Package 'daedalus' reference manual

Title:	Model Health, Social, and Economic Costs of a Pandemic
Description:	Model the health, education, and economic costs of directly transmitted respiratory virus pandemics, under different scenarios of prior vaccine investment, policy interventions, and public behavioural change, using the 'DAEDALUS' integrated health-economics model adapted from Haw et al. (2022) <doi:10.1038/s43588-022-00233-0>.
Authors:	Pratik Gupte [aut, cre] (ORCID: <https://orcid.org/0000-0001-5294-7819>), Patrick Doohan [aut] (ORCID: <https://orcid.org/0000-0001-8076-1106>), Robert Johnson [aut] (ORCID: <https://orcid.org/0000-0002-7365-0042>), Pablo Perez-Guzman [aut] (ORCID: <https://orcid.org/0000-0002-5277-5196>), Rich FitzJohn [aut] (ORCID: <https://orcid.org/0000-0001-8888-3837>), Emma Russell [aut], David Mears [aut], Katharina Hauck [aut] (ORCID: <https://orcid.org/0000-0003-3138-4169>), Abdul Latif Jameel Institute for Disease and Emergency Analytics [fnd], Imperial College of Science, Technology and Medicine [cph, fnd]
Maintainer:	Pratik Gupte <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.7
Built:	2026-06-01 09:41:40 UTC
Source:	https://github.com/jameel-institute/daedalus

Represent behaviour-change mechanisms in DAEDALUS

Description

Helper functions to prepare ⁠<daedalus_behaviour>⁠ class responses corresponding to either old or new behavioural change mechanisms. Inherits from ⁠<daedalus_response>⁠ to allow for more control in future over when these mechanisms are active.

Usage

daedalus_old_behaviour(rate = 0.001, lower_limit = 0.2)

daedalus_new_behaviour(
  hospital_capacity,
  behav_effectiveness = 0.5,
  baseline_optimism = 0.5,
  responsiveness = 1.5,
  k0 = 4.59,
  k1 = -9.19
)

is_daedalus_behaviour(x)

## S3 method for class 'daedalus_behaviour'
print(x, ...)
daedalus_old_behaviour(rate = 0.001, lower_limit = 0.2)

daedalus_new_behaviour(
  hospital_capacity,
  behav_effectiveness = 0.5,
  baseline_optimism = 0.5,
  responsiveness = 1.5,
  k0 = 4.59,
  k1 = -9.19
)

is_daedalus_behaviour(x)

## S3 method for class 'daedalus_behaviour'
print(x, ...)

Arguments

rate

The marginal rate of decrease in the scaling factor for each additional daily death in the 'old' behavioural model.

lower_limit

The lower limit of the scaling factor in the 'old' behavioural model; prevents scaling factor from reaching zero.

hospital_capacity

The emergency hospital capacity of a country, but may also be a ⁠<daedalus_country>⁠ object from which the hospital capacity is extracted. See $\bar H$ in Details.

behav_effectiveness

A single double value for the effectiveness of adopting behavioural measures against the risk of infection. Expected to be in the range $[0, 1]$ , where 0 represents no effectiveness, and 1 represents full effectiveness. See $\delta$ in Details. Defaults to 0.5.

baseline_optimism

A single double value for the baseline optimism about pandemic outcomes. Expected to be in the range $[0, 1]$ . See $\bar B$ in Details. Defaults to 0.5.

responsiveness

A single double value for the population responsiveness to an epidemic signal. Must have a lower value of 0, but the upper bound is open. See $k_2$ in Details. Defaults to 1.5.

k0

A single, optional double value which is a scaling parameter for the sigmoidal relationship between $p_t$ and behav_effectiveness. See $k_0$ in Details. Defaults to 4.59.

k1

A single, optional double value which is another scaling parameter for the sigmoidal relationship between $p_t$ and behav_effectiveness. See $k_1$ in Details. Defaults to -9.19. Expected to be a negative number.

x

A ⁠<daedalus_behaviour>⁠ object.

...

Other arguments passed to format().

Details

Daedalus currently supports two behavioural models (and the option to have neither model active). These models simulate population-level changes towards adopting behaviours that help reduce the risk of infection, which might be expected during a major disease outbreak.

In both models, the transmission rate of the infection $\beta$ is reduced for all transmissions by a scaling factor; the models differ in how the scaling factor is calculated.

Note that a major issue with including this in a model run (any value other than "off") is that it leads to substantially lower response costs, and generally better health outcomes (lives lost), without accounting for any attendant economic or social costs. As such, please treat the behavioural models as experimental.

Deaths-based public concern model

This model existed prior to ⁠daedalus v0.2.25⁠ and is referred to as the "old" behavioural model.

The scaling factor for $\beta$ in this model is

$(1 - \text{rate})^{\Delta D} \times (1 - L) + L$

where $\text{rate}$ is the marginal rate of reduction for each additional death per day ( $\Delta D$ ), and $L$ is the lower limit of the scaling factor to prevent the FOI going to zero.

Optimism-responsiveness-effectiveness model

This model is referred to as the "new" behavioural model, and the scaling factor for $\beta$ is given by

$\mathcal{B}(p_t, \delta) = p_t(1 - \delta)[p_t(1 - \delta) + (1 - p_t)] + (1 - p_t)[p_t(1 - \delta) + (1 - p_t)]$

where $p_t$ is the proportion of the population taking protective behaviour and $\delta$ is the effectiveness of the behaviour, both in the range $[0, 1]$ . When either is zero, $\beta$ is not scaled down.

$p_t$ is computed during the model run, and is given by

$p_t = \frac{1}{1 + \text{exp}\left\{-\left(k_0 + k_1 \bar B + k_2 \frac{H_t}{\bar H}\right)\right\}}$

where $k_0 = 4.59, k_1 = -9.19, k_2$ are scaling parameters. $k_0, k_1$ have constant values that cannot be changed by the user, and these have been chosen to produce a sigmoidal relationship between $\bar B$ and $p_t$ .

$k_2$ a constant user-supplied parameter that should be interpreted as population responsiveness to a signal of epidemic severity $H_t / \bar H$ , which is the relative burden on hospital capacity ( $H_t$ : hospital demand at time $t$ , and $\bar H$ : emergency hospital capacity).

$\bar B$ is a constant user-supplied parameter that captures the baseline population optimism about the outbreak.

Value

An object of class ⁠<daedalus_behaviour>⁠ which inherits from ⁠<daedalus_response>⁠. This is primarily a list holding behavioural parameters.

Represent countries and territories for DAEDALUS

Description

Helper functions to create and work with S3 class ⁠<daedalus_country>⁠ objects for use with daedalus(). These objects store country parameters for reuse and have methods for easy parameter access and editing, as well as processing raw country characteristics for the DAEDALUS model.

