sweep.SIR.models {covid19.analytics}

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')