generate.SIR.model: function to generate a simple SIR...

In covid19.analytics: Load and Analyze Live Data from the COVID-19 Pandemic

function to generate a simple SIR (Susceptible-Infected-Recovered) model based on the actual data of the coivd19 cases

Description

function to generate a simple SIR (Susceptible-Infected-Recovered) model based on the actual data of the coivd19 cases

Usage

generate.SIR.model(
  data = NULL,
  geo.loc = "Hubei",
  t0 = NULL,
  t1 = NULL,
  deltaT = NULL,
  tfinal = 90,
  fatality.rate = 0.02,
  tot.population = 1.4e+09,
  staticPlt = TRUE,
  interactiveFig = FALSE,
  add.extras = FALSE
)

Arguments

data	time series dataset to consider
geo.loc	country/region to analyze
t0	initial period of time for data consideration
t1	final period of time for data consideration
deltaT	interval period of time from t0, ie. number of days to consider since t0
tfinal	total number of days
fatality.rate	rate of causality, deafault value of 2 percent
tot.population	total population of the country/region
staticPlt	optional flag to activate/deactive plotting of the data and the SIR model generated
interactiveFig	optional flag to activate/deactive the generation of an interactive plot of the data and the SIR model generated
add.extras	boolean flag to add extra indicators, such as, the "force of infection" and time derivatives

Examples

data <- covid19.data("ts-confirmed")
generate.SIR.model(data,"Hubei", t0=1,t1=15)
generate.SIR.model(data,"Germany",tot.population=83149300)
generate.SIR.model(data,"Uruguay", tot.population=3500000)
generate.SIR.model(data,"Canada", tot.population=37590000, add.extras=TRUE)

covid19.analytics documentation built on Oct. 16, 2023, 1:06 a.m.