Usage

daedalus_country(country, contact_matrix = NULL, group_working_age = NULL)

is_daedalus_country(x)

## S3 method for class 'daedalus_country'
print(x, ...)
daedalus_country(country, contact_matrix = NULL, group_working_age = NULL)

is_daedalus_country(x)

## S3 method for class 'daedalus_country'
print(x, ...)

Arguments

country

A string giving the country or territory name, or ISO2 or ISO3 code; must be from among daedalus.data::country_codes_iso2c or daedalus.data::country_codes_iso3c or daedalus.data::country_names.

contact_matrix

An optional named list of numeric matrices, representing age-specific contact matrices in different contexts or settings. Defaults to NULL, which selects the country-specific contact matrix provided in daedalus.data::country_data.

group_working_age

An optional value for the age-group that is considered to be the working-age group. Defaults to 3, which is taken from an internal constant.

x

An object of the ⁠<daedalus_country>⁠ class.

...

Other parameters passed to print().

Value

daedalus_country() returns an object of the S3 class ⁠<daedalus_country>⁠
is_daedalus_country() returns a logical for whether an object is a ⁠<daedalus_country>⁠.
print.daedalus_country() invisibly returns the ⁠<daedalus_country>⁠ object x. Called for printing side-effects.

Examples

x <- daedalus_country("Canada")

x

# check whether `x` is a <country> object
is_daedalus_country(x)

# using assignment operators; must be assigned as a list
x$contact_matrix <- list(home = matrix(99, 4, 4), school = matrix(1, 4, 4))
x

x <- daedalus_country("Canada")

x

# check whether `x` is a <country> object
is_daedalus_country(x)

# using assignment operators; must be assigned as a list
x$contact_matrix <- list(home = matrix(99, 4, 4), school = matrix(1, 4, 4))
x

Create and work with `⁠<daedalus_output>⁠` objects

Description

Create and work with ⁠<daedalus_output>⁠ objects

Print ⁠<daedalus_output>⁠ class objects

Usage

as_daedalus_output(x)

validate_daedalus_output(x)

is_daedalus_output(x)

## S3 method for class 'daedalus_output'
print(x, ...)
as_daedalus_output(x)

validate_daedalus_output(x)

is_daedalus_output(x)

## S3 method for class 'daedalus_output'
print(x, ...)

Arguments

x

An object of the ⁠<daedalus_output>⁠ class.

...

Not used; added for compatibility with the generic.

Value

An object of the ⁠<daedalus_output>⁠ class. Holds the raw ODE solution and ODE events logs in dust2 forma), as well as country, infection, vaccination, and behaviour classes representing input arguments to daedalus().

Invisibly returns x; called primarily for its side effects of erroring when the object does not satisfy the ⁠<daedalus_output>⁠ class requirements.

A logical for whether x is of the ⁠<daedalus_output>⁠ class.

None; called for its printing side effects.

Represent infection parameters for DAEDALUS

Description

Helper functions to create and work with S3 class ⁠<daedalus_infection>⁠ objects for use with daedalus(). These objects store infection parameters for reuse and have methods for easy parameter access and editing, as well as processing raw infection characteristics for the DAEDALUS model.

Usage

daedalus_infection(name, ...)

is_daedalus_infection(x)

## S3 method for class 'daedalus_infection'
print(x, ...)
daedalus_infection(name, ...)

is_daedalus_infection(x)

## S3 method for class 'daedalus_infection'
print(x, ...)

Arguments

name

An epidemic name from among daedalus.data::epidemic_names. Selecting an epidemic automatically pulls in infection parameters associated with the epidemic; these are stored as packaged data in daedalus.data::infection_data. Default infection parameters for epidemics can be over-ridden by passing them as a named list to ....

...

Other parameters passed to print().

x

An object of the ⁠<daedalus_infection>⁠ class.

Details

Included epidemics

Epidemics for which data are available are given below (pathogen in parentheses). The string indicates the name that must be passed to the name argument.

"sars_cov_1": SARS 2004 (SARS-CoV-1),
"influenza_2009": influenza 2009 (influenza A H1N1),
"influenza_1957": influenza 1957 (influenza A H2N2),
"influenza_1918": influenza 1918 (influenza A H1N1),
"sars_cov_2_pre_alpha": Covid-19 wild type (SARS-Cov-2 wild type),
"sars_cov_2_omicron": Covid-19 Omicron (SARS-CoV-2 omicron),
"sars_cov_2_pre_delta": (SARS-CoV-2 delta).

Infection parameters

All infections have the following parameters, which take default values stored in the package under daedalus.data::infection_data. Users can pass custom values for these parameters as arguments via ....

r0: A single numeric value for the basic reproduction value of the infection $R_0$ .
sigma: A single numeric value > 0.0 for the rate of transition from the exposed compartment to one of two infectious compartments.
p_sigma: A single numeric value in the range $[0.0, 1.0]$ for the proportion of infectious individuals who are also symptomatic. Asymptomatic individuals can have a different contribution to the force of infection from symptomatic individuals.
epsilon: A single numeric value for the relative contribution of asymptomatic infectious individuals to the force of infection (compared to symptomatic individuals).
gamma_Is: A single numeric value for the recovery rate of infectious individuals who are not hospitalised.
gamma_Ia: A single numeric value for the recovery rate from asymptomatic infection.
ifr: A numeric vector of length N_AGE_GROUPS (4) for the age-specific infection fatality risk.
eta: A numeric vector of length N_AGE_GROUPS (4) for the age-specific hospitalisation rate for individuals who are infectious and symptomatic.
hfr: A numeric vector of length N_AGE_GROUPS (4) for the age-specific probability of death conditional on hospitalisation.
gamma_H_recovery: A single numeric value for the recovery rate of hospitalised individuals.
gamma_H_death: A single numeric value for the death rate of hospitalised individuals.
rho: A single numeric value for the rate at which infection-derived immunity wanes, returning individuals in the 'recovered' compartment to the 'susceptible' compartment.

Value

daedalus_infection() returns an object of the S3 class ⁠<daedalus_infection>⁠.
is_daedalus_infection() returns a logical for whether an object is a ⁠<daedalus_infection>⁠.
print.daedalus_infection() invisibly returns the ⁠<daedalus_infection>⁠ object x. Called for printing side-effects.

Examples

# make a <daedalus_infection> object with default parameter values
daedalus_infection("sars_cov_1")

# modify infection parameters R0 and immunity waning rate
daedalus_infection("influenza_1918", r0 = 2.5, rho = 0.01)
# make a <daedalus_infection> object with default parameter values
daedalus_infection("sars_cov_1")

# modify infection parameters R0 and immunity waning rate
daedalus_infection("influenza_1918", r0 = 2.5, rho = 0.01)

Represent non-pharmaceutical intervention strategies for DAEDALUS

Description

Helper functions to create and work with S3 class ⁠<daedalus_npi>⁠ objects for use with daedalus(). These objects store NPI parameters for reuse and have methods for easy parameter access and editing, as well as processing raw NPI characteristics for the DAEDALUS model.

Most NPIs must be initialised with an associated country and infection; these are used to determine flag positions as well as the state-values (hospital capacity) and incidence-prevalence ratio (IPR) at which the NPI starts and ends reactively.

daedalus_timed_npi() is a helper function to create a ⁠<daedalus_npi>⁠ that is only trigged by time, and is not responsive to state variables. Primarily intended for use in real time modelling.

