growth_initial: Exponential Growth Models for Regional Infections

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

Exponential Growth Models for Regional Infections

Description

Estimates N exponential growth models for a given time period in N regions.

Usage

growth_initial(
  object, 
  time_units = 10,
  GI = 4,
  nls = TRUE,
  nls_start = list(a = 1, b = 0.1),
  add_constant = 1,
  verbose = FALSE
  )

Arguments

object	object of class infpan
time_units	numeric value for the analysis time (time units from start)
GI	Generation interval for computing R_0
nls	Nonlinear estimation? TRUE or FALSE
nls_start	A list with start values for the two parameters to be estimated
add_constant	Numeric constant to be added to y if zero values occur (only relevant for OLS estimation)
verbose	bool argument which indicates whether progress messages are displayed

Details

The method estimates exponential growth models for regional infections based on an infpan object. Such models are design for the analysis of the initial phase of an epidemic spread. The user must state how much time units (from start) are included. See exponential_growth for further details of the estimation.

Value

object of class growthmodels-class

Author(s)

Thomas Wieland

References

Bonifazi G et al. (2021) A simplified estimate of the effective reproduction number Rt using its relation with the doubling time and application to Italian COVID-19 data. The European Physical Journal Plus 136, 386. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1140/epjp/s13360-021-01339-6")}

Chowell G, Viboud C, Hyman JM, Simonsen L (2015) The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates. PLOS Currents Outbreaks, ecurrents.outbreaks.8b55f4bad99ac5c5db3663e916803261. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1371/currents.outbreaks.8b55f4bad99ac5c5db3663e916803261")}

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) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Science 131, 104924. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.ssci.2020.104924")}

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_initialgrowth_3weeks <- 
  growth_initial(
    infpan_CH,
    time_units = 21
  )
summary(CH_covidwave1_initialgrowth_3weeks)
# Exponential models for infpan object CH_covidwave1 
# initial growth in the first 3 weeks

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