sweep.SIR.models: function to perform a sweep of models and generate values of...

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

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

Description

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

Usage

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
)

Arguments

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

Examples

# 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')

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