Usage

daedalus_npi(
  name,
  country,
  infection,
  openness = NULL,
  start_time = 30,
  end_time = NULL,
  max_duration = 365
)

is_daedalus_npi(x)

## S3 method for class 'daedalus_npi'
print(x, ...)

daedalus_timed_npi(start_time, end_time, openness, country)
daedalus_npi(
  name,
  country,
  infection,
  openness = NULL,
  start_time = 30,
  end_time = NULL,
  max_duration = 365
)

is_daedalus_npi(x)

## S3 method for class 'daedalus_npi'
print(x, ...)

daedalus_timed_npi(start_time, end_time, openness, country)

Arguments

name

An NPI strategy name from among daedalus.data::closure_strategy_names, or NA. Passing a pre-defined strategy name automatically pulls in openness parameters for the associated response strategy; these are stored as packaged data in daedalus.data::closure_strategy_data.

Pass NA to define a custom response strategy by passing a vector to openness.

country

A country or territory object of class ⁠<daedalus_country>⁠, or a country or territory name from those included in the package; see daedalus.data::country_names, or a country ISO2 or ISO3 code; see daedalus.data::country_codes_iso2c and daedalus.data::country_codes_iso3c. Country-specific data such as the community and workplace contacts, the demography, and the distribution of the workforce into economic sectors is automatically accessed from package data for the relevant country name if it is passed as a string. To override package defaults for country characteristics, pass a ⁠<daedalus_country>⁠ object instead. See daedalus_country() for more.

infection

An infection parameter object of the class ⁠<daedalus_infection>⁠, or an epidemic name for which data are provided in the package; see daedalus.data::epidemic_names for historical epidemics or epidemic waves for which parameters are available. Passing the name as a string automatically accesses the default parameters of an infection. Create and pass a ⁠<daedalus_infection>⁠ to tweak infection parameters using daedalus_infection().

openness

For daedalus_npi(), an optional numeric matrix giving the openness of each setting in the model when the NPI is in effect.

For daedalus_timed_npi(), a list of numeric matrices giving the openness coefficients for each interval specified by corresponding elements of start_time and end_time.

Expected to have a minimum size of N_ECON_SECTORS + N_AGE_GROUPS (currently 98), corresponding to two settings with 49 strata per setting, with all values are in the range $[0, 1]$ ,

start_time

For daedalus_npi(), a single number giving the number of days after the start of the model that the NPI response begins. Defaults to 30.

For daedalus_timed_npi(): may be a vector of NPI start times, with no default values.

end_time

For daedalus_npi(), a single number giving the number of days after the start of the model that the NPI response ends. Defaults to NULL, indicating that the response ends (if no other condition is met), at the maximum duration.

For daedalus_timed_npi(): may be a vector of NPI end times, with no default values. Timed NPIs have no default duration.

max_duration

The maximum number of days that an NPI response is active whether started by passing the start_time or when a state threshold is crossed. Defaults to 365 days. NPIs created using daedalus_timed_npi() have no default maximum duration and are encoded by pairs of start and end times.

x

An object to be tested or printed as a ⁠<daedalus_npi>⁠.

...

For daedalus_npi(), other parameters passed to new_daedalus_response(). For the print method, other parameters passed to print().

Details

Note that NPIs created using daedalus_npi() are reactive to the model state (i.e., the epidemic state), and can trigger multiple times when state conditions are met.

Value

daedalus_npi(): A ⁠<daedalus_npi>⁠ class object that specifies an NPI that may be responsive to both model state and time.

daedalus_timed_npi(): A ⁠<daedalus_npi>⁠ class object which specifies time-limited interventions only.

Examples

# for a school closure strategy
daedalus_npi("school_closures", "GBR", "sars_cov_1")

# set custom openness
daedalus_npi(
  NA,
  "GBR", "sars_cov_1",
  openness = cbind(rep(1, 49), rep(0.1, 49))
)

# time-limited NPI with multiple phases
phase_1 <- cbind(
  rep(1, 49),
  rep(0.5, 49)
)
phase_2 <- cbind(
  rep(1, 49),
  rep(0.3, 49)
)
phase_3 <- cbind(
  rep(1, 49),
  rep(0.8, 49)
)

daedalus_timed_npi(
  start_time = c(10, 20, 30),
  end_time = c(15, 25, 40),
  openness = list(
    phase_1, phase_2, phase_3
  ),
  country = "GBR"
)

# for a school closure strategy
daedalus_npi("school_closures", "GBR", "sars_cov_1")

# set custom openness
daedalus_npi(
  NA,
  "GBR", "sars_cov_1",
  openness = cbind(rep(1, 49), rep(0.1, 49))
)

# time-limited NPI with multiple phases
phase_1 <- cbind(
  rep(1, 49),
  rep(0.5, 49)
)
phase_2 <- cbind(
  rep(1, 49),
  rep(0.3, 49)
)
phase_3 <- cbind(
  rep(1, 49),
  rep(0.8, 49)
)

daedalus_timed_npi(
  start_time = c(10, 20, 30),
  end_time = c(15, 25, 40),
  openness = list(
    phase_1, phase_2, phase_3
  ),
  country = "GBR"
)

Represent vaccine investment scenarios for DAEDALUS

Description

Helper functions to create and work with S3 class ⁠<daedalus_vaccination>⁠ objects for use with daedalus(). These objects store vaccination parameters for reuse and have methods for easy parameter access and editing, as well as processing raw vaccination characteristics for the DAEDALUS model.

Usage

daedalus_vaccination(
  name,
  country,
  start_time = NULL,
  rate = NULL,
  uptake_limit = NULL,
  efficacy = 50,
  waning_period = 180
)

is_daedalus_vaccination(x)

## S3 method for class 'daedalus_vaccination'
print(x, ...)
daedalus_vaccination(
  name,
  country,
  start_time = NULL,
  rate = NULL,
  uptake_limit = NULL,
  efficacy = 50,
  waning_period = 180
)

is_daedalus_vaccination(x)

## S3 method for class 'daedalus_vaccination'
print(x, ...)

Arguments

name

A vaccination investment scenario name as a string. Passing a name from among daedalus.data::vaccination_scenario_names leads to any optional parameters (see below) being drawn from pre-canned scenarios. These are stored as packaged data in daedalus.data::vaccination_scenario_data.

Passing a name that is not among pre-canned scenarios causes all following parameters to become compulsory and non-optional.

country

A ⁠<daedalus_country>⁠ object or a 2- or 3-character string that can be coerced to a ⁠<daedalus_country>⁠ (e.g. "GBR" for the United Kingdom). Used to determine when vaccination should end.

start_time

The number of days after the start of the epidemic that vaccination begins. Must be a single number. Defaults to NULL and is optional if name is a pre-canned strategy. If so, the start time is taken from the vaccination scenarios specified by name. Passed to the time_on argument in new_daedalus_response() via the class constructor new_daedalus_vaccination().

rate

A single number for the percentage of the total population that can be vaccinated each day. This is converted into a proportion automatically within daedalus(). Defaults to NULL and is optional if name is a pre-canned strategy; if so rate is taken from pre-canned scenario data.

uptake_limit

A single number giving the upper limit for the percentage of the population that can be vaccinated. When this limit is reached, vaccination ends. Passed to the value_state_off argument in new_daedalus_response() via the class constructor new_daedalus_vaccination(). Defaults to NULL and is optional if name is a pre-canned strategy in which case uptake_limit is taken from pre-canned scenario data.

efficacy

A single number in the range ⁠[0, 100]⁠ giving the efficacy of vaccination in preventing infection. A value of 0 indicates that vaccinated individuals are as susceptible to infection as unvaccinated ones, while 100 would indicate completely non-leaky vaccination that completely protects against infection. Defaults to 50%.

waning_period

A single number representing the number of days over which the average individual wanes out of the vaccinated stratum to the unvaccinated stratum. Only individuals in the susceptible and recovered compartments can wane out of being vaccinated. Defaults to 180 days.

x

An object to be tested or printed as a ⁠<daedalus_vaccination>⁠.

