swash (version 1.3.2)

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,
  verbose = FALSE
  )

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

verbose

bool argument which indicates whether progress messages are displayed

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