View source: R/covid19_models.R

generate.SIR.model | R Documentation |

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

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 )

`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 |

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)

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