...

For daedalus_vaccination(), other parameters passed to new_daedalus_response(). For the print method, other parameters passed to print().

Details

Note that vaccination once ended by reaching the uptake_limit does not restart once individuals wane out of the vaccinated compartment.

Examples

# for no advance vaccine investment in the UK
daedalus_vaccination("none", "GBR")

# modifying parameters for pre-defined strategies during initialisation
# set daily vaccination rate to 1.5% of population
daedalus_vaccination("low", "GBR", rate = 1.5)

# a fully customised strategy
daedalus_vaccination(
  "custom", "GBR",
  start_time = 100, rate = 1.5, uptake_limit = 70
)
# for no advance vaccine investment in the UK
daedalus_vaccination("none", "GBR")

# modifying parameters for pre-defined strategies during initialisation
# set daily vaccination rate to 1.5% of population
daedalus_vaccination("low", "GBR", rate = 1.5)

# a fully customised strategy
daedalus_vaccination(
  "custom", "GBR",
  start_time = 100, rate = 1.5, uptake_limit = 70
)

DAEDALUS model for health, social and economic costs of a pandemic

Description

Run the DAEDALUS model from R. This is a work in progress.

Usage

daedalus(
  country,
  infection,
  response_strategy = NULL,
  vaccine_investment = NULL,
  behaviour = NULL,
  response_time = 30,
  initial_state_manual = NULL,
  time_end = 600,
  ...
)
daedalus(
  country,
  infection,
  response_strategy = NULL,
  vaccine_investment = NULL,
  behaviour = NULL,
  response_time = 30,
  initial_state_manual = NULL,
  time_end = 600,
  ...
)

Arguments

country

infection

response_strategy

A string for the name of response strategy followed, a numeric of length 45 (number of economic sectors), a matrix with dimensions 49 x N where N is the number of contacts settings, or a ⁠<daedalus_npi>⁠ object. Defaults to NULL, which simulates an unmitigated epidemic. While the response strategy is active, economic contacts are scaled using the package data object daedalus.data::closure_strategy_data.

vaccine_investment

Either a single string or a ⁠<daedalus_vaccination>⁠ object specifying the vaccination parameters associated with an advance vaccine-investment scenario. Defaults to NULL for absolutely no vaccination in the model. A vaccination investment of "none" indicates no prior investment, but the model will include vaccination beginning after 1 year, at a low rate across all age groups. Other accepted values are "low", "medium" and "high". See daedalus_vaccination() for more information.

behaviour

An optional object of class ⁠<daedalus_behaviour>⁠ which determines how population-level perception of epidemic signals affects infection transmission. May be NULL for no behavioural modification of infection transmission. See the ⁠<daedalus_behaviour>⁠ class documentation for more details on the available behavioural mechanisms

response_time

A single numeric value for the time in days at which the selected response is activated. This is ignored if the response has already been activated by the hospitalisation threshold being reached. Defaults to 30 days. Responses have a default maximum duration of 365 days. This can be changed by passing a ⁠<daedalus_npi>⁠ object to response_strategy.

initial_state_manual

An optional named list with the names p_infectious, p_asymptomatic, and p_immune. p_infectious and p_asymptomatic give the proportion of infectious and symptomatic individuals in each age group and economic sector. Defaults to 1e-6 and 0.0 respectively. p_immune may be a single number in the range ⁠0.0 <= p_immune <= 1.0⁠ or a 4-element vector in that range (the number of age groups in the model), for the proportion of individuals in the population or in each age group that have some pre-existing immunity to infection (reduced susceptibility). See Details for more.

time_end

An integer-like value for the number of timesteps at which to return data. This is treated as the number of days with data returned for each day. Defaults to 300 days.

...

Optional arguments that are passed to dust2::dust_ode_control().

Details

Details: Initial state

Users can pass the following initial state parameters to initial_state_manual:

p_infectious: A single numeric value in the range $[0.0, 1.0]$ giving the proportion of individuals in each age group and economic sector that are to be initialised as infectious. Defaults to 1e-6, or one in every one million as infectious.
p_asymptomatic: A single numeric value in the range $[0.0, 1.0]$ for the proportion of initially infectious individuals who are considered to be asymptomatic. Defaults to 0.0.
p_immune: Either a single number or a vector of 4 elements (the number of age groups) in the range $[0.0, 1.0]$ for the proportion of the population (or each age group) that is considered to have pre-existing immunity. This is a stop-gap implementation that assumes one of two cases: (1) if no vaccination is intended in the model and vaccine_investment is NULL, the susceptibility of individuals pre-existing immunity is 50%; or (2) if a vaccination strategy is specified, the pre-existing immunity is assumed to be from a prior rollout, and the susceptibility is determined by the chosen vaccination strategy (as 1 - efficacy).

Value

A ⁠<daedalus_output>⁠ object if infection is a string or a single ⁠<daedalus_infection>⁠ object.

Examples

# country and infection specified by strings using default characteristics
output <- daedalus(
  "Canada", "influenza_1918"
)

# print output
output

# country passed as <daedalus_country> with a new 'total' contact matrix
conmat_new <- get_data(daedalus_country("Canada"), "contact_matrix")
country_x <- daedalus_country(
  "Canada",
  contact_matrix = list(total = conmat_new * 2.0) # simulate new data
)
output <- daedalus(country_x, "influenza_1918")

# with some infection parameters over-ridden by the user
output <- daedalus(
  "United Kingdom",
  daedalus_infection("influenza_1918", r0 = 1.3)
)

# with default initial conditions over-ridden by the user
output <- daedalus(
  "United Kingdom", "influenza_1918",
  initial_state_manual = list(p_infectious = 1e-3)
)

# including behavioural modification
output <- daedalus(
  "Canada", "influenza_1918",
  behaviour = daedalus_old_behaviour(),
  time_end = 100
)

# country and infection specified by strings using default characteristics
output <- daedalus(
  "Canada", "influenza_1918"
)

# print output
output

# country passed as <daedalus_country> with a new 'total' contact matrix
conmat_new <- get_data(daedalus_country("Canada"), "contact_matrix")
country_x <- daedalus_country(
  "Canada",
  contact_matrix = list(total = conmat_new * 2.0) # simulate new data
)
output <- daedalus(country_x, "influenza_1918")

# with some infection parameters over-ridden by the user
output <- daedalus(
  "United Kingdom",
  daedalus_infection("influenza_1918", r0 = 1.3)
)

# with default initial conditions over-ridden by the user
output <- daedalus(
  "United Kingdom", "influenza_1918",
  initial_state_manual = list(p_infectious = 1e-3)
)

# including behavioural modification
output <- daedalus(
  "Canada", "influenza_1918",
  behaviour = daedalus_old_behaviour(),
  time_end = 100
)

Get epidemic losses from a DAEDALUS model run

Description

Calculate the costs of an epidemic and any mitigation measures, either from a Daedalus model run, or from a timeseries of epidemic data and relevant demographic and economic parameters.

