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)
```

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

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