swash (version 1.2.2)

growth: 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
  )

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

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

Author

Thomas Wieland

Details

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

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. 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. 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. 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