swash (version 1.2.2)

growth_initial: 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
  )

Value

list with two entries:

results:

Object of class "data.frame" Results of the exponential growth models (growth rate, basic reproduction number, doubling rate) for each region

exponential_growth_models:

Object of class "list" List with \(N\) entries for \(N\) exponential models

Arguments

object

object of class sbm

time_units

numeric value for the analysis time (time units from start)

GI

Generation interval for computing \(R_0\)

Author

Thomas Wieland

Details

The function estimates exponential growth models for regional infections based on a sbm 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.

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. 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. 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. 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. 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

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