NPI related times and contact reductions
   contact_interventions <- mod_args[["intervention"]][["contacts"]]
   npi_time_begin <- get_parameter(contact_interventions, "time_begin")
@@ -188,7 +180,9 @@
   # return selected arguments for internal C++ function
   list(
     initial_state = initial_state,
-    beta = beta, alpha = alpha, gamma = gamma,
+    transmissibility = mod_args$transmissibility,
+    infectiousness_rate = mod_args$infectiousness_rate,
+    recovery_rate = mod_args$recovery_rate,
     contact_matrix = contact_matrix,
     npi_time_begin = npi_time_begin, npi_time_end = npi_time_end,
     npi_cr = npi_cr,