swash (version 1.1.0)

is_balanced: Test whether Panel Dataset with Regional Infection Data is Balanced

Description

The function tests whether the input panel data with regional infections is balanced.

Usage

is_balanced(
  data, 
  col_cases, 
  col_date, 
  col_region, 
  as_balanced = TRUE, 
  fill_missing = 0
  )

Value

List with two entries:

data_balanced

Result of test (TRUE or FALSE)

data

Input dataset (data.frame)

Arguments

data

data.frame with regional infection data

col_cases

Column containing the cases (numeric)

col_date

Column containing the time points (e.g., days)

col_region

Column containing the unique identifier of the regions (e.g., name, NUTS 3 code)

as_balanced

Boolean argument which indicates whether non-balanced panel data shall be balanced (default: TRUE)

fill_missing

Constant to fill missing values (default and recommended: 0)

Author

Thomas Wieland

Details

The Swash-Backwash Model for the Single Epidemic Wave does not necessarily require balanced panel data in order for the calculations to be carried out. However, for a correct estimation it is implicitly assumed that the input data is balanced. The function tests whether the panel data is balanced. It is executed automatically whithin the swash() function (using automatic correction with as_balanced = TRUE), but can also be used separately.

References

Swash-Backwash Model:

Cliff AD, Haggett P (2006) A swash-backwash model of the single epidemic wave. Journal of Geographical Systems 8(3), 227-252. tools:::Rd_expr_doi("https://doi.org/10.1007/s10109-006-0027-8")

Smallman-Raynor MR, Cliff AD, Stickler PJ (2022) Meningococcal Meningitis and Coal Mining in Provincial England: Geographical Perspectives on a Major Epidemic, 1929–33. Geographical Analysis 54, 197–216. tools:::Rd_expr_doi("https://doi.org/10.1111/gean.12272")

Smallman-Raynor MR, Cliff AD, The COVID-19 Genomics UK (COG-UK) Consortium (2022) Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020–December 2021. Epidemiology and Infection 150, e145. tools:::Rd_expr_doi("https://doi.org/10.1017/S0950268822001285").

Panel data:

Greene, WH (2012) Econometric Analysis. Ch. 11.

Wooldridge, JM (2012) Introductory Econometrics. A Modern Approach. Ch. 13.

See Also

as_balanced

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[!COVID19Cases_geoRegion$geoRegion %in% c("CH", "CHFL"),]
# Exclude CH = Switzerland total and CHFL = Switzerland and Liechtenstein total

COVID19Cases_geoRegion <- 
  COVID19Cases_geoRegion[COVID19Cases_geoRegion$datum <= "2020-05-31",]
# Extract first COVID-19 wave

COVID19Cases_geoRegion_balanced <- 
  is_balanced(
  data = COVID19Cases_geoRegion,
  col_cases = "entries",
  col_date = "datum",
  col_region = "geoRegion"
)
# Test whether "COVID19Cases_geoRegion" is balanced panel data 

COVID19Cases_geoRegion_balanced$data_balanced
# Balanced? TRUE or FALSE

if (COVID19Cases_geoRegion_balanced$data_balanced == FALSE) {
  COVID19Cases_geoRegion <- 
    as_balanced(
    COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion"
  )
}
# Correction of dataset "COVID19Cases_geoRegion"
# not necessary as parameter balance of is_balanced is set TRUE by default