Usage

## S3 method for class 'daedalus_output'
get_costs(
  x,
  summarise_as = c("none", "total", "domain"),
  productivity_loss_infection = 1,
  ...
)

## S3 method for class 'data.frame'
get_costs(
  x,
  comp_non_working,
  comp_infected,
  comp_dead,
  workforce,
  daily_gva,
  productivity_loss_infection,
  vsl_by_age,
  life_expectancy,
  value_school_year,
  n_students,
  edu_effectiveness_remote,
  npi_data = NULL,
  summarise_as = c("none", "total", "domain"),
  ...
)

get_costs(x, ...)
## S3 method for class 'daedalus_output'
get_costs(
  x,
  summarise_as = c("none", "total", "domain"),
  productivity_loss_infection = 1,
  ...
)

## S3 method for class 'data.frame'
get_costs(
  x,
  comp_non_working,
  comp_infected,
  comp_dead,
  workforce,
  daily_gva,
  productivity_loss_infection,
  vsl_by_age,
  life_expectancy,
  value_school_year,
  n_students,
  edu_effectiveness_remote,
  npi_data = NULL,
  summarise_as = c("none", "total", "domain"),
  ...
)

get_costs(x, ...)

Arguments

x

A ⁠<daedalus_output>⁠ object from a call to daedalus(), or a ⁠<data.frame>⁠ of an epidemic timeseries.

If x is a ⁠<data.frame>⁠, it must have the columns "time", "compartment", "age_group", "econ_sector" and "value", giving the model time, the epidemiological compartment, and the number of individuals of each age group and economic sector in each compartment at each time. See Details for more on the expectations around how this dataset is organised.

summarise_as

A string from among "none", "total", or "domain", for how the costs should be returned. Select "none", the default, for the raw costs along with overall and domain-specific totals; "total" for the overall cost, and "domain" for the total costs per domain; the domains are 'economic', 'education', and 'life years'.

productivity_loss_infection

A single number in the range $[0, 1]$ giving the loss in productivity associated with symptomatic infection. Currently defaults to 1.0 for compatibility with earlier function versions.

...

<dynamic-dots> Other arguments to class methods.

comp_non_working

A character vector giving the names of compartments in which individuals are assumed to have a reduced ability to work. This may (and should) include compartments where individuals are infected and symptomatic, as well as hospitalised or dead.

comp_infected

A character vector giving the name of compartments in which individuals are assumed to be infected with reduced ability to work. This should not include compartments where individuals are assumed to have no ability to work (hospitalised or dead).

comp_dead

A string giving the name of the compartment holding the count of deaths. Note that only a single deaths compartment is supported, so models with multiple death states should pass the final state. Used to calculate the value of lives lost.

workforce

A numeric vector of the number of workers in each economic sector of the population. Must be absolute numbers (i.e., not scaled to thousands or millions). Must have the same length as daily_gva.

daily_gva

A numeric vector of the daily gross-value added (GVA) in million dollars, by each economic sector of the population (or territory or state) in which the epidemic occurs.

vsl_by_age

A numeric vector of the value of a statistical life (VSL) for each age age group in x.

life_expectancy

A numeric vector of the life-expectancy per age group. Expected to be the same length as vsl_by_age.

value_school_year

A single number giving the value of a school year. Users can use the helper function get_value_school_year() if a population's gross income per-capita is known to apply the internal Daedalus model for valuing a school year, or a value obtained in some other way.

n_students

A single number for the number of students in the population, whose education is expected to be impacted by the epidemic.

edu_effectiveness_remote

A single number for the effectiveness of remote education, in the range $[0, 1]$ .

npi_data

A list with numeric elements that mirrors the elements of a ⁠<daedalus_npi>⁠, with the following names:

npi_times_start: A numeric vector of the start times of any NPIs modelled.
npi_times_end: A numeric vector of the end times of modelled NPIs.
npi_durations: A numeric vector of the durations of NPIs.
npi_periods: A numeric vector giving integer-ish elements in the ranges specified by the start and end times.
openness: Either a numeric vector (if modelling a single NPI), or a list of numeric vectors (if modelling multiple NPIs), giving the openness coefficients of NPI regimes. Vector lengths must be the same as workforce.

Details

Expectations for non-Daedalus model data

When a ⁠<data.frame> x⁠ is passed, it must have at least one row, at least some non-missing values, and:

Columns "time", "age_group", "compartment", "econ_sector", and "value", giving the model time (numeric), age group identifier (character), epidemiological compartment identifier (character), economic sector identifier (character), and the value of the number of individuals in each age, epi-compartment, and economic group at each time (numeric). Numeric values must be non-negative $> 0$ , non-missing, and finite.
Compartments in comp_infected, comp_non_working, and comp_dead must be found in x$compartment.
All compartments of comp_infected and comp_dead must be in comp_non_working.

Output

The total cost in million dollars is returned as total_cost. This is comprised of the following costs.

Economic costs

A three element list of economic_cost_total, the total costs from pandemic impacts on economic sectors, including both costs of lost gross value added (GVA) due to pandemic-control restrictions or closures (economic_cost_closures), and pandemic-related absences due to illness and death (economic_cost_absences).

Educational costs

A three element list of education_cost_total, the total costs from pandemic impacts on education due to pandemic-control restrictions or closures (education_cost_closures), and pandemic-related absences due to illness and death (education_cost_absences).

Life-value lost

A four-element vector (for the number of age groups) giving the value of life-years lost per age group. This is calculated as the life-expectancy of each age group times the value of a statistical life, with all years assumed to have the same value.

Life-years lost

A four-element vector (for the number of age groups) giving the value of life-years lost per age group. This is calculated as the life-expectancy of each age group times the number of deaths in that age group. No quality adjustment is applied.

Value

A list of different cost values, including the total cost. See Details for more information.

Examples

# for <daedalus_output> objects from `daedalus()`
o <- daedalus("CAN", "sars_cov_1", time_end = 100)
get_costs(o, "domain")

# for a data.frame of epi compartment timeseries, as from any epi model
output <- daedalus("Canada", "influenza_1918", time_end = 100)

data <- get_data(output)
comp_non_working <- c(
  "infect_symp",
  "hospitalised_recov",
  "hospitalised_death",
  "dead"
)
comp_infected <- "infect_symp"
comp_dead <- "dead"

daily_gva <- output$country$gva
workforce <- output$country$workers
vsl_by_age <- output$country$vsl
life_expectancy <- output$country$life_expectancy

value_school_year <- 1e6 # 1 million dollars
n_students <- output$country$demography[3L]

edu_effectiveness_remote <- 0.33

productivity_loss_infection <- 1.0

get_costs(
  data,
  comp_non_working,
  comp_infected,
  comp_dead,
  daily_gva,
  workforce,
  vsl_by_age,
  life_expectancy,
  value_school_year,
  n_students,
  productivity_loss_infection = productivity_loss_infection,
  edu_effectiveness_remote = edu_effectiveness_remote,
  summarise_as = "domain"
)

