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')
```

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