Virus design — virus • epiworldR

Viruses can be considered to be anything that can be transmitted (e.g., diseases, as well as ideas.) Most models in epiworldR can feature multiple viruses.

Usage

virus(
  name,
  prevalence,
  as_proportion,
  prob_infecting,
  recovery_rate = 0.5,
  prob_death = 0,
  post_immunity = -1,
  incubation = 7
)

set_name_virus(virus, name)

get_name_virus(virus)

add_virus(model, virus, proportion)

virus_set_state(virus, init, end, removed)

rm_virus(model, virus_pos)

virus_fun_logit(vars, coefs, model)

set_prob_infecting(virus, prob)

set_prob_infecting_ptr(virus, model, param)

set_prob_infecting_fun(virus, model, vfun)

set_prob_recovery(virus, prob)

set_prob_recovery_ptr(virus, model, param)

set_prob_recovery_fun(virus, model, vfun)

set_prob_death(virus, prob)

set_prob_death_ptr(virus, model, param)

set_prob_death_fun(virus, model, vfun)

set_incubation(virus, incubation)

set_incubation_ptr(virus, model, param)

set_incubation_fun(virus, model, vfun)

set_distribution_virus(virus, distfun)

distribute_virus_randomly(prevalence, as_proportion)

distribute_virus_set(agents_ids)

Arguments

name: of the virus
prevalence: Numeric scalar. Prevalence of the virus.
as_proportion: Logical scalar. If TRUE, the prevalence is set as a proportion of the total number of agents in the model.
prob_infecting: Numeric scalar. Probability of infection (transmission).
recovery_rate: Numeric scalar. Probability of recovery.
prob_death: Numeric scalar. Probability of death.
post_immunity: Numeric scalar. Post immunity (prob of re-infection).
incubation: Numeric scalar. Incubation period (in days) of the virus.
virus: An object of class epiworld_virus
model: An object of class epiworld_model.
proportion: Deprecated.
init, end, removed: states after acquiring a virus, removing a virus, and removing the agent as a result of the virus, respectively.
virus_pos: Positive integer. Index of the virus's position in the model.
vars: Integer vector. Indices (starting from 0) of the positions of the variables used to compute the logit probability.
coefs: Numeric vector. Of the same length of vars, is a vector of coefficients associated to the logit probability.
prob: Numeric scalar. A probability (between zero and one).
param: Character scalar. Name of the parameter featured in model that will be added to the virus (see details).
vfun: An object of class epiworld_virus_fun.
distfun: An object of class epiworld_distribution_virus.
agents_ids: Integer vector. Indices of the agents that will receive the virus.

Value

The set_name_virus function does not return a value, but merely assigns a name to the virus of choice.

The get_name_virus function returns the name of the virus of class epiworld_virus.

The add_virus function does not return a value, instead it adds the virus of choice to the model object of class epiworld_model.

The virus_set_state function does not return a value but assigns epidemiological properties to the specified virus of class epiworld_virus.

The rm_virus function does not return a value, but instead removes a specified virus from the model of class epiworld_model.

The set_prob_infecting function does not return a value, but instead assigns a probability to infection for the specified virus of class epiworld_virus.

The set_prob_recovery function does not return a value, but instead assigns a probability to recovery for the specified virus of class epiworld_virus.

The set_prob_death function does not return a value, but instead assigns a probability to death for the specified virus of class epiworld_virus.

The set_incubation function does not return a value, but instead assigns an incubation period to the specified virus of class epiworld_virus.

The distribute_virus_randomly function returns a function that can be used to distribute the virus in the model.

Details

The virus() function can be used to initialize a virus. Virus features can then be modified using the functions set_prob_*.

The function virus_fun_logit() creates a "virus function" that can be evaluated for transmission, recovery, and death. As the name sugests, it computes those probabilities using a logit function (see examples).

The name of the epiworld_virus object can be manipulated with the functions set_name_virus() and get_name_virus().

In the case of set_prob_infecting_ptr, set_prob_recovery_ptr, and set_prob_death_ptr, the corresponding parameters is passed as a pointer to the virus. The implication of using pointers is that the values will be read directly from the model object, so changes will be reflected.

The distribute_virus_randomly function is a factory function used to randomly distribute the virus in the model. The prevalence can be set as a proportion or as a number of agents. The resulting function can then be passed to set_distribution_virus.

Examples

mseirconn <- ModelSEIRCONN(
  name                = "COVID-19",
  prevalence          = 0.01,
  n                   = 10000,
  contact_rate        = 4,
  incubation_days     = 7,
  transmission_rate   = 0.5,
  recovery_rate       = 0.99
)

delta <- virus(
  "Delta Variant", 0, .5, .2, .01, prevalence = 0.3, as_proportion = TRUE
)

# Adding virus and setting/getting virus name
add_virus(mseirconn, delta)
set_name_virus(delta, "COVID-19 Strain")
get_name_virus(delta)
#> [1] "COVID-19 Strain"

run(mseirconn, ndays = 100, seed = 992)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.
mseirconn
#> ________________________________________________________________________________
#> Susceptible-Exposed-Infected-Removed (SEIR) (connected)
#> It features 10000 agents, 2 virus(es), and 0 tool(s).
#> The model has 4 states.
#> The final distribution is: 3609 Susceptible, 113 Exposed, 11 Infected, and 6267 Recovered.

rm_virus(mseirconn, 0) # Removing the first virus from the model object
set_distribution_virus(delta, distribute_virus_randomly(100, as_proportion = FALSE))
add_virus(mseirconn, delta)

# Setting parameters for the delta virus manually
set_prob_infecting(delta, 0.5)
set_prob_recovery(delta, 0.9)
set_prob_death(delta, 0.01)
run(mseirconn, ndays = 100, seed = 992) # Run the model to observe changes
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.

# If the states were (for example):
# 1: Infected
# 2: Recovered
# 3: Dead
delta2 <- virus(
  "Delta Variant 2", 0, .5, .2, .01, prevalence = 0, as_proportion = TRUE
)
virus_set_state(delta2, 1, 2, 3)
# Using the logit function --------------
sir <- ModelSIR(
  name = "COVID-19", prevalence = 0.01,
  transmission_rate = 0.9, recovery = 0.1
)

# Adding a small world population
agents_smallworld(
  sir,
  n = 10000,
  k = 5,
  d = FALSE,
  p = .01
)

run(sir, ndays = 50, seed = 11)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> | done.
plot(sir)


# And adding features
dat <- cbind(
  female = sample.int(2, 10000, replace = TRUE) - 1,
  x      = rnorm(10000)
)

set_agents_data(sir, dat)

# Creating the logit function
vfun <- virus_fun_logit(
  vars  = c(0L, 1L),
  coefs = c(-1, 1),
  model = sir
)

# The infection prob is lower
hist(plogis(dat %*% rbind(-1, 1)))


vfun # printing
#> An epiworld_virus_function object.
#> (model: Susceptible-Infected-Recovered (SIR))
#> This function was built using -virus_fun_logit()-. and it features the following coefficients:
#>   0: -1.00
#>   1:  1.00 

set_prob_infecting_fun(
  virus = get_virus(sir, 0),
  model = sir,
  vfun  = vfun
)

run(sir, ndays = 50, seed = 11)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> | done.
plot(sir)