# for <daedalus_output> objects from `daedalus()`
o <- daedalus("CAN", "sars_cov_1", time_end = 100)
get_costs(o, "domain")

# for a data.frame of epi compartment timeseries, as from any epi model
output <- daedalus("Canada", "influenza_1918", time_end = 100)

data <- get_data(output)
comp_non_working <- c(
  "infect_symp",
  "hospitalised_recov",
  "hospitalised_death",
  "dead"
)
comp_infected <- "infect_symp"
comp_dead <- "dead"

daily_gva <- output$country$gva
workforce <- output$country$workers
vsl_by_age <- output$country$vsl
life_expectancy <- output$country$life_expectancy

value_school_year <- 1e6 # 1 million dollars
n_students <- output$country$demography[3L]

edu_effectiveness_remote <- 0.33

productivity_loss_infection <- 1.0

get_costs(
  data,
  comp_non_working,
  comp_infected,
  comp_dead,
  daily_gva,
  workforce,
  vsl_by_age,
  life_expectancy,
  value_school_year,
  n_students,
  productivity_loss_infection = productivity_loss_infection,
  edu_effectiveness_remote = edu_effectiveness_remote,
  summarise_as = "domain"
)

Run daedalus for multiple infection parameter sets

Description

Run daedalus() for multiple parameter sets, with the intended use case of running the model under uncertainty in infection parameters.

Usage

daedalus_multi_infection(
  country,
  infection,
  response_strategy = NULL,
  vaccine_investment = NULL,
  behaviour = NULL,
  response_time = 30,
  initial_state_manual = NULL,
  time_end = 600,
  ...
)
daedalus_multi_infection(
  country,
  infection,
  response_strategy = NULL,
  vaccine_investment = NULL,
  behaviour = NULL,
  response_time = 30,
  initial_state_manual = NULL,
  time_end = 600,
  ...
)

Arguments

country

infection

A list of ⁠<daedalus_infection>⁠ objects. Must have a minimum length of 2.

response_strategy

vaccine_investment

behaviour

response_time

initial_state_manual

time_end

An integer-like value for the number of timesteps at which to return data. This is treated as the number of days with data returned for each day. Defaults to 300 days.

...

Optional arguments that are passed to dust2::dust_ode_control().

Value

A list of ⁠<daedalus_output>⁠ objects of the same length as infection.

Economic constants used in DAEDALUS

Description

Economic constants used in DAEDALUS

Usage

EDU_EFFECTIVENESS_REMOTE

EDU_ANNUAL_ROR

WORK_EXPECTED_YEARS

EARNINGS_LOSS_DISCOUNT
EDU_EFFECTIVENESS_REMOTE

EDU_ANNUAL_ROR

WORK_EXPECTED_YEARS

EARNINGS_LOSS_DISCOUNT

Format

An object of class numeric of length 1.

Details

The Daedalus model uses these constants for economic value calculations:

edu_effectiveness_remote: The effectiveness of remote education.
edu_annual_ror: The rate of return on a year of education.
work_expected_years: The expected number of years of work for school-age children.
earnings_loss_discount: The discounting rate used in the calculation of lost earnings.

Epidemiological compartments and indices

Description

Names and indices for the epidemiological compartments used in DAEDALUS, for reuse in model code. The duplication is for ease of extracting indices from a named list, and of extracting names without having to call names().

Usage

COMPARTMENTS

idx_COMPARTMENTS

N_EPI_COMPARTMENTS

i_EPI_COMPARTMENTS

N_MODEL_COMPARTMENTS

N_INFECTION_SUBSYSTEM
COMPARTMENTS

idx_COMPARTMENTS

N_EPI_COMPARTMENTS

i_EPI_COMPARTMENTS

N_MODEL_COMPARTMENTS

N_INFECTION_SUBSYSTEM

Format

An object of class character of length 10.

An object of class list of length 10.

An object of class integer of length 1.

An object of class integer of length 8.

An object of class integer of length 1.

Details

DAEDALUS has 8 epidemiological compartments: susceptible, exposed, infectious and symptomatic ("infect_symp"), infectious and asymptomatic ("infect_asymp") , hospitalised leading to recovery ("hospitalised_recov"), hospitalised leading to death ("hospitalised_death"), recovered, and dead.

There are 2 additional compartments that track the number of new infections and new hospitalisations.

Value

COMPARTMENTS returns a character vector of the epidemiological compartment names. idx_COMPARTMENTS returns a list with the compartment index.

All other constants return integer values.

Calculate daily incidences and summarise epidemic measures

Description

Functions to quickly summarise timeseries data from daedalus() to provide daily values for infections, hospitalisations, deaths, and vaccinations, while allowing grouping by different strata.

Usage

get_incidence(
  data,
  measures = c("infections", "hospitalisations", "deaths"),
  groups = NULL
)

get_epidemic_summary(
  data,
  measures = c("infections", "hospitalisations", "deaths"),
  groups = NULL
)

get_new_vaccinations(data, groups = NULL)
get_incidence(
  data,
  measures = c("infections", "hospitalisations", "deaths"),
  groups = NULL
)

get_epidemic_summary(
  data,
  measures = c("infections", "hospitalisations", "deaths"),
  groups = NULL
)

get_new_vaccinations(data, groups = NULL)

Arguments

data

Either a ⁠<data.frame>⁠ from a call to get_data() on a ⁠<daedalus_output>⁠ object, or such an object directly.

measures

A character vector of one or more of the following, passed to get_incidence() and get_epidemic_summary(): "infections", "hospitalisations" or "deaths" for the measure to return. Defaults to returning all three in long format.

get_daily_vaccinations() does not accept a measures argument and only provides the number of daily vaccinations.

groups

An optional character vector of grouping variables that correspond to model strata. Defaults to NULL which gives incidence across the whole population. Allowed groups correspond to modelled strata: "age_group", "vaccine_group", and "econ_sector".

get_daily_vaccinations() only accepts "age_group" and "econ_sector".

Value

A ⁠<data.frame>⁠ in long format, with one entry per model timestep, measure, and group chosen.

get_incidence() returns a data frame with the number of daily new infections, new hospitalisations, and/or new deaths in each of the groups specified by groups.
get_epidemic_summary() returns a data frame with the total number of the value specified in measure for each of the groups specified by groups.
get_daily_vaccinations() returns a data frame with columns for the number of new daily vaccination in each combination of groups if provided. Columns for the groups are added when groups are specified.

Examples

data <- daedalus("Canada", "sars_cov_1")

# new infections
new_infections <- get_incidence(data, "infections")

# epidemic summary
get_epidemic_summary(
  data,
  groups = "age_group"
)

# get daily vaccinations
daily_vaccinations <- get_new_vaccinations(data)

data <- daedalus("Canada", "sars_cov_1")

# new infections
new_infections <- get_incidence(data, "infections")

# epidemic summary
get_epidemic_summary(
  data,
  groups = "age_group"
)

# get daily vaccinations
daily_vaccinations <- get_new_vaccinations(data)

Get parameters from DAEDALUS classes

Description

Generic and methods for S3 classes for safely getting class parameters.

