seasonal_sim: Seasonal agent-based model simulation

In jakedilliott/covidfireMASS: Agent-Based Model For COVID-19 Dynamics In A Wildfire Season

Description

Seasonal agent-based model simulation

Usage

seasonal_sim(
  inc_data,
  mod_data,
  inc_info,
  overhead_ids = NULL,
  incl_A = TRUE,
  De = 5,
  gamma = 8,
  eir = 0.005,
  R_init = 0,
  I_init = 0,
  vax_init = 0,
  vax_efficacy = 0.95,
  vax_df = NULL,
  BI = 0.15,
  module_multiplier = 4,
  leads = 2,
  p_overhead_leads = 0.5,
  pIQ = 0.5,
  pAQ = 0,
  raw = FALSE
)

Arguments

inc_data	Resource assignments for incident IDs
mod_data	Resource assignments for module IDs
inc_info	Additional information on each incident
overhead_ids	Which res id's should be considered seasonal overhead (for vaccination purposes)
incl_A	Include Asymptomatic, TRUE or FALSE
De	Incubation period
gamma	Recovery time in days
eir	Entry infection rate
R_init	Initial number of recovered agents
I_init	Initial number of infectious agents
vax_init	Initial number of vaccinated agents
vax_efficacy	Proportion of vaccinated agents that become immune
vax_df	Data frame with specified number of agents to vaccinate at each time step
BI	Beta for symptomatic agents
module_multiplier	Scales up the infectiousness of agents within crew and equipment modules
leads	Leads per module, can be a proportion (0.5) or whole number (4)
p_overhead_leads	Proportion of the overhead module that should be leaders
pIQ	Probability that Symptomatic agents do not quarantine
pAQ	Probability that Asymptomatic will be caught and quarantined
raw	If true save all agent data at each timestep, default is FALSE

jakedilliott/covidfireMASS documentation built on Dec. 20, 2021, 8:59 p.m.