SURV model

## Arguments

- name
String. Name of the virus.

- prevalence
Initial number of individuals with the virus.

- efficacy_vax
Double. Efficacy of the vaccine. (1 - P(acquire the disease)).

- latent_period
Double. Shape parameter of a 'Gamma(latent_period, 1)' distribution. This coincides with the expected number of latent days.

- infect_period
Double. Shape parameter of a 'Gamma(infected_period, 1)' distribution. This coincides with the expected number of infectious days.

- prob_symptoms
Double. Probability of generating symptoms.

- prop_vaccinated
Double. Probability of vaccination. Coincides with the initial prevalence of vaccinated individuals.

- prop_vax_redux_transm
Double. Factor by which the vaccine reduces transmissibility.

- prop_vax_redux_infect
Double. Factor by which the vaccine reduces the chances of becoming infected.

- surveillance_prob
Double. Probability of testing an agent.

- transmission_rate
Double. Raw transmission probability.

- prob_death
Double. Raw probability of death for symptomatic individuals.

- prob_noreinfect
Double. Probability of no re-infection.

- x
Object of class SURV.

- main
Title of the plot.

- ...
Currently ignore.

## Value

The

`ModelSURV`

function returns a model of class epiworld_model.

The `plot`

function returns a plot of the SURV model of class
epiworld_model.

## See also

epiworld-methods

Other Models:
`ModelDiffNet()`

,
`ModelSEIR()`

,
`ModelSEIRCONN()`

,
`ModelSEIRD()`

,
`ModelSEIRDCONN()`

,
`ModelSEIRMixing()`

,
`ModelSIR()`

,
`ModelSIRCONN()`

,
`ModelSIRD()`

,
`ModelSIRDCONN()`

,
`ModelSIRLogit()`

,
`ModelSIRMixing()`

,
`ModelSIS()`

,
`ModelSISD()`

,
`epiworld-data`

## Examples

```
model_surv <- ModelSURV(
name = "COVID-19",
prevalence = 20,
efficacy_vax = 0.6,
latent_period = 4,
infect_period = 5,
prob_symptoms = 0.5,
prop_vaccinated = 0.7,
prop_vax_redux_transm = 0.8,
prop_vax_redux_infect = 0.95,
surveillance_prob = 0.1,
transmission_rate = 0.2,
prob_death = 0.001,
prob_noreinfect = 0.5
)
# Adding a small world population
agents_smallworld(
model_surv,
n = 10000,
k = 5,
d = FALSE,
p = .01
)
# Running and printing
run(model_surv, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> done.
model_surv
#> ________________________________________________________________________________
#> Surveillance
#> It features 10000 agents, 1 virus(es), and 1 tool(s).
#> The model has 8 states.
#> The final distribution is: 9974 Susceptible, 0 Latent, 0 Symptomatic, 0 Symptomatic isolated, 0 Asymptomatic, 0 Asymptomatic isolated, 26 Recovered, and 0 Removed.
# Plotting
plot(model_surv, main = "SURV Model")
```