Usage

## S3 method for class 'daedalus_country'
get_data(x, to_get, setting = c("default", "all"), ...)

## S3 method for class 'daedalus_infection'
get_data(x, to_get, ...)

## S3 method for class 'daedalus_npi'
get_data(x, to_get, ...)

## S3 method for class 'daedalus_output'
get_data(x, to_get = NULL, ...)

## S3 method for class 'daedalus_vaccination'
get_data(x, to_get, ...)

get_data(x, ...)
## S3 method for class 'daedalus_country'
get_data(x, to_get, setting = c("default", "all"), ...)

## S3 method for class 'daedalus_infection'
get_data(x, to_get, ...)

## S3 method for class 'daedalus_npi'
get_data(x, to_get, ...)

## S3 method for class 'daedalus_output'
get_data(x, to_get = NULL, ...)

## S3 method for class 'daedalus_vaccination'
get_data(x, to_get, ...)

get_data(x, ...)

Arguments

x

An S3 class object from the daedalus package of the ⁠<daedalus_country>⁠, ⁠<daedalus_infection>⁠, ⁠<daedalus_npi>⁠, ⁠<daedalus_output>⁠, and ⁠<daedalus_vaccination>⁠ class.

to_get

A string giving the name of the element of x to return. For ⁠<daedalus_output>⁠ the additional internal data for $R_\text{eff}$ can be accessed by passing "rt_data".

setting

Optional string for a contact setting. Defaults to the first provided contact matrix if multiple are present.

...

<dynamic-dots> Other arguments to class methods. Class methods do not currently support any other arguments.

Value

Returns a member of x, with the class preserved (e.g. numeric vector for a country "demography").

For ⁠<daedalus_output>⁠ objects, returns the model timeseries data when no element is specified.

Examples

# simple example of getting data
country_A <- daedalus_country("United Kingdom")
get_data(country_A, "demography")

get_data(country_A, "contact_matrix")

disease_x <- daedalus_infection("sars_cov_1", r0 = 1.9)
get_data(disease_x, "r0")

# get model data
output <- daedalus("Canada", "influenza_1918")
head(
  get_data(output)
)

# simple example of getting data
country_A <- daedalus_country("United Kingdom")
get_data(country_A, "demography")

get_data(country_A, "contact_matrix")

disease_x <- daedalus_infection("sars_cov_1", r0 = 1.9)
get_data(disease_x, "r0")

# get model data
output <- daedalus("Canada", "influenza_1918")
head(
  get_data(output)
)

Get pandemic fiscal costs from a model run

Description

A helper function that post-processes a ⁠<daedalus_output>⁠ object to calculate the costs to a national government (fiscal costs) of responding to a pandemic. Includes costs of economic support, vaccinations given, and NPIs administered or implemented.

Usage

get_fiscal_costs(
  x,
  support_level = 0.2,
  price_vax = 1,
  price_npi = 1,
  uptake_npi = 1,
  interest_rate = 4,
  tax_rate = 35,
  spending_rate = 45,
  starting_debt = 0,
  productivity_loss_infection = 1
)
get_fiscal_costs(
  x,
  support_level = 0.2,
  price_vax = 1,
  price_npi = 1,
  uptake_npi = 1,
  interest_rate = 4,
  tax_rate = 35,
  spending_rate = 45,
  starting_debt = 0,
  productivity_loss_infection = 1
)

Arguments

x

A ⁠<daedalus_output>⁠ object from a call to daedalus(), or a ⁠<data.frame>⁠ of an epidemic timeseries.

support_level

The proportion of pandemic-related economic losses that a government compensates, as a proportion.

price_vax

The per-dose price of vaccination.

price_npi

The per-day and per-person price of implementing any pandemic response. May include costs such as testing or masks.

uptake_npi

The extent to which NPIs are taken up by the population; essentially a number that modifies (reduces) the total cost of implementing an NPI.

interest_rate

The annual interest rate on government borrowing for pandemic response.

tax_rate

The annual mean tax rate on economic output; used to calculate government revenue.

spending_rate

The annual mean rate of public spending as a percentage of GDP.

starting_debt

The value of national starting debt. Currently assumed to be zero while country-specific data are collected.

productivity_loss_infection

A single number in the range $[0, 1]$ giving the loss in productivity associated with symptomatic infection. Currently defaults to 1.0 for compatibility with earlier function versions.

Details

Default argument values are intended to be representative. This function is intended to be called after daedalus() and parameters required to compute fiscal costs may need to be bundled along with model outputs. Note that all rates (interest rate, spending rate, and tax rate) are given as annual percentages. Only the interest rate is converted to an daily value from an annual one for use in compounding.

Public spending

Spending is calculated as:

$G_t = \sigma(\text{GVA}^* - \text{GVA}_t) + Cv_t + Cp_t$

where $Cv_t$ is the time-specific cost of vaccination, and is calculated as the cost of new vaccinations in each timestep: $P_v \times \delta V_t$ .

$Cp_t$ is the time-specific cost of implementing pandemic response, and is calculated as $\psi (\bar N - D_t) P_p$ , where $\bar N - D_t$ is the remaining number of individuals in the population, and $\psi$ is the proportion taking up any protection offered by the response.

Interest on spending and fiscal cost

We assume that the government borrows to spend on pandemic mitigation measures outlined above such that the total cost to the public is then

$\text{TCG}_t = G_t + (1 + R_t^T) \text{TCG}_{t - 1}$

where $R_t^T$ is the daily rate of interest to be paid on the borrowed amount.

The interest rate is modelled as being constant over time. Users pass the annual rate of interest as a percentage, and this is converted to a daily rate using the internal function annual_rate_daily() as $(1 + R_t^T)^{1 / 365} - 1$ .

Total public debt

The total public debt at the end of the pandemic $b_t$ is then the sum of :

total public spending on the pandemic $\text{TCG}_t$ ,
existing day-to-day public spending $\bar G$ which is assumed to be a fraction of daily GDP $\nu \text{GDP}$ ,
existing debt owed due to past daily spending (including on pandemic mitigation), and interest to be paid on the debt,
less the revenues collected from taxation, $\mu \text{GVA}_{t-1}$ , where $\mu$ is the mean rate of taxation.

The daily GVA is the pre-pandemic GVA scaled by the available labour supply during the pandemic, taking into account labour restrictions due to illness-related absences and deaths, and response-related restrictions.

GDP is calculated as the sum of sector-specific daily GVA, and existing debt is currently assumed to be zero and is not included in the equation.

$b_t = \bar G + \text{TCG}_{t-1} + (1 + R_{t-1}^T) b_{t-1} - \mu \text{GVA}_{t-1}$

Value

A two-element list giving:

A list of fiscal_costs with elements giving the fiscal costs $\text{TCG}_t$ and a breakdown of these costs, as well as interest;
A list of public_debt of the public debt $b_t$ , which is the net of baseline public spending, pandemic response costs, and pandemic-impacted revenue.

