calculate_incidence: Incidence from Infection Numbers

In swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data

Incidence from Infection Numbers

Description

Calculation of the incidence from infection numbers and population for infections panel data.

Usage

calculate_incidence(
  object,
  use_column = NULL,
  col_name = NULL,
  pop_factor = 100000,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object	object of class infpan
use_column	character value specifying which column should be used for incidence calculation
col_name	character value specifying the column name of the computed incidence
pop_factor	numeric value specifying the factor with which the incidence should be multiplied (e.g., cases/pop*100000)
overwrite	bool argument which indicates whether the column should be overwritten if already existing
verbose	bool argument which indicates whether progress messages are displayed

Details

Calculates the incidence of the infections panel data for all time points for each region. Use use_column to specify which column should be used for the calculation of incidence. The following values are permitted: "Cases" (default, incremental cases), "Cum. cases" (cumulative cases), "Roll. mean" (rolling mean of cases), or "Roll. sum" (rolling sum of cases). If the specified column does not exist in the infections panel data of the infpan object, the function raises an error. If in the infpan object, no "Population" column is defined, incidence calculation is not possible. If col_name is NULL, the column is defined as "<Column name of cases>_inc". Set overwrite to TRUE, if an existing column should be overwritten.

Value

infpan object including column with incidence values in the infections panel data

Author(s)

Thomas Wieland

References

an der Heiden M, Hamouda O (2020) Schätzung der aktuellen Entwicklung der SARS-CoV-2-Epidemie in Deutschland - Nowcasting. Epidemiologisches Bulletin 17, 10-15. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.25646/6692")}

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18335/region.v7i2.324")}

See Also

infpan-class, load_infections_paneldata, calculate_Rt, calculate_cum, calculate_rollmean

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

infpan_CH <- load_infections_paneldata(
    data = COVID19Cases_geoRegion,
    col_cases = "entries",
    col_date = "datum",
    col_region = "geoRegion",
    other_cols = c("Population" = "pop"), 
    verbose = TRUE
  )
# Import as infections panel data set (class infpan)

infpan_CH <- calculate_incidence(
  infpan_CH, 
  col_name = "incidence",
  verbose = TRUE
)
# Calculate incidence of cases as "incidence"

summary(infpan_CH)
# Summary of infpan object

swash documentation built on April 7, 2026, 1:06 a.m.