growth: Logistic Growth Models for Regional Infections

In swash: Swash-Backwash Model for the Single Epidemic Wave

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,
  verbose = FALSE
  )

Arguments

object	object of class sbm
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
verbose	bool argument which indicates whether progress messages are displayed

Details

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

Value

list with two entries:

results:	Object of class "data.frame" Results of the logistic growth models (coefficients and derivates)
logistic_growth_models:	Object of class "list" List with N entries for N growth models resp. loggrowth objects

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

CH_covidwave1 <- 
  swash (
    data = COVID19Cases_geoRegion, 
    col_cases = "entries", 
    col_date = "datum", 
    col_region = "geoRegion"
    )
# Swash-Backwash Model for Swiss COVID19 cases
# Spatial aggregate: NUTS 3 (cantons)

CH_covidwave1_growth <- growth(CH_covidwave1)
CH_covidwave1_growth
# Logistic growth models for sbm object CH_covidwave1

swash documentation built on Feb. 15, 2026, 5:07 p.m.