Examples

# get fiscal costs for UK with SARS-CoV-2
# and both closures and vaccinations
o <- daedalus(
  "GBR", "sars_cov_2_pre_alpha",
  "economic_closures", "high",
  time_end = 100
)
fc <- get_fiscal_costs(o)

# also works when no closures are applied
o <- daedalus(
  "CAN", "influenza_2009",
  time_end = 30
)

# Compare public debt added estimates with other estimates from Covid-19:
# https://www.ctf.ca/EN/EN/Newsletters/Perspectives/2020/3/200302.aspx
get_fiscal_costs(o)
# get fiscal costs for UK with SARS-CoV-2
# and both closures and vaccinations
o <- daedalus(
  "GBR", "sars_cov_2_pre_alpha",
  "economic_closures", "high",
  time_end = 100
)
fc <- get_fiscal_costs(o)

# also works when no closures are applied
o <- daedalus(
  "CAN", "influenza_2009",
  time_end = 30
)

# Compare public debt added estimates with other estimates from Covid-19:
# https://www.ctf.ca/EN/EN/Newsletters/Perspectives/2020/3/200302.aspx
get_fiscal_costs(o)

Get life-years lost by demographic group.

Description

Get life-years lost by demographic group.

Usage

get_life_years_lost(output, groups = c("none", "age_group"))
get_life_years_lost(output, groups = c("none", "age_group"))

Arguments

output

A ⁠<daedalus_output>⁠ object.

groups

Whether to get the life-years lost by age group. Selecting "none" gives the total life-years lost.

Value

A ⁠<data.frame>⁠ with the number of life-years lost, optionally per age-group.

DAEDALUS model constants

Description

Frequently used values for the DAEDALUS model related to the model population and structure. See also epi_constants for constants specific to the epidemiological model.

Usage

N_AGE_GROUPS

i_AGE_GROUPS

N_VACCINE_STRATA

N_VACCINE_DATA_GROUPS

AGE_GROUPS

i_WORKING_AGE

i_SCHOOL_AGE

N_ECON_SECTORS

i_ECON_SECTORS

i_EDUCATION_SECTOR

N_ECON_STRATA

i_NOT_WORKING

DIM_AGE_GROUPS

DIM_EPI_COMPARTMENTS

DIM_ECON_SECTORS

DIM_VACCINE_STRATA

i_UNVACCINATED_STRATUM

i_VACCINATED_STRATUM

i_NEW_VAX_STRATUM

VACCINE_GROUPS

N_OUTPUT_COLS

N_FLAGS

FLAG_NAMES

MIN_DATA_COL_NAMES
N_AGE_GROUPS

i_AGE_GROUPS

N_VACCINE_STRATA

N_VACCINE_DATA_GROUPS

AGE_GROUPS

i_WORKING_AGE

i_SCHOOL_AGE

N_ECON_SECTORS

i_ECON_SECTORS

i_EDUCATION_SECTOR

N_ECON_STRATA

i_NOT_WORKING

DIM_AGE_GROUPS

DIM_EPI_COMPARTMENTS

DIM_ECON_SECTORS

DIM_VACCINE_STRATA

i_UNVACCINATED_STRATUM

i_VACCINATED_STRATUM

i_NEW_VAX_STRATUM

VACCINE_GROUPS

N_OUTPUT_COLS

N_FLAGS

FLAG_NAMES

MIN_DATA_COL_NAMES

Format

An object of class integer of length 1.

An object of class integer of length 4.

An object of class integer of length 1.

An object of class character of length 4.

An object of class integer of length 1.

An object of class integer of length 45.

An object of class integer of length 1.

An object of class character of length 3.

An object of class integer of length 1.

An object of class character of length 9.

An object of class character of length 5.

Details

The DAEDALUS model requires the following values to be fixed.

Number of age groups: 4
Number of vaccination strata: 2
Age group bins: 0-4 years, 5-19 years, 20-65 years (working age), 65+ years
Index of the working-age age groups: 3; see AGE_GROUPS
Number of economic sectors: 45
Index of individuals not working as a layer in the 3D state tensor: 1
Number of economic strata: 46; note that this is always one more than the number of economic sectors, with the additional stratum for those not in work which includes all individuals not of working age, and a proportion of working age individuals.
Array dimension of age groups: 1
Array dimension of epidemiological compartments: 2
Array dimension of economic sectors: 3
Array dimension of vaccination strata: 4
Indices and numbers of key groups.
Number of state variables that are flags, switches, or indicators.

Value

Values for the model constants.

Set parameters in DAEDALUS classes

Description

Generic and methods for S3 classes for safely setting class parameters. Only parameters considered safe to change – mostly contact data in the ⁠<country>⁠, but all parameters in ⁠<infection>⁠ – can be changed in this way.

Usage

## S3 method for class 'daedalus_infection'
set_data(x, ...)

## S3 method for class 'daedalus_vaccination'
set_data(x, ...)

set_data(x, ...)
## S3 method for class 'daedalus_infection'
set_data(x, ...)

## S3 method for class 'daedalus_vaccination'
set_data(x, ...)

set_data(x, ...)

Arguments

x

An S3 class object from the daedalus package of the ⁠<daedalus_country>⁠ or ⁠<infection>⁠ class.

...

<dynamic-dots> Named optional arguments for parameters to be changed, with their new values. The only values allowed for ⁠<daedalus_country>⁠ objects are "contact_matrix", "contacts_workplace", and "contacts_consumer_worker".

Value

An S3 object of the same class as input x.

Examples

# simple example of setting all contacts to 1
country_A <- daedalus_country("United Kingdom")
country_A

disease_x <- daedalus_infection("sars_cov_1")
disease_x <- set_data(disease_x, r0 = 3.0)
disease_x
# simple example of setting all contacts to 1
country_A <- daedalus_country("United Kingdom")
country_A

disease_x <- daedalus_infection("sars_cov_1")
disease_x <- set_data(disease_x, r0 = 3.0)
disease_x

Model output measures and groups for summaries and derived time-series

Description

Model output measures and groups for summaries and derived time-series

Usage

SUMMARY_MEASURES

SUMMARY_GROUPS
SUMMARY_MEASURES

SUMMARY_GROUPS

Format

An object of class character of length 3.

Package 'daedalus'

Help Index

Represent behaviour-change mechanisms in DAEDALUS

Description

Usage

Arguments

Details

Deaths-based public concern model

Optimism-responsiveness-effectiveness model

Value

Represent countries and territories for DAEDALUS

Description

Usage

Arguments

Value

Examples

Create and work with ⁠<daedalus_output>⁠ objects

Description

Usage

Arguments

Value

Represent infection parameters for DAEDALUS

Description

Usage

Arguments

Details

Included epidemics

Infection parameters

Value

Examples

Represent non-pharmaceutical intervention strategies for DAEDALUS

Description

Usage

Arguments

Details

Value

Examples

Represent vaccine investment scenarios for DAEDALUS

Description

Usage

Arguments

Details

Examples

DAEDALUS model for health, social and economic costs of a pandemic

Description

Usage

Arguments

Details

Details: Initial state

Value

Examples

Get epidemic losses from a DAEDALUS model run

Description

Usage

Arguments

Details

Expectations for non-Daedalus model data

Output

Economic costs

Educational costs

Life-value lost

Life-years lost

Value

Examples

Run daedalus for multiple infection parameter sets

Description

Usage

Arguments

Value

Economic constants used in DAEDALUS

Description

Usage

Format

Details

Epidemiological compartments and indices

Description

Usage

Format

Details

Value

Create and work with `⁠<daedalus_output>⁠` objects