growth: Logistic Growth Models for Regional Infections

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

Logistic Growth Models for Regional Infections

Description

Estimates N logistic growth models for N regions.

Usage

growth(
  object, 
  S_iterations = 10, 
  S_start_est_method = "bisect", 
  seq_by = 10, 
  nls = TRUE,
  add_constant = 1,
  overwrite = FALSE,
  verbose = FALSE
  )

Arguments

object	object of class infpan
S_iterations	Number of iterations for saturation value search
S_start_est_method	Method for saturation value search, either "bisect" or "trial_and_error"
seq_by	No of segments for the "trial_and_error" estimation of the saturation value
nls	Nonlinear estimation? TRUE or FALSE
add_constant	Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)
overwrite	bool argument which indicates whether the column containing cumulative cases should be overwritten if already existing
verbose	bool argument which indicates whether progress messages are displayed

Details

The function estimates logistic growth models for regional infections based on a infpan object. See logistic_growth for further details.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Chowell G, Simonsen L, Viboud C, Yang K (2014) Is West Africa Approaching a Catastrophic Phase or is the 2014 Ebola Epidemic Slowing Down? Different Models Yield Different Answers for Liberia. PLoS currents 6. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://dx.doi.org/10.1371/currents.outbreaks.b4690859d91684da963dc40e00f3da81")}

Pell B, Kuang Y, Viboud C, Chowell G (2018) Using phenomenological models for forecasting the 2015 ebola challenge. Epidemics 22, 62–70. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.epidem.2016.11.002")}

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

logistic_growth, exponential_growth

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)

CH_covidwave1_growth <- 
  growth(infpan_CH)
summary(CH_covidwave1_growth)
# Logistic growth models for infpan object infpan_CH

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