swash (version 1.3.2)

exponential_growth: Exponential Growth Model for Epidemic Data

Description

Estimation of exponential growth models from daily infections data

Usage

exponential_growth(
  y, 
  t, 
  GI = 4,
  verbose = FALSE
  )

Value

object of class expgrowth-class

Arguments

y

numeric vector with cumulative infections data over time

t

vector of class numeric or Date with time points or dates

GI

Generation interval for computing \(R_0\)

verbose

bool argument which indicates whether progress messages are displayed

Author

Thomas Wieland

Details

This function allows the estimation of an exponential growth model. The user must specify the dependent variable (daily infections) and the time variable (time counter or date values). The estimation is performed using a linearized model as an OLS estimator. The results are the exponential growth rate \(r\), basic reproduction number \(R_0\), and the doubling rate.

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")

See Also

growth_initial, logistic_growth

Examples

data(COVID19Cases_geoRegion)
# Get SWISS COVID19 cases at NUTS 3 level

COVID19Cases_ZH <-
  COVID19Cases_geoRegion[
  (COVID19Cases_geoRegion$geoRegion == "ZH")
  & (COVID19Cases_geoRegion$sumTotal > 0)
  ,]
# COVID cases for Zurich

expgrowth_BS <- exponential_growth (
  y = COVID19Cases_ZH$sumTotal[1:28], 
  t = COVID19Cases_ZH$datum[1:28] 
)
# Exponential growth model for the first 4 weeks

summary(expgrowth_BS)
# Summary of exponential growth model

expgrowth_BS@doubling
# Doubling rate