View source: R/covid19_models.R

sweep.SIR.models | R Documentation |

function to perform a sweep of models and generate values of R0

sweep.SIR.models( data = NULL, geo.loc = "Hubei", t0_range = 15:20, t1 = NULL, deltaT = NULL, tfinal = 90, fatality.rate = 0.02, tot.population = 1.4e+09 )

`data` |
time series dataset to consider |

`geo.loc` |
country/region to analyze |

`t0_range` |
range of initial date 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 |

# read TimeSeries data TS.data <- covid19.data("TS-confirmed") # select a location of interest, eg. France # France has many entries, just pick "la France" France.data <- TS.data[ (TS.data$Country.Region == "France") & (TS.data$Province.State == ""),] # sweep values of R0 based on range of dates to consider for the model ranges <- 15:20 deltaT <- 20 params_sweep <- sweep.SIR.models(data=France.data,geo.loc="France", t0_range=ranges, deltaT=deltaT) # obtain the R0 values from the parameters R0s <- unlist(params_sweep["R0",]) # nbr of infected cases FR.infs<- preProcessingData(France.data,"France") # average per range # define ranges lst.ranges <- lapply(ranges, function(x) x:(x+deltaT)) # compute averages avg.FR.infs <- lapply(lst.ranges, function(x) mean(FR.infs[x])) # plots plot(R0s, type='b') # plot vs average number of infected cases plot(avg.FR.infs, R0s, type='b')

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.