exponential_growth: Exponential Growth Model for Epidemic Data

In swash: Swash-Backwash Model for the Single Epidemic Wave

Exponential Growth Model for Epidemic Data

Description

Estimation of exponential growth models from daily infections data

Usage

exponential_growth(
  y, 
  t, 
  GI = 4
  )

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

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.

Value

list with four entries:

exp_gr:	numeric value of exponential growth rate
R0:	numeric value of basic reproduction number
doubling:	numeric value of doubling rate
model_data:	lm object of regression model

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")}

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

expgrowth_BS$doubling
# Doubling rate

swash documentation built on Aug. 8, 2025, 7